JP6909109B2 - Information processing equipment, information processing methods, and programs - Google Patents

Description

The present invention relates to an information processing apparatus, information processing method, and a program.

An apparatus using an optical coherence tomography (OCT) that can acquire a tomographic image to be measured non-invasively (hereinafter referred to as an OCT apparatus) is widely used particularly in the field of ophthalmology.

In recent years, an angiography method using OCT (OCT Angiography: OCTA) has been proposed as an angiography method that does not use a contrast medium. OCTA generates a blood vessel image (hereinafter referred to as OCTA image) by projecting three-dimensional motion contrast data acquired by OCT onto a two-dimensional plane. Here, the motion contrast data is data obtained by repeatedly photographing the same cross section of the measurement target by OCT and detecting a temporal change of the measurement target during the shooting. The motion contrast data can be obtained, for example, by calculating the temporal change of the phase, vector, and intensity of the complex OCT signal from the difference, the ratio, the correlation, and the like.

In the motion contrast data, the portion of the OCT signal where noise is generated is also represented as a temporal change of the measurement target. In this regard, a good OCTA image can be obtained by setting a threshold value and removing noise.

Patent Document 1 discloses another method for denoising an OCTA image. In the technique disclosed in Patent Document 1, when the motion contrast data is generated, the phase difference information of the OCT signal and the vector difference information are multiplied to obtain the calculation result of one information and the calculation result of the other information. Weighting is done based on. It is said that this makes it possible to generate an OCTA image with less noise.

By the way, in the imaging by OCT, the signal strength of the acquired interference signal changes according to the change in the imaging condition such as the movement of the measurement target in the depth direction. Therefore, depending on the shooting conditions, the strength of the pixel value may occur in the tomographic image generated based on the interference signal.

On the other hand, in Patent Document 2, a table for correcting a change in signal strength due to a difference in sensitivity in the depth direction of OCT is prepared, and a table prepared for correcting a positional deviation in the depth direction of OCT is used. The technique for correcting the signal strength is disclosed.

However, the strength of the pixel value in the tomographic image, that is, the brightness of the image, is not only caused by the movement of the measurement target in the depth direction, but is also affected by changes in other shooting conditions.

When the tomographic image generated by OCT becomes bright and dark according to the strength of the pixel value, the motion contrast data based on the tomographic image is also affected. Therefore, even in the OCTA image which is a motion contrast image based on the motion contrast data, partial brightness and darkness occur.

JP 2015-131107 Japanese Unexamined Patent Publication No. 2011-135933

When partial light and darkness occurs in the OCTA image, blood vessels in the dark part may not be clearly visible. In addition, if the depiction of blood vessels is uneven, the measured blood vessel density will also be uneven when the blood vessel density is measured using OCTA. Therefore, in such a case, it is not possible to accurately measure the blood vessel density using the OCTA image.

Therefore, the present invention provides an information processing device, an information processing method, and a program capable of generating a motion contrast image in which the occurrence of light and darkness due to the strength of tomographic data is suppressed.

The information processing apparatus according to one embodiment of the present invention acquires a plurality of tomographic data indicating information on the tomography of the tongue obtained based on measurement light controlled to scan the same position of the tongue. The means, the first schematic parameter obtained by converting the parameter used for calculating the motion contrast in the tomographic data in the first dimension, and the second schematic parameter in which the parameter in the tomographic data is lower than the first dimension. the operation of the second schematic parameter converted in dimension, a second obtaining means for obtaining a correction coefficient, and correction means for correcting at least one of the tomographic data of the plurality of the tomographic data by using the correction coefficient provided on the basis of the auxiliary Tadashisa the said at least one motion contrast calculated using the plurality of tomographic data including fault data, and generating means for generating a motion contrast image.

The information processing apparatus according to another embodiment of the present invention acquires a plurality of tomographic data indicating information on the fault of the fundus obtained based on measurement light controlled to scan the same position of the fundus. The acquisition means, the first schematic parameter obtained by converting the parameter used for calculating the motion contrast in the tomographic data in the first dimension, and the parameter in the tomographic data to be lower than the first dimension. By calculating with the second approximate parameter converted in the dimension of, the second acquisition means for acquiring the correction coefficient and the correction coefficient are used to correct the threshold of the threshold processing applied to the parameter of the tomographic data. A generation means for generating a motion contrast image based on the correction means and the motion contrast calculated using the parameters of at least one of the tomographic data among the plurality of the tomographic data, which has been threshold-processed using the corrected threshold. And.

The information processing apparatus according to another embodiment of the present invention includes: a first acquiring unit that acquire the tomographic data indicative of the fault information of the fundus obtained on the basis of the controlled measuring light to scan the eye bottom , first and summary parameters, second schematic of converting the parameters in the tomographic data in the second dimension lower than the first dimension of Rupa parameters put on the tomographic data converted in the first dimension the calculation of the parameters comprises a second obtaining means for obtaining a correction coefficient, and correction means for correcting the previous SL tomographic data, and generating means for generating the images using pre Symbol corrected tomographic data.

The information processing apparatus according to still another embodiment of the present invention acquires a plurality of tomographic data indicating the information of the tomography of the tongue obtained based on the measurement light controlled to scan the same position of the tongue. One acquisition means, a second acquisition means for acquiring a target of the distribution of parameters in the tomographic data used for calculating motion contrast, and the distribution of the parameters in at least one of the plurality of the tomographic data. A motion contrast image is generated based on a correction means that corrects It is provided with a generation means to be generated.

Other implementation information processing method according to the embodiment of the present invention are that acquires a plurality of tomographic data indicative of the fault information of the fundus obtained on the basis of the measurement light which is controlled to scan the same position of the fundus , The first schematic parameter obtained by converting the parameter used for calculating the motion contrast in the fault data in the first dimension, and the parameter in the fault data in the second dimension lower than the first dimension. the calculation of the conversion a second schematic parameters, and obtaining a correction factor, and correcting at least one of the tomographic data of the plurality of the tomographic data by using the correction coefficient, the corrected at least This includes generating a motion contrast image based on the motion contrast calculated using the plurality of tomographic data including one tomographic data.

Other implementation information processing method according to the embodiment of the present invention are that acquires a plurality of tomographic data indicative of the fault information of the fundus obtained on the basis of the measurement light which is controlled to scan the same position of the fundus , The first schematic parameter obtained by converting the parameter used for calculating the motion contrast in the fault data in the first dimension, and the parameter in the fault data in the second dimension lower than the first dimension. The correction coefficient was obtained by calculation with the converted second approximate parameter, and the correction coefficient was used to correct the threshold of the threshold processing applied to the parameter of the tomographic data. This includes generating a motion contrast image based on the motion contrast calculated using the parameters of at least one tomographic data among the plurality of the tomographic data, which has been threshold-processed using the threshold value.

The information processing method according to still another embodiment of the present invention are that acquire tomographic data indicative of the fault information of the fundus obtained on the basis of the controlled measuring light to scan the eye bottom, the By calculation of the first schematic parameter obtained by converting the parameter in the tomographic data in the first dimension and the second approximate parameter obtained by converting the parameter in the fault data in the second dimension lower than the first dimension. , Acquiring the correction coefficient, correcting the tomographic data, and generating an image using the corrected tomographic data .

According to the present invention, it is possible to generate a motion contrast image in which the occurrence of light and darkness due to the strength of the tomographic data is suppressed.

The overall configuration of the OCT apparatus according to the first embodiment is schematically shown. The configuration of the photographing optical system and the base according to the first embodiment is schematically shown. The configuration of the control unit according to the first embodiment is schematically shown. The structure of three-dimensional data is shown schematically. An example of the user interface according to the first embodiment is shown. The flow of OCTA image generation processing which concerns on Example 1 is shown. The graph of the data distribution which concerns on Example 1 is shown. The graph of the data distribution which concerns on Example 2 is shown. The flow of OCTA image generation processing which concerns on Example 3 is shown. It is a figure for demonstrating the correction of the data distribution which concerns on the modification of Example 3. FIG. It is a graph which shows the data distribution before and after the application of the process for compensating the signal attenuation in the depth direction by the roll-off characteristic which concerns on Example 4 and Example 5. The flow of OCTA image generation processing which concerns on Example 4 is shown. It is a figure explaining the OCTA image generation process which concerns on Example 4. FIG. The luminance profile corresponding to the white dotted line portion of the image shown in FIG. 13 is shown. The flow of OCTA image generation processing which concerns on Example 5 is shown.

Hereinafter, exemplary examples for carrying out the present invention will be described in detail with reference to the drawings. However, the dimensions, materials, shapes, relative positions of the components, etc. described in the following examples are arbitrary and can be changed according to the configuration of the device to which the present invention is applied or various conditions. Also, in the drawings, the same reference numerals are used between the drawings to indicate elements that are the same or functionally similar. In addition, in this specification, "the same position" includes the completely same position and substantially the same position. Here, the substantially identical positions refer to positions that are substantially the same to generate a motion contrast image (OCTA image). The "fault data" is signal data including information on the fault of the inspected object, data based on the interference signal by OCT, and data obtained by performing fast Fourier transform (FFT) or arbitrary signal processing. Refers to those containing.

(Example 1)
The OCT apparatus according to the first embodiment will be described with reference to FIGS. 1 to 7. The OCT apparatus according to the present embodiment can suppress the occurrence of light and darkness due to the strength of the tomographic data and can satisfactorily generate the OCTA image. In the following, the eye to be inspected will be described as an example of the object to be inspected.

(Main unit configuration)
FIG. 1 shows a schematic overall configuration of the OCT apparatus 1 according to this embodiment. The OCT device 1 is provided with a photographing optical system 200, a base 250, a control unit 300, an input unit 360, and a display unit 500.

The photographing optical system (photographing apparatus) 200 acquires data on an anterior segment image of the eye to be inspected, an SLO (Scanning Laser Ophthalmoscope) fundus image, and a tomographic image. The photographing optical system 200 is provided on the base 250 and is held by an electric stage or the like (not shown) so as to be movable in the XYZ direction with respect to the base 250.

The control unit (information processing device) 300 is connected to the photographing optical system 200, the input unit 360, and the display unit 500. The control unit 300 controls the imaging of the imaging optical system 200, analyzes and reconstructs the acquired data on the anterior segment image, the SLO fundus image, and the tomographic image, and displays the anterior segment image, the SLO image, the tomographic image, the OCTA image, and the like. Generate. The control unit 300 can be configured by using a general-purpose computer, but may be configured as a dedicated computer of the OCT device 1. The input unit 360 is an input device for giving an instruction to the control unit 300, and is composed of, for example, a keyboard, a mouse, and the like.

The display unit 500 displays various information and various images sent from the control unit 300, a mouse cursor according to the operation of the input unit 360, and the like. The display unit 500 can be configured by using any monitor. In this embodiment, the photographing optical system 200, the control unit 300, the input unit 360, and the display unit 500 are individually configured, but these may be partially or wholly configured as one.

(Composition of shooting optical system and base)
Next, the configurations of the photographing optical system 200 and the base 250 will be described with reference to FIG. FIG. 2 shows a schematic configuration of the photographing optical system 200 and the base 250.

First, the inside of the photographing optical system 200 will be described. The objective lens 201 is installed facing the eye E to be inspected. A first dichroic mirror 202 and a second dichroic mirror 203 are provided on the optical axis of the objective lens 201. The optical path from the objective lens 201 is led to the optical path L1 of the OCT optical system, the optical path L2 for the SLO optical system and the fixation lamp, and the optical path L3 for front eye observation by the first and second dichroic mirrors 202 and 203. It is branched for each wavelength band of light passing through. In this embodiment, the optical path L1 of the OCT optical system and the optical path L2 for the SLO optical system and the fixation lamp are arranged in the transmission direction of the first dichroic mirror 202, and the optical path L3 for observing the anterior segment of the eye is arranged in the reflection direction. NS. Further, the SLO optical system and the optical path L2 for the fixation lamp are arranged in the transmission direction of the second dichroic mirror 203, and the optical path L1 of the OCT optical system is arranged in the reflection direction. However, the arrangement of these optical paths is not limited to this arrangement, and each optical path may be arranged so that the first dichroic mirror 202 and the second dichroic mirror 203 are arranged in opposite directions in the transmission direction and the reflection direction.

The optical path L2 for the SLO optical system and the fixation lamp is used for the acquisition of the SLO fundus image and the fixation of the eye E to be inspected. The optical path L2 is provided with an SLO scanning means 204, lenses 205, 206, a mirror 207, a third dichroic mirror 208, a photodiode 209, an SLO light source 210, and a fixation lamp 211. The components other than the fixation lamp 211 on the optical path L2 constitute the SLO optical system.

The SLO scanning means 204 scans the light emitted from the SLO light source 210 and the fixation lamp 211 on the eye E to be inspected. The SLO scanning means 204 is composed of an X scanner that scans in the X direction and a Y scanner that scans in the Y direction. In this embodiment, the X scanner is composed of a polygon mirror and the Y scanner is composed of a galvano mirror. However, the X scanner and the Y scanner are not limited to this, and can be configured by using any deflection means according to a desired configuration.

The lens 205 is a focusing lens, and is driven along the optical axis direction indicated by an arrow in the figure by a motor (not shown) controlled by a control unit 300 for focusing the SLO optical system and the fixation lamp. ..

The mirror 207 is a prism on which a perforated mirror or a hollow mirror is deposited, and separates the illumination light by the SLO light source 210 and the light from the fixation lamp and the return light from the eye E to be inspected. Specifically, the mirror 207 passes the illumination light from the SLO light source 210 and the light from the fixation lamp, reflects the return light from the eye E to be inspected, and guides the light to the photodiode 209. The photodiode 209 may be provided in the passing direction of the mirror 207, and the third dichroic mirror 208, the SLO light source 210, and the fixation lamp 211 may be provided in the reflection direction.

The third dichroic mirror 208 branches the optical path L2 into an optical path to the SLO light source 210 and an optical path to the fixation lamp 211 for each wavelength band of light passing through the optical path L2. Specifically, the fixation lamp 211 is provided in the transmission direction of the third dichroic mirror 208, and the SLO light source 210 is provided in the reflection direction. The SLO light source 210 may be arranged in the transmission direction of the third dichroic mirror 208, and the fixation lamp 211 may be provided in the reflection direction.

The photodiode 209 detects the return light from the eye E to be inspected and generates a signal corresponding to the return light. The control unit 300 can obtain a front image (SLO image) of the fundus of the eye E to be inspected based on the signal (SLO signal) generated by the photodiode 209.

The SLO light source 210 generates light having a wavelength near 780 nm. The light emitted from the SLO light source 210 is reflected by the third dichroic mirror 208, passes through the mirror 207, passes through the lenses 206 and 205, and is scanned by the SLO scanning means 204 on the eye E to be inspected. The return light from the eye E to be inspected returns to the same path as the illumination light, is reflected by the mirror 207, and is guided to the photodiode 209. By processing the output signal of the photodiode 209 by the control unit 300, a front image of the fundus of the eye E to be inspected can be obtained.

The fixation lamp 211 can generate visible light to promote fixation of the subject. The light emitted from the fixation lamp 211 passes through the third dichroic mirror 208, passes through the mirror 207 and the lenses 206 and 205, and is scanned by the SLO scanning means 204 on the eye E to be inspected. At this time, the control unit 300 blinks the fixation lamp 211 in accordance with the movement of the SLO scanning means 204 to create an arbitrary shape at an arbitrary position on the eye E to be inspected and promote the fixation of the subject. be able to.

In this embodiment, SLO is used as a fundus observation system for observing the fundus, but the configuration of the fundus observation system is not limited to this. For example, the fundus observation system may be configured by using a known observation system such as a fundus camera for photographing the fundus.

Next, the optical path L3 for observing the anterior segment of the eye is provided with lenses 212, 213, a split prism 214, and a CCD 215 for observing the anterior segment of the eye, which detects infrared light. These components arranged in the optical path L3 for observing the anterior segment of the eye constitute an optical system for observing the anterior segment of the eye.

In the optical path L3, light having a wavelength of about 970 nm is irradiated to the anterior segment of the eye E to be inspected from a light source for observing the anterior segment (not shown). The reflected light from the anterior segment of the eye E to be inspected enters the split prism 214 via the objective lens 201, the first dichroic mirror 202, and the lens 212.

The split prism 214 is arranged at a position conjugate with the pupil of the eye E to be inspected. The light emitted from the split prism 214 enters the CCD 215 via the lens 213.

The CCD215 has a sensitivity in the wavelength of the irradiation light from a light source for observing the anterior segment of the eye (not shown), specifically in the vicinity of 970 nm. The CCD215 detects the reflected light from the anterior segment of the eye and generates a signal corresponding to the reflected light from the anterior segment of the eye. The control unit 300 can generate an anterior segment image of the eye E to be inspected based on the signal generated by the CCD 215. At this time, the control unit 300 detects the reflected light that has passed through the split prism 214 by the CCD 215 to determine the distance of the photographing optical system 200 from the split image of the anterior eye portion to the eye E to be inspected in the Z direction (depth direction). Can be detected. The generated anterior segment image can be used at the time of alignment between the photographing optical system 200 and the eye E to be inspected.

Next, the optical path L1 of the OCT optical system will be described. The optical path L1 forms an optical path for the OCT optical system as described above, and is used for acquiring an interference signal for forming a tomographic image of the eye E to be inspected. The optical path L1 is provided with an XY scanner 216 and lenses 217 and 218.

The XY scanner 216 constitutes an OCT scanning means for scanning the measurement light on the eye E to be inspected. Although the XY scanner 216 is shown as one mirror, it is composed of two galvano mirrors that perform scanning in the X-axis and Y-axis directions, respectively. The configuration of the XY scanner 216 is not limited to this, and can be configured by using any deflection means. Further, the XY scanner 216 may be configured by a deflection mirror such as a MEMS mirror that can deflect light in a two-dimensional direction.

The lens 217 is a focusing lens used to focus the measurement light emitted from the optical fiber 224 connected to the optical coupler 219 to the eye E to be inspected. The lens 217 is driven by a motor (not shown) controlled by the control unit 300 in the optical axis direction of the measurement light indicated by the arrow in the figure. By this focusing, the return light of the measurement light from the eye E to be inspected is simultaneously imaged and incident on the tip of the optical fiber 224 in a spot shape. The optical fiber 224, each optical member arranged in the optical path L1, the first and second dichroic mirrors 202 and 203, the objective lens 201, and the like constitute an OCT measurement optical system in which the measurement light propagates in the OCT optical system.

Next, the configuration of the optical path from the OCT light source 220, the reference optical system, and the spectroscope 230 will be described. The optical fiber 224 is connected to an optical coupler 219. The optical coupler 219 is connected to an optical fiber 224 of the OCT measurement optical system, an optical fiber 225 connected to the OCT light source 220, an optical fiber 226 of the OCT reference optical system, and an optical fiber 227 connected to the spectroscope 230. The optical coupler 219 is a divider that divides the light from the OCT light source 220 into the measurement light and the reference light, and as an interference unit that causes the return light and the reference light of the measurement light from the eye E to interfere with each other to generate interference light. Function. In this embodiment, the optical fibers 224 to 227 are single-mode optical fibers connected to and integrated with the optical coupler 219.

The OCT light source 220 is an SLD (Super Lumine Diode), which is a typical low coherent light source. In this embodiment, the OCT light source 220 has a center wavelength of 855 nm and a wavelength bandwidth of about 100 nm. Here, the bandwidth is an important parameter because it affects the resolution of the obtained tomographic image in the optical axis direction.

In this embodiment, SLD is used as the OCT light source 220, but as the OCT light source 220, it is sufficient that low coherent light can be emitted, and ASE (Amplified Spontaneous Emission) or the like can also be used. Further, in view of measuring the eye, those having a central wavelength of near-infrared light can be used. Further, the central wavelength affects the lateral resolution of the obtained tomographic image, and the one having a short central wavelength may be used as much as possible. In this embodiment, a light source having a center wavelength of 855 nm is used for both of the above reasons.

The light emitted from the OCT light source 220 is the measurement light propagating through the optical fiber 225 and the OCT measurement optical system such as the optical fiber 224 and the reference light propagating through the OCT reference optical system such as the optical fiber 226 via the optical coupler 219. It is divided into. The measurement light is irradiated to the eye E to be observed through the polarization adjusting unit 228 and the optical path L1 of the OCT optical system described above, and reaches the optical coupler 219 as return light through the same optical path due to reflection and scattering by the eye E to be observed. do.

On the other hand, the reference light reaches the reference mirror 221 and is reflected through the optical fiber 226, the polarization adjusting unit 229, the lens 223, and the dispersion compensating glass 222 inserted to match the dispersion of the measurement light and the reference light. Then, it returns to the same optical path and reaches the optical coupler 219. Here, the optical fiber 226, the polarization adjusting unit 229, the lens 223, the dispersion compensation glass 222, and the reference mirror 221 constitute an OCT reference optical system.

The polarization adjusting unit 228 is a polarization adjusting unit on the measurement light side provided in the optical fiber 224. The polarization adjusting unit 229 is a polarization adjusting unit on the reference light side provided in the optical fiber 226. The polarization adjusting units 228 and 229 have some portions in which an optical fiber is drawn in a loop. In the polarization adjusting units 228 and 229, the loop-shaped portion is rotated around the longitudinal direction of the fiber to twist the fiber, and the polarization states of the measurement light and the reference light are adjusted and matched, respectively. can.

The return light and the reference light of the measurement light from the eye E to be inspected are combined by the optical coupler 219 to become interference light. Here, when the optical path length of the measurement light and the optical path length of the reference light are in a state of being substantially equal, the return light and the reference light of the measurement light interfere with each other and become interference light. The reference mirror 221 is held in an adjustable manner in the optical axis direction of the reference light indicated by the arrow in the figure by a motor and a drive mechanism (not shown) controlled by the control unit 300, and the measurement varies depending on the measured unit of the eye E to be inspected. It is possible to match the optical path length of the reference light with the optical path length of the light. The interfering light is guided to the spectroscope 230 via the optical fiber 227.

The spectroscope 230 is provided on the base 250. The spectroscope 230 is provided with a lens 232, 234, a diffraction grating 233, and a line sensor 231. The interference light emitted from the optical fiber 227 becomes substantially parallel light through the lens 234, is separated by the diffraction grating 233, and is imaged on the line sensor 231 by the lens 232. The line sensor 231 is shown as an example of a light receiving element that receives the interference light and outputs an output signal corresponding to the interference light. The control unit 300 can acquire information about the tomography of the eye E to be inspected and generate a tomographic image based on the signal generated by the line sensor 231.

In this embodiment, the Michelson interference system is used as the interference system as in the above configuration, but a Mach-Zehnder interference system may be used. For example, a Mach-Zehnder interference system can be used when the light amount difference is large, and a Michelson interference system can be used when the light amount difference is relatively small, depending on the light amount difference between the measurement light and the reference light.

With the above configuration, the OCT apparatus 1 can acquire a tomographic image of the eye E to be inspected, and can acquire an SLO image of the eye E to be inspected having high contrast even with near infrared light. can.

(Structure of control unit)
The configuration of the control unit 300 will be described with reference to FIG. FIG. 3 schematically shows the configuration of the control unit 300. The control unit 300 includes an acquisition unit 310, an image generation unit 320, a reconstruction unit 321, an analysis unit 322, a target acquisition unit 323, a data correction unit 324, a motion contrast generation unit 325, a drive control unit 330, a storage unit 340, and A display control unit 350 is provided.

The acquisition unit (first acquisition means) 310 acquires various signals from the photodiode 209, CCD215, and line sensor 231 of the photographing optical system 200. Further, the acquisition unit 310 acquires a signal after Fourier transform generated based on the interference signal from the line sensor 231 and a signal obtained by performing some signal processing on the signal from the image generation unit 320 and the reconstruction unit 321. You can also do it.

The image generation unit (generation means) 320 is based on signals from the acquisition unit 310, the reconstruction unit 321 and the motion contrast generation unit 325, and the anterior segment image, the SLO image, the tomographic image, and the motion contrast image (OCTA image). ) Is generated. The image generation unit 320 converts the tomographic data in the depth direction (Z direction) at a certain point of the eye to be inspected E acquired from the acquisition unit 310 or the reconstruction unit 321 into luminance or density information, and converts the tomographic data in the depth direction at the point. Acquire a tomographic image. The scanning method for acquiring the interference signal in the depth direction at one point of the object to be inspected is called A scan, and the obtained tomographic image is called A scan image.

A plurality of A-scan images can be acquired by repeatedly performing such A-scan while scanning the measurement light in a predetermined transverse direction of the object to be inspected by the XY scanner 216. For example, if the measurement light is scanned in the X direction by the XY scanner 216, a tomographic image on the XZ plane is obtained, and if it is scanned in the Y direction, a tomographic image on the YZ plane is obtained. The method of scanning the object to be inspected in a predetermined transverse direction in this way is called a B scan, and the obtained tomographic image is called a B scan image.

The reconstruction unit 321 generates three-dimensional volume data of the eye E to be inspected based on the interference signal from the line sensor 231 acquired by the acquisition unit 310. As a result, the photographing data photographed by the photographing optical system 200 is reconstructed from the interference signal data as three-dimensional volume data by the reconstructing unit 321.

Specifically, the acquisition unit 310 acquires the interference signal from the line sensor 231 as 12-bit integer format data. The reconstruction unit 321 performs wave number transform, fast Fourier transform (FFT: Fast Fourier Transform), and absolute value transform (acquisition of amplitude) for each point on the eye E to be inspected, and performs the absolute value transform (acquisition of amplitude) in the depth direction. Generate as tomographic data. The reconstruction unit 321 can generate the three-dimensional volume data 400 by converting and combining all the tomographic data in the region to be imaged.

Here, the tomographic data for an arbitrary point (x, y) on the eye E to be inspected is A scan data, and the A scan data for the first scanning direction corresponding to one two-dimensional tomographic image in the three-dimensional volume data 400. The set of is called B scan data. In the following, B scan data will be referred to as tomographic image data. As shown in FIG. 4, the three-dimensional volume data 400 corresponds to a plurality of tomographic image data 401 to 40n arranged in the second scanning direction for each scanning position. The number n, which is the number of tomographic image data included in the three-dimensional volume data 400, may be set to an arbitrary number according to a desired configuration.

Here, the three-dimensional volume data 400 will be described in more detail with reference to FIG. FIG. 4 schematically shows the structure of the three-dimensional volume data 400 according to this embodiment. The reconstructing unit 321 can generate tomographic image data 401 corresponding to the B scan by reconstructing the tomographic data acquired by photographing the eye E to be inspected along one scanning line with the photographing optical system 200. can. The plane on which the interference signal related to the B scan data is acquired is defined as the xz-axis plane, and the eye E to be inspected is continuously photographed in the y-axis direction by the photographing optical system 200. Tomographic image data 401-40n can be generated. Then, the reconstruction unit 321 can generate the three-dimensional volume data 400 by arranging the generated plurality of tomographic image data 401 to 40n in the y-axis direction.

By performing imaging so as to acquire a plurality of B scan data from the same location of the eye E to be inspected by the photographing optical system 200, the reconstruction unit 321 performs a plurality of three-dimensional volume data in a predetermined range (imaging range) of the eye E to be inspected. 400 can be generated. As another method, the reconstruction unit 321 divides the acquired interference signal into a plurality of sets based on the wavelength band and the like, and performs FFT processing on the interference signals of each set to obtain an imaging range. It is also possible to acquire a plurality of three-dimensional volume data by taking a picture once.

Further, the image generation unit 320 can also acquire the front image 410 by generating an image of the xy-axis plane from the three-dimensional volume data 400 generated by the reconstruction unit 321. Further, as will be described later, the motion contrast generation unit 325 generates motion contrast data by calculating the change between the plurality of three-dimensional volume data 400. Then, the front image 410 of the xy-axis plane generated by the image generation unit 320 from the motion contrast data becomes the OCTA image.

The analysis unit 322 analyzes the three-dimensional volume data generated by the reconstruction unit 321. More specifically, the analysis unit 322 analyzes each tomographic image data 401 to 40n (or the corresponding tomographic image) of the three-dimensional volume data, and in each tomographic image data 401 to 40n, the layered structure of the retina of the eye E to be inspected. Detects the shape of the layer boundary based on. The layer boundaries detected by the analysis unit 322 are 10 types of ILM, NFL / GCL, GCL / IPL, IPL / INL, INL / OPL, OPL / ONL, IS / OS, OS / RPE, RPE / Choroid, and BM. .. The detected layer boundary is not limited to this, and may be changed according to a desired configuration.

Further, the layer boundary detection method may be performed by any known method. For example, the analysis unit 322 applies a median filter and a Sobel filter to the tomographic image corresponding to the tomographic image data to generate an image (hereinafter, also referred to as a median image and a Sobel image, respectively). Next, the analysis unit 322 generates a profile for each data corresponding to the A scan from the generated median image and the Sobel image. The generated profile is a brightness value profile in the median image and a gradient profile in the Sobel image. Then, the analysis unit 322 detects the peak in the profile generated from the Sobel image. The analysis unit 322 detects and extracts the boundary of each region of the retinal layer by referring to the profile of the median image corresponding to before and after the detected peak and between the peaks.

The target acquisition unit (second acquisition means) 323, the data correction unit 324, and the motion contrast generation unit 325 function to generate motion contrast data. The target acquisition unit 323 acquires the target of the distribution of the tomographic data to be corrected by the data correction unit 324. In this embodiment, the target acquisition unit 323 acquires the target stored in the storage unit 340 in advance. The target can be set from past test results for each subject, statistical data of a plurality of subjects, and the like.

The data correction unit (correction means) 324 corrects the distribution of the tomographic data included in the three-dimensional volume data generated by the reconstruction unit 321 as a preprocessing for motion contrast data generation. The correction method will be described later.

The motion contrast generation unit 325 first corrects the positional deviation between a plurality of three-dimensional volume data captured in the same range of the eye E to be inspected for each tomographic image data. The misalignment correction method may be any method.

For example, the motion contrast generation unit 325 photographs the same range M times, and in the acquired M three-dimensional volume data, positions the tomographic image data corresponding to the same location by using features such as the fundus shape. Make a match. Specifically, one of the M tomographic image data is selected as a template, the similarity with other tomographic image data is obtained while changing the position and angle of the template, and the amount of misalignment with the template is obtained. .. After that, the motion contrast generation unit 325 corrects each tomographic image data based on the obtained positional deviation amount, and corrects the positional deviation between the M three-dimensional volume data.

ここで、Ａｘｙは断層像データＡの位置（ｘ、ｙ）における輝度、Ｂｘｙは断層像データＢの同一位置（ｘ、ｙ）における輝度を示している。なお、脱相関値Ｍｘｙを求める２つの三次元ボリュームデータは、互いに対応する各断層像データに関する撮影時間が所定の時間間隔以内であればよく、撮影時間が連続していなくてもよい。 Next, the motion contrast generation unit 325 obtains the decorrelation value Mxy between the tomographic image data of the two three-dimensional volume data in which the imaging times for the tomographic image data are continuous with each other by Equation 1.

Here, Axy indicates the brightness at the position (x, y) of the tomographic image data A, and Bxy indicates the brightness at the same position (x, y) of the tomographic image data B. It should be noted that the two three-dimensional volume data for which the decorrelation value Mxy is obtained may have the imaging time for each tomographic image data corresponding to each other within a predetermined time interval, and the imaging time may not be continuous.

The decorrelation value Mxy is a value of 0 to 1, and the larger the difference between the two luminances, the larger the value of Mxy. The motion contrast generation unit 325 can obtain a plurality of decorrelation values Mxy from three or more three-dimensional volume data repeatedly acquired at the same position. The motion contrast generation unit 325 can generate final motion contrast data by performing statistical processing such as maximum value calculation and averaging calculation of a plurality of obtained decorrelation values Mxy.

On the other hand, the motion contrast calculation formula shown in Equation 1 tends to be easily affected by noise. For example, if there is noise in the non-signal portion of a plurality of tomographic image data and the values are different from each other, the decorrelation value becomes high and the noise is superimposed on the motion contrast image.

In order to avoid this, the motion contrast generation unit 325 can regard the tomographic data below the predetermined threshold value Dth as noise and replace it with zero as preprocessing. Hereinafter, Dth is referred to as a noise threshold. Further, the process of replacing the tomographic data below the noise threshold Dth with zero is described as a noise mask process. As a result, the image generation unit 320 can generate a motion contrast image with reduced influence of noise based on the generated motion contrast data. As an example of the calculation method of the noise threshold value Dth, for example, when the average brightness value of the entire B scan data taken without an object to be inspected is BGa and the standard deviation of the brightness value of the entire B scan data is σ. It can be calculated as Dth = BGa + 2σ. In addition, any known noise threshold calculation method can be used.

The drive control unit 330 controls the drive of each component of the photographing optical system 200, such as the SLO light source 210, the SLO scanning means 204, the OCT light source 220, and the XY scanner 216. The storage unit 340 stores various images generated by the image generation unit 320, input information on the subject, programs constituting the control unit 300, and the like. The display control unit 350 controls the display unit 500, and causes the display unit 500 to display various images stored in the storage unit 340, information on the subject, and the like.

Each component of the control unit 300 can be configured by a module executed by the CPU or MPU of the control unit 300. Further, each component of the control unit 300 may be configured by a circuit or the like that realizes a specific function such as an ASIC. The storage unit 340 can be configured by using an arbitrary storage device / storage medium such as a memory or an optical disk.

(User interface configuration)
The configuration of the user interface for generating the OCTA image according to the present embodiment will be described with reference to FIG. FIG. 5 shows an example of a user interface displayed when generating an OCTA image.

The display unit 500 displays a user interface 550 for generating an OCTA image on the screen after the shooting by the shooting optical system 200 is completed. The user interface 550 is provided with three display areas: a fundus image display area 510, a tomographic image display area 520, and an OCTA image display area 530.

The fundus image 511 of the eye E to be inspected is displayed in the fundus image display area 510. The imaging area annotation 512 and the tomographic position annotation 513 are displayed on the fundus image 511. The photographing area annotation 512 indicates a photographing area by the photographing optical system 200 that has acquired the interference signal corresponding to the three-dimensional volume data. In this embodiment, the SLO image obtained from the photographing optical system 200 is displayed as the fundus image 511. However, as the fundus image 511, a fundus image taken by a fundus image capturing device other than the photographing optical system 200 may be displayed. In that case, the position of the imaging area annotation is aligned with the fundus image and displayed at an appropriate position.

The tomographic position annotation 513 indicates the position of the tomographic image 521 displayed on the tomographic image display area 520 on the fundus image 511. By moving the tomographic position annotation 513 via the input unit 360, the operator can determine the position of the tomographic image 521 to be displayed in the tomographic image display area 520 on the eye E to be inspected. The image generation unit 320 generates a tomographic image 521 corresponding to the determined position based on the three-dimensional volume data.

In the tomographic image display area 520, the tomographic image 521 corresponding to the position of the tomographic position annotation 513 is displayed. On the tomographic image 521, the upper end 522 of the generation range and the lower end 523 of the generation range for designating the generation range of the OCTA image are displayed. In FIG. 5, the upper end 522 of the generation range is indicated by a broken line, and the lower end 523 of the generation range is indicated by a chain double-dashed line.

The control unit 300 projects motion contrast data in a range corresponding to the range between the upper end 522 of the generation range and the lower end 523 of the generation range, which is the designated depth range, on a two-dimensional plane to generate an OCTA image. Specifically, the image generation unit 320 is an OCTA which is a front image of the motion contrast image based on the motion contrast data corresponding to the range between the upper end 522 of the generation range and the lower end 523 of the generation range of the entire motion contrast data. Generate an image. The motion contrast generation unit 325 may be configured to generate motion contrast data using the tomographic data in the range between the upper end 522 of the generation range and the lower end 523 of the generation range. In this case, the image generation unit 320 can generate an OCTA image based on the tomographic data in a specified depth range by generating an OCTA image based on the generated motion contrast data.

The operator can determine the positions of the upper end 522 of the generation range and the lower end 523 of the generation range via the input unit 360. The shapes of the upper end 522 of the generation range and the lower end 523 of the generation range can be specified by a specific layer shape, a straight line, or a freehand. Further, the positions of the upper end 522 of the generation range and the lower end 523 of the generation range can be arbitrarily set, and may be freely moved via the input unit 360, or may be set to a specific position or layer. However, it may be set to any distance from these. Further, the OCTA image generation range may be set as a preset in advance so that it can be selected.

In the OCTA image display area 530, the OCTA image 531 generated based on the tomographic data between the upper end 522 of the generation range and the lower end 523 of the generation range of the designated OCTA image is displayed.

As described above, the operator can use the user interface 550 to determine the tomographic data to be used to generate the OCTA image. More specifically, the operator specifies the position of the tomographic position annotation 513 and then determines the range of tomographic data used to generate the OCTA image 531 in the tomographic image display area 520. After that, the OCTA image 531 generated based on the determined tomographic data is displayed in the OCTA image display area 530.

In the above, the operator determines the tomographic data to be used for generating the OCTA image, but the method for determining the tomographic data that is the source of the OCTA image is not limited to this. For example, the control unit 300 may determine the tomographic data to be used for generating the OCTA image based on the preset inspection site, the past inspection contents of the subject, the imaging mode, and other information. In this case, the control unit 300 can specify the range of the tomographic data used for generating the OCTA image based on the layer detected by the analysis unit 322.

(OCTA image generation flow according to this embodiment)
Next, the OCTA image generation flow according to this embodiment will be described with reference to FIGS. 6 and 7. FIG. 6 shows a flowchart of the OCTA image generation process according to this embodiment.

The interference signal acquired by the photographing optical system 200 may have a partially low signal intensity when the state of the eye to be inspected E or the imaging conditions are not optimal. When the signal strength is low, the reconstructed tomographic image becomes a dark image, and the OCTA image also has a dark portion. Therefore, in the OCTA image generation process according to the present embodiment, the data correction unit 324 sets the distribution of the parameters of the tomographic data included in the three-dimensional volume data used for calculating the motion contrast data to the ideal target distribution. Make corrections to bring them closer. The control unit 300 can generate an OCTA image, which is a front image of a motion contrast image, based on the corrected tomographic data, thereby generating an OCTA image that suppresses the occurrence of light and darkness due to the strength of the tomographic signal. can. In this embodiment, the data correction unit 324 corrects each distribution of the A scan data included in the three-dimensional volume scan data.

Specifically, when the OCTA image generation process is started, in step S601, the acquisition unit 310 acquires the three-dimensional volume data from the reconstruction unit 321. As described above, the three-dimensional volume data is generated by the reconstruction unit 321 based on the interference signal acquired by the photographing optical system 200.

In step S602, the target acquisition unit 323 acquires the target for correcting the tomographic data by the data correction unit from the storage unit 340. The target may be acquired from another device connected to the control unit 300. Here, as described above, the target can be set from the past test results for each subject, the statistical data of a plurality of subjects, and the like.

In step S603, the data correction unit 324 extracts the distribution of parameters used for calculating the motion contrast data of the tomographic data to be corrected from the three-dimensional volume data. In this embodiment, the brightness value of the tomographic data corresponding to the amplitude of the complex number data after FFT is used as the parameter used for calculating the motion contrast data. The data correction unit 324 obtains the distribution of the brightness value of each A scan data included in the three-dimensional volume data in order to correct the distribution of each A scan data included in the three-dimensional volume data. The parameters used for calculating the motion contrast data are not limited to the brightness value, and may be a phase value, both a brightness value, a phase value, and the like, depending on the method for calculating the motion contrast data.

7 (a) to 7 (c) are diagrams for explaining a histogram of A scan data included in the three-dimensional volume data. In FIGS. 7A to 7C, the horizontal axis represents the luminance value, and the vertical axis represents the frequency number at each luminance. FIG. 7 (a) schematically shows an example of a histogram of A scan data when the signal strength of the interference signal is sufficiently high, and FIG. 7 (b) shows a case where the signal strength of the interference signal is low. Hereinafter, for the sake of explanation, the histogram of the A scan data to be corrected is referred to as the histogram shown in FIG. 7 (b), and the histogram of the target A scan data is referred to as the histogram shown in FIG. 7 (a).

In step S604, the data correction unit 324 corrects the distribution of the A scan data to be corrected shown by the broken line so as to approach the target shown by the solid line, as shown by the arrow in FIG. 7C.

ここで、ｙは補正後のＡスキャンデータであり、ｘが補正前のＡスキャンデータである。 The data correction unit 324 sets each A scan data so that the average value Av_0 and the standard deviation σ0 of each A scan data approach the average value Av_t and the standard deviation σt of the target A scan data acquired by the target acquisition unit 323. Transform the distribution of. Specifically, the following equation 2 is used to perform conversion so that the distribution of the target A scan data approaches the target distribution of the A scan data.

Here, y is the A scan data after correction, and x is the A scan data before correction.

The data correction unit 324 can convert the distribution of the A scan data before correction into a distribution having the average value and the standard deviation of the target A scan data by performing the calculation according to the equation 2.

Next, in step S605, the motion contrast generation unit 325 generates the motion contrast data as described above using the corrected tomographic data.

In step S606, the image generation unit 320 generates an OCTA image, which is a front image of the motion contrast image, based on the generated motion contrast data. By the OCTA image generation process, the control unit 300 generates an OCTA image based on the corrected tomographic data, so that a high-quality OCTA image can be obtained without being affected by the partial brightness of the tomographic image.

In this embodiment, the acquisition of the target (step S602) is performed after the acquisition of the three-dimensional volume data (step S601), but the target may be performed before the correction of the data distribution (step S604). .. Therefore, it may be performed after the extraction of the data distribution (step S603), or may be performed at the same time as the acquisition of the three-dimensional volume data (step S601) and the extraction of the data distribution (step S603).

As described above, the control unit 300 according to the present embodiment includes an acquisition unit 310, a target acquisition unit 323, a data correction unit 324, and an image generation unit 320. The acquisition unit 310 acquires a plurality of tomographic data indicating tomographic information of the fundus obtained based on the measurement light controlled to scan the same position of the fundus of the eye E to be inspected. The target acquisition unit 323 acquires a target of the distribution of parameters in the tomographic data, which is used to calculate the motion contrast. In this embodiment, the brightness of the tomographic data is used as the parameter. The target acquisition unit 323 acquires a predetermined distribution, which is a distribution determined for each subject or a distribution statistically determined for a plurality of subjects, as a target. The data correction unit 324 corrects the distribution of parameters in at least one tomographic data among the plurality of tomographic data so as to approach the target. The image generation unit 320 generates a motion contrast image based on the motion contrast calculated using a plurality of tomographic data including at least one tomographic data corrected by the data correction unit 324.

The control unit 300 corrects the distribution of parameters in the tomographic data, which is the original data for generating the motion contrast, so as to approach the target. Therefore, the strength of each parameter can be suppressed. Therefore, the control unit 300 can suppress the brightness of the OCTA image due to the strength of the tomographic data used for calculating the motion contrast, and can obtain a high-quality OCTA image.

When the correction is performed by the above method, if the average value or standard deviation of the A scan data to be corrected is extremely small with respect to the target, the gains Ga and Gb values in Equation 2 become large, and noise is generated on the OCTA image. May stand out. Further, when the average value or standard deviation of the A scan data to be corrected is remarkably large with respect to the target, the vascular network in the OCTA image may be obscured. As these measures, the upper limit and the lower limit of the gains Ga and Gb are set, and when the gains Ga and Gb exceed the upper limit and the lower limit, the gains Ga and Gb can be replaced with the upper limit value or the lower limit value.

Further, in this embodiment, the correction for each A scan data is given as an example, but the correction target is not limited to this. For example, the distribution of the entire B scan data or the three-dimensional volume data may be corrected so as to approach the target distribution. Further, the distribution of a plurality of consecutive sets of A scan data obtained by dividing the B scan data into strips may be corrected, or similarly, the distribution of a plurality of consecutive sets of B scan data may be corrected. Further, for example, an averaging distribution in which a plurality of three-dimensional volume data used when generating motion contrast data may be combined may be corrected. Further, the distribution of a combination of a plurality of tomographic images that can be generated by using SSADA, which is one of the motion contrast data generation methods, may be corrected. Here, it is assumed that these distributions are also included in the distribution of fault data. In these cases, the target distribution is also a distribution corresponding to the distribution of these tomographic data, that is, a distribution such as B scan data, three-dimensional volume data, and a set of a plurality of continuous A scan data. By implementing such a method, the control unit 300 can suppress the brightness of the OCTA image due to the strength of the tomographic data and generate a high-quality OCTA image. Furthermore, by collectively correcting data in a relatively wide range, it is possible to reduce overcorrection of tomographic data as compared with local correction.

Further, in this embodiment, the distribution of each A scan data included in the three-dimensional volume data is corrected, but the correction target is not limited to this. For example, among the A scan data included in the three-dimensional volume data, only the distribution of the A scan data in which the statistical value of the brightness included in the A scan data is less than a predetermined threshold value may be corrected. The statistical value may be a total value, an average value, a median value, a maximum value, a variance value, a mode value, a combination thereof, or the like. When the correction target is a distribution of B scan data or the like, the data correction unit 324 sets a predetermined threshold value of the brightness statistical value included in the correction target according to the unit of the correction target (B scan data or the like). Only the distribution of the correction target that is less than can be corrected. In addition, depending on the desired configuration, not only the lower limit threshold value but also the upper limit threshold value may be set, and correction may be performed even when the upper limit is exceeded.

Further, the data correction unit 324 compares the distribution of the A scan data as a candidate for correction with the target distribution, and only when the difference between the statistical values between the distributions is larger than a predetermined threshold value, the target A scan data. The distribution of may be corrected. As the difference in the statistical values between the distributions, for example, at least one difference among the average value, the maximum value, the mode value, the variance value, the frequency range, and the like in the distribution of the tomographic data to be compared may be used. In this case, since the correction is performed only for the tomographic data in which the distribution of the tomographic data differs from the target distribution to some extent or more, the amount of calculation related to the correction can be reduced and the processing time can be shortened. The data correction unit compares the distribution of the A scan data, which is a candidate for correction, with the distribution of the tomographic data, which is an arbitrary template selected from the three-dimensional volume data, in addition to the target distribution. May be good. In the tomographic data used as a template in this case, among the three-dimensional volume data, the average value of the brightness values is higher than the average value of the brightness of the entire three-dimensional volume data, or the brightness value of the entire three-dimensional volume data is the highest. It can be a thing or the like.

Further, in this embodiment, the data correction unit 324 corrects the distribution of the entire A scan data, but the distribution of the A scan data only in the depth range of the retina analyzed by the analysis unit 322 may be corrected. Further, the data correction unit 324 may correct the distribution of the A scan data in the range between the upper end 522 of the generation range and the lower end 523 of the generation range.

Further, in the imaging by OCT, if the eye E to be inspected moves during imaging, accurate imaging at a desired position cannot be performed, so that the desired position may be rescanned. In this case, the shooting condition at the time of rescanning may change from the shooting condition at the time of the original scan, and the brightness of the tomographic image may be bright or dark. Therefore, as a countermeasure against the occurrence of lightness and darkness in the brightness of the tomographic image due to the rescanning, the correction processing according to the present embodiment may be performed only on the rescanning portion. Even in this case, the object to be corrected is not limited to the distribution of A scan data, but may be the distribution of B scan data, a plurality of A scan data including the rescan portion, B scan data, and the above. It may be the distribution of tomographic data.

In this embodiment, the target average value and standard deviation are set, and the average value and standard deviation in the distribution of the A scan data are corrected so as to approach the target, but the target to approach the target is not limited to this. For example, the standard deviation may be maintained and only the mean may be corrected. Further, at least one of the mean value, the median value, the maximum value, the mode value, the variance value, and the frequency range may be corrected so as to approach the target. In addition to setting the target as a value prepared in advance, a method of inputting and setting from the outside of the control unit 300 may be used.

(Example 2)
In Example 1, the data correction unit 324 corrects the average value and standard deviation of the entire distribution of the target A scan data so as to be close to the target average value and standard deviation. On the other hand, in the second embodiment, the portion of the distribution of the target A scan data excluding the non-signal portion is the correction target. Hereinafter, the control unit according to the second embodiment will be described with reference to FIGS. 8A to 8C. Since the components of the control unit according to the present embodiment are the same as those of the control unit 300 according to the first embodiment, the same reference numerals will be used and the description thereof will be omitted. Hereinafter, the control unit according to the present embodiment will be described focusing on the difference from the control unit 300 according to the first embodiment.

8 (a) to 8 (c) are diagrams for explaining the object of the correction according to the second embodiment. 8 (a) to 8 (c) correspond to FIGS. 7 (a) to 7 (c), respectively. Further, in order to explain the object of correction according to this embodiment, the noise threshold value Dth is shown in FIGS. 8A to 8C. In FIGS. 8A to 8C, the horizontal axis represents the luminance value, and the vertical axis represents the frequency number at each luminance.

In imaging of retinal tomography, the acquired tomographic data contains many non-signaled parts. Noise is included in such a non-signal portion. As described above, when the tomographic data contains noise, the motion contrast based on the tomographic data also expresses the noise portion as a temporal change of the measurement target. Here, if the noise portion included in the tomographic data is also corrected by the data correction unit, the noise included in the noise portion may be amplified. In this case, noise also increases in the OCTA image based on the corrected data. Therefore, in this embodiment, the portion of the tomographic data distribution other than the non-signal portion is targeted for correction, and the distribution of the noise portion is corrected to prevent the noise from being amplified.

The data correction unit 324 according to this embodiment sets the average value Av_t of the target A scan data with respect to the distribution of the target A scan data, and corrects the distribution of each A scan data so as to approach the target. At that time, as shown in FIG. 8C, only the data having the brightness value equal to or higher than the noise threshold value Dth is corrected according to the following equation 3.

By performing the correction in this way, the data correction unit 324 has an average distribution targeting a value equal to or higher than the noise threshold Dth without changing the value less than the noise threshold Dth in the distribution of the A scan data before correction. Can be converted to data. Therefore, the control unit according to the present embodiment can prevent the noise in the OCTA image from increasing due to the correction of the data.

As described above, the data correction unit 324 according to the present embodiment corrects the distribution of the parameter portion having a value equal to or higher than the noise threshold value in the parameter distribution of the tomographic data. By generating motion contrast data based on the tomographic data corrected in this way, the control unit suppresses the influence of the brightness of the tomographic image on the OCTA image without amplifying the noise, resulting in higher image quality. OCTA image can be obtained. In addition, the signal less than the noise threshold value may be removed from the calculation of the motion contrast data.

The noise threshold Dth may be stored in the storage unit 340 in advance for each imaging optical system 200 (for each imaging device) for tomographic imaging of the eye E to be inspected, or the measurement light is stored at the start of the imaging optical system 200 or at the start of imaging. It may be set based on the background data acquired by blocking. Further, the noise threshold Dth may be set based on either the value of the parameter used for calculating the motion contrast data of the tomographic data included in the plurality of three-dimensional volume data and the frequency of the parameter. In this case, for example, the data correction unit 324 may set the lower 10th percentile of the parameter value or frequency of the fault data to be corrected as a threshold value. The value of the percentile is an example, and may be arbitrarily set according to a desired configuration.

Further, as in the first embodiment, the target to be corrected by the data correction unit 324 is not limited to the A scan data, but may be B scan data, three-dimensional volume data, or the like.

(Example 3)
In Examples 1 and 2, the data correction unit 324 corrects the distribution of the tomographic data so as to approach the preset target average value and standard deviation. On the other hand, in the third embodiment, the target average value and standard deviation are calculated from the three-dimensional volume data generated by the reconstruction unit 321 and the distribution of the tomographic data is corrected so as to approach the calculated target. Hereinafter, the control unit according to this embodiment will be described with reference to FIG. Since the components of the control unit according to the present embodiment are the same as those of the control unit 300 according to the first embodiment, the same reference numerals will be used and the description thereof will be omitted. Hereinafter, the control unit according to the present embodiment will be described focusing on the difference from the control unit 300 according to the first embodiment.

FIG. 9 is a flowchart of the OCTA image generation process according to the third embodiment. In the flowchart shown in FIG. 9, the steps other than steps S902 and S903 are the same as the steps of the OCTA image generation process according to the first embodiment, and thus the description thereof will be omitted.

When the acquisition unit 310 acquires the three-dimensional volume data in step S901, the process proceeds to step S902. In step S902, the target acquisition unit 323 selects the B scan data, which excludes the B scan data having a low signal strength from the three-dimensional volume data, as reference data. In this embodiment, B scan data in which the average value of the brightness of the retinal portion (ILM to BM) of the B scan data is larger than the average value of the brightness of all the retinal parts in the three-dimensional volume data is selected as reference data.

First, the target acquisition unit 323 obtains the average value of the brightness of each A scan data included in the three-dimensional volume data from ILM to BM according to Equation 4. A_av (x, y) represents the average value of the x-th A scan in the y-th B scan. Each depth data Z (i, x, y) in each A scan is added from the ILM position Pilm (x, y) to the BM position Pbm (x, y). A_av (x, y) is calculated by dividing the added total by the number of data.

Next, the target acquisition unit 323 obtains the average value of the brightness of each B scan data according to the equation 5. B_av (y) represents the average value of the brightness of the y-th B scan data in the three-dimensional volume data. Here, B_av (y) is obtained by adding the average values of the brightnesses of all the A scan data in the y-th B scan data obtained by Equation 4 and dividing by the number of A scan data m in the B scan data to obtain B_av (y). calculate.

Further, the target acquisition unit 323 obtains the average value Av_all of the brightness from ILM to BM of the entire three-dimensional volume data according to Equation 6. Here, Av_all is obtained by adding the average values of the brightnesses of all B scans in the three-dimensional volume data obtained in Equation 5 and dividing by the number n of B scans in the three-dimensional volume data.

Finally, the target acquisition unit 323 selects the B scan as reference data when the equation 7 is satisfied, that is, when the average value B_av (y) of the B scan data is larger than the average value Av_all of the entire three-dimensional volume data. ..

Thereby, the B scan data excluding the B scan data having a low signal strength from the n B scan data included in the three-dimensional volume data can be selected as reference data. In particular, by using the brightness value of only the retinal portion in the above calculation, reference data can be selected without excluding B scan data in which the non-retinal portion such as the macula and the optic nerve head is large.

Next, in step S903, the target acquisition unit 323 calculates the average value Av_t and standard deviation σ_t of the target A scan data using the B scan data selected as reference data. The target acquisition unit 323 can calculate the target average value Av_t by dividing the total of the average value B_av'of the brightness of each selected B scan data by the selected number of B scans s according to the equation 8. can.

Further, the target acquisition unit 323 is selected by summing all the squares of the difference between the average value of the brightness of each B scan data selected as the reference data and the average value Av_t obtained by the equation 8 according to the equation 9. The standard deviation δ_t is calculated by dividing by the number of B scans s.

From the above, the target acquisition unit 323 obtains the target average value Av_t and standard deviation σ_t. When the target average value Av_t and standard deviation σ_t are obtained, the process proceeds to step S904. Since the processing after step S904 is the same as the processing according to the first embodiment, the description thereof will be omitted.

The average value Av_t and the standard deviation σ_t obtained by the target acquisition unit 323 can be obtained by using data in the same range as the data used in the data distribution correction (S905) to be performed later. For example, the target acquisition unit 323 obtains the average value or standard deviation of the retinal portion when the data correction unit 324 corrects the distribution of tomographic data only in the retinal portion, and does not correct the distribution including the non-retinal portion. The average value and standard deviation of the whole including the retinal part can be obtained.

Further, the steps related to the calculation of the target (steps S902 and S903) may be performed simultaneously with or after the steps related to the extraction of the data distribution (step S904).

As described above, the target acquisition unit 323 according to the present embodiment sets a target based on at least one tomographic data among the plurality of three-dimensional volume data. The control unit according to this embodiment corrects the distribution of the A scan data so as to approach the target (mean value Av_t, standard deviation σ_t) set in this way. In this case, the tomographic data in the 3D volume data is corrected by correcting the distribution of the tomographic data in the 3D volume data with the target of the ideal data distribution with high signal strength in the same 3D volume data. The strength of each is suppressed. Therefore, the control unit can generate an OCTA image in which the occurrence of light and darkness due to the strength of the tomographic data is further suppressed. In addition, by obtaining the target from the acquired three-dimensional volume data, it is possible to more effectively correct the change in the tomographic data due to the difference in the diseased eye and the imaging conditions according to the actual situation.

When the correction method is applied to the OCTA image generation process according to this example in Example 2, the average value Av_t and standard deviation σ_t of the tomographic data having a brightness equal to or higher than the noise threshold Dth are calculated and corrected. Then, a more appropriate target can be set for the correction target.

Further, in the selection of the B scan data according to this embodiment, the average value of the brightness of the retinal portion was calculated, but the average value of the brightness may be calculated from a specific layer in the retina, or the non-retinal portion may be calculated. It may be calculated from the entire A scan data including.

Further, in this embodiment, B scan data larger than the average value of the brightness of all the retinal portions in the three-dimensional volume data was selected as reference data. However, the method of selecting the reference data is not limited to this, and for example, the higher B scan data in descending order of the brightness value may be selected. Further, the luminance distribution may be obtained and the B scan data in which the distribution is most concentrated may be selected. Also by these methods, it is possible to select the tomographic data of the portion that does not include the tomographic data having low signal strength. In addition, as a countermeasure against the occurrence of lightness and darkness in the brightness of the tomographic image due to the rescan, reference data may be selected only from the rescan portion or the original scan portion.

Further, in this embodiment, B scan data in which the average value of the brightness of the B scan data is larger than the average value of all the brightness in the three-dimensional volume data is selected as reference data. However, the criteria for selecting reference data for determining goals are not limited to this. For example, the target acquisition unit 323 performs a B scan in which any one of the average value, the median value, the maximum value, the mode value, the variance value, and the frequency range of the brightness values of the three-dimensional volume data is higher than the threshold value. The data may be selected as reference data. The threshold value may be a predetermined threshold value, or may be a threshold value calculated from the three-dimensional volume data, such as the average value of all the luminances in the three-dimensional volume data in this embodiment.

As in the case of the first embodiment, the correction target according to the present embodiment is not limited to the A scan data, and the distribution in the B scan data, the entire three-dimensional volume data, etc. is corrected to the target distribution as described above. May be good.

Further, as in the first embodiment, the data correction unit 324 distributes the A scan data in which the brightness statistical value included in the A scan data is less than a predetermined threshold value among the A scan data included in the three-dimensional volume data. Only may be corrected. The statistical value may be a total value, an average value, a median value, a maximum value, a variance value, a mode value, or the like. Further, when the correction target is a distribution such as B scan data, the data correction unit 324 corrects the brightness statistical value included in the correction target to be less than a predetermined threshold value according to the unit of the distribution of the correction target. Only the distribution of objects can be corrected.

Further, in this embodiment, the target acquisition unit 323 obtains the average value of the B scan data and selects the B scan data as the reference data used for calculating the target. The unit of data for which the average value is calculated is not limited to this. For example, the target acquisition unit 323 may obtain an average value for each A scan data or three-dimensional volume data and select reference data. In addition, the target acquisition unit 323 calculates an average value for each of a plurality of consecutive A scan data obtained by dividing the B scan data into strips, or for each of a plurality of similar continuous B scan data, and selects reference data. May be good. Further, the target acquisition unit 323 may select reference data by obtaining the average value of a plurality of three-dimensional volume data obtained by photographing the same range a plurality of times. In these cases as well, the target acquisition unit 323 calculates the target based on the selected reference data.

Further, in the target setting method according to the present embodiment, the target acquisition unit 323 obtains one target for the tomographic data in the three-dimensional volume data, but obtains a separate target for the tomographic data for each specific range. You may. For example, the target acquisition unit 323 may obtain a target from one or more A scan data groups or B scan data groups adjacent to the A scan data or B scan data to be corrected. Similarly, the target may be obtained from a plurality of A scan data to be corrected or a group of a plurality of A scan data adjacent to the B scan data or a group of a plurality of B scan data. Examples of these cases will be described with reference to FIGS. 10 (a) to 10 (d). The width of each scan line in FIGS. 10 (a) to 10 (c) is shown wider than it actually is for the sake of explanation. In practice, the spacing between adjacent scanlines is so narrow that the distribution of tomographic data between adjacent scanlines or scanline groups is essentially equal.

FIG. 10A shows a case where the target is obtained from the A scan data group adjacent to the A scan data to be corrected. In this case, the target acquisition unit 323 selects the A scan data groups La1 and La3 adjacent to the A scan data La2 to be corrected as reference data. That is, the A scan data groups La1 and La3 acquired from the A scanline group adjacent to the A scanline from which the A scan data La2 was acquired are selected as reference data. Then, the target average value and standard deviation are obtained from the A scan data groups La1 and La3. The average value and standard deviation are obtained in the same manner as in the above method. Similarly, FIG. 10B shows a case where the target is obtained from the B scan data group adjacent to the B scan data to be corrected. In this case, the B scan data groups Lb1 and Lb3 acquired from the B scanline group adjacent to the B scanline from which the B scan data Lb2 was acquired are selected as reference data. Then, the target average value and standard deviation are obtained from the B scan data groups Lb1 and Lb3.

Similarly, FIG. 10C shows a case where a target is obtained from a group of a plurality of A scan data to be corrected and a plurality of adjacent A scan data. In this case, the target acquisition unit 323 selects a group of a plurality of A scan data Aa1 and Aa3 adjacent to the plurality of A scan data Aa2 to be corrected as reference data. That is, the A scan data groups Aa1 and Aa3 acquired from the size area corresponding to the area adjacent to the area in which the plurality of A scan data Aa2 are acquired are selected as reference data. Then, the target average value and standard deviation are obtained from the groups Aa1 and Aa3 of the plurality of A scan data. Similarly, FIG. 10D shows a case where a target is obtained from a group of a plurality of B scan data to be corrected and a plurality of adjacent B scan data. In this case, the target acquisition unit 323 selects the groups Ab1 and Ab3 of the B scan data acquired from the area corresponding to the area adjacent to the area where the plurality of B scan data Ab2 are acquired as reference data. Then, the target average value and standard deviation are obtained from the groups Ab1 and Ab3 of the plurality of B scan data.

In this way, the target acquisition unit 323 can set the target based on the tomographic data adjacent to the tomographic data having the distribution of the parameters to be corrected. In this case, the data correction unit 324 corrects the distribution of the macula and the nipple in the correction of the part where the distribution of the tomographic data is slightly different from the distribution of the tomographic data of other places such as the B scan data including the macula and the nipple. The correction can be made in consideration of the characteristics. Therefore, the control unit can perform effective correction according to the actual situation.

In these cases, the data correction unit 324 compares the distribution of the tomographic data that is a candidate for correction with the distribution of the adjacent tomographic data group, and the difference between the statistical values between the distributions is larger than the predetermined threshold value. The distribution of the target fault data may be corrected only for. As the difference in the statistical values between the distributions, for example, at least one difference among the average value, the maximum value, the mode value, the variance value, the frequency range, and the like in the distribution of the tomographic data to be compared may be used. In this case, since the correction is performed only for the fault data whose distribution of the fault data is different from the adjacent fault data group to some extent or more, the amount of calculation related to the correction can be reduced and the processing time can be shortened. ..

Further, as in the above case, the target acquisition unit 323 may obtain a target from one or more three-dimensional volume data groups whose shooting time is continuous with the three-dimensional volume data to be corrected.

The correction of the tomographic data distribution according to the above embodiment is not limited to the correction of the tomographic data distribution directly generated from the interference signal acquired by the photographing optical system 200. Corrects the distribution of tomographic data after applying other corrections for tomographic data, for example, correction processing for compensating for signal attenuation in the depth direction caused by the roll-off characteristics of the imaging device (hereinafter referred to as roll-off correction). You may.

Hereinafter, the roll-off correction process will be briefly described. The correction coefficient H (z) in the depth direction for performing roll-off correction can be expressed, for example, as in Equation 11 when the normalized roll-off characteristic function with the depth position z as an argument is RoF (z).

Here, BGa and σ indicate the average value and the standard deviation, which are statistical values regarding the brightness distribution BG of the entire B scan data acquired in the absence of the object to be inspected, respectively. Further, BGa (z) and σ (z) are the average value and standard of the brightness distribution in the direction orthogonal to the z-axis calculated at each depth position (z) in the B scan data acquired in the absence of the object to be inspected. Shows the deviation. Further, z ₀ indicates a reference depth position included in the B scan range. An arbitrary constant may be set for z ₀ , but here, it is set to a value of 1/4 of the maximum value of z. In addition, BGa and σ may indicate an average value and a standard deviation which are statistical values regarding the brightness distribution BG of the entire A scan data acquired a plurality of times in the absence of an object to be inspected, respectively. Similarly, BGa (z) and σ (z) are the average luminance distributions in the direction orthogonal to the z-axis calculated at each depth position (z) in the A scan data acquired multiple times in the absence of an object to be inspected. Values and standard deviations may be indicated.

FIG. 11A shows an example of the average value BGa (z) of the brightness at each depth position (z) in the B scan data acquired in the absence of the object to be inspected. Further, FIG. 11B shows an example of the result of multiplying the average value BGa (z) by the correction coefficient H (z) to compensate for the luminance attenuation in the depth direction. In FIGS. 11A and 11B, the horizontal axis represents the depth position z, and the vertical axis represents the amplitude of the complex number data corresponding to the luminance.

The three-dimensional volume data Ih (x, y, z) after roll-off correction can be expressed by the following equation 12.

By performing such roll-off correction, it is possible to acquire tomographic data in which signal attenuation in the depth direction caused by the roll-off characteristic of the photographing apparatus is reduced. In the control unit 300 according to the above embodiment, the target acquisition unit 323 obtains the correction coefficient H (z) based on the tomographic data acquired from the reconstruction unit 321. After that, the data correction unit 324 performs roll-off correction based on the correction coefficient H (z), and corrects the distribution of the tomographic data after the roll-off correction according to the above embodiment.

As described above, the correction of the tomographic data distribution according to the above embodiment may be performed in combination with other correction methods such as roll-off correction. The roll-off correction formula is not limited to the above, and any known correction formula may be used. Further, as the parameters of the tomographic data used for the roll-off correction, those corresponding to the parameters used for the calculation of the motion contrast can be used.

(Example 4)
In Example 3, the target (mean value and standard deviation) of the parameter distribution was calculated from the three-dimensional volume data generated by the reconstruction unit 321 and the distribution of the parameter of the tomographic data was corrected so as to approach the target. On the other hand, in the fourth embodiment, the approximate value of the tomographic data projected in the front direction of the fundus is calculated based on the three-dimensional volume data to obtain the correction coefficient, and the parameter of the tomographic data is corrected based on the obtained correction coefficient. do.

When taking a tomographic image by OCT, the time interval from the original scan to the rescan becomes longer due to factors such as the scan order and poor fixation, and the condition of the eye to be inspected (eyelids, eyelashes, pupil position, etc.) changes during that time. I have something to do. In this case, the signal strength of the acquired interference signal changes according to a change in the shooting condition or the like, and the strength of the pixel value occurs in the tomographic image generated based on the interference signal. When the tomographic data based on such an interference signal is used, a tomographic image or motion contrast data including a low-luminance region due to the strength of the pixel value is generated, which hinders observation and analysis.

Therefore, in this embodiment, the correction coefficient of the tomographic data is obtained based on the distribution of the approximate values of the tomographic data, and the parameters of the tomographic data are corrected using the correction coefficient, so that the partial brightness of the tomographic image is affected. Instead, a high-quality OCTA image is obtained.

More specifically, in this embodiment, as the first schematic parameter, the parameter of the tomographic data obtained by smoothing the projected image of the front of the fundus based on the three-dimensional volume data in the two-dimensional direction is obtained. Further, as the second schematic parameter, the parameter of the tomographic data obtained by smoothing the projected image in the one-dimensional direction (axial direction of the main scan) is obtained. Then, the correction coefficient is calculated by dividing the first schematic parameter by the second schematic parameter. The parameters of the tomographic data are corrected using the calculated correction coefficient, and the motion contrast is calculated using the parameters of the corrected tomographic data. As a result, the tomographic data corresponding to the low-luminance region is corrected by the correction coefficient based on the schematic parameters, so that a high-quality OCTA image can be obtained without being affected by the partial brightness of the tomographic image.

Hereinafter, the control unit according to this embodiment will be described with reference to FIGS. 12 to 14 (c). Since the components of the control unit according to the present embodiment are the same as those of the control unit 300 according to the third embodiment, the same reference numerals will be used and the description thereof will be omitted. Hereinafter, the control unit according to the present embodiment will be described focusing on the difference from the control unit 300 according to the third embodiment.

FIG. 12 is a flowchart of the OCTA image generation process according to the fourth embodiment. In the flowchart shown in FIG. 12, the steps S1210, S1270, and S1280 are the same as the corresponding steps of the OCTA image generation process according to the third embodiment, and thus the description thereof will be omitted.

13 (a) to 13 (f) show examples of OCTA images, projected images, and the like related to the OCTA image generation process according to this embodiment, respectively. It is assumed that the images shown in FIGS. 13 (a) to 13 (f) are generated based on the same three-dimensional volume data. FIG. 13A shows an OCTA image 1301 generated without applying the processing according to this embodiment. FIG. 13B shows an OCT projection image Ph corresponding to the front surface of the fundus. _{FIG. 13 (c) shows the first schematic parameter S 2d} (x, y) obtained by smoothing the OCT projection image Ph in the two-dimensional direction. _{FIG. 13D shows a second schematic parameter S 1d} (x, y) in which the OCT projection image Ph is smoothed in the one-dimensional direction (main scanning direction). FIG. 13 (e) shows a correction coefficient C (x, y) obtained by dividing _{the first schematic parameter S 2d} (x, y) by the second schematic parameter S _{1d (x, y).} FIG. 13 (f) shows the OCTA image 1304 generated by the OCTA image generation process according to this embodiment. Here, (x, y) indicates the position of each pixel in the xy plane corresponding to the front surface of the fundus.

Referring to FIG. 13B, a low-luminance region 1312 exists in the OCT projection image Ph as indicated by a white arrow, and a difference in brightness is generated in the sub-scanning direction (vertical direction). When the tomographic data including such a low-luminance region 1312 is used, an OCTA image 1301 including the low-luminance region 1311 as shown by the white arrow in FIG. 13A is generated, which hinders observation and analysis. Therefore, in this embodiment, the correction coefficient is obtained using the approximate parameter obtained by calculating the approximate value distribution of the tomographic data, and the tomographic data is corrected using the correction coefficient to reduce the occurrence of the low-luminance region. .. Here, the approximate value distribution of the tomographic data refers to the tomographic data obtained by smoothing the tomographic data or transforming the tomographic data by a morphology operation described later, and is, for example, the approximate parameters shown in FIGS. 13 (c) and 13 (d). Equivalent to.

First, in step S1210, when the acquisition unit 310 acquires the three-dimensional volume data, the process proceeds to step S1220. In step S1220, the target acquisition unit 323 calculates the correction coefficient H (z) in the depth direction for roll-off correction by the above-mentioned method in order to compensate for the signal attenuation due to the roll-off characteristic of the OCT device 1.

Next, in step S1230, the target acquisition unit 323 smoothes the projected image of the front of the fundus based on the three-dimensional volume data in the two-dimensional direction, and calculates the first approximate parameter. Specifically, the target acquisition unit 323 projects the three-dimensional volume data Ih (x, y, z) after applying the roll-off correction in the depth direction, and the fundus as shown in FIG. 13 (b). The OCT projection image Ph corresponding to the front surface is generated. The target acquisition unit 323 smoothes each pixel of the generated OCT projection image Ph in the two-dimensional direction, thereby smoothing the first schematic parameter S _{2d of the tomographic data parameter as shown in FIG. 13 (c).} (X, y) is calculated.

In this embodiment, as the projection process of the three-dimensional volume data, the average value of the tomographic data in the depth direction corresponding to each pixel in the plane corresponding to the front of the fundus is used as the pixel value of the pixel. However, the projection process is not limited to such average value projection, and any known projection method may be used. For example, the median value, the maximum value, the mode value, and the like of the tomographic data in the depth direction corresponding to each pixel may be used as the pixel value. Further, by projecting only the brightness value of the pixel exceeding the noise threshold corresponding to the background brightness value, the pixel value excluding the influence of the background region (derived from the brightness value of the fundus tissue) can be obtained. Further, in this embodiment, the smoothing process is performed as an example of the process of calculating the approximate value distribution, but a morphology operation such as a Closing process or an Opening process may be performed as described later. Further, the smoothing process may be smoothed by using an arbitrary spatial filter, or may be smoothed by frequency-converting the tomographic data using a fast Fourier transform (FFT) or the like and then suppressing a high-frequency component. When FFT is used, the convolution operation is not required, so that the smoothing process can be executed at high speed.

In step S1240, the target acquisition unit 323 performs a process (smoothing process or morphology calculation) for calculating an approximate value distribution regarding the main scanning direction (one-dimensional direction) of the measurement light for each pixel of the OCT projected image Ph. The approximate parameter S _1d (x, y) of 2 is calculated. Then, in step S1250, the target acquisition unit 323 _{divides the first schematic parameter S 2d} (x, y) by the second schematic parameter S _1d (x, y) according to Equation 13 (FIG. 13). The correction coefficient C (x, y) as shown in c) is calculated.

14 (a), (b), and (c) are shown on the white dotted lines 1321, 1322, 1323 drawn in the sub-scanning direction (vertical direction) in FIGS. 13 (c), (d), and (e), respectively. The profile of the parameters of the fault data along is shown. In FIGS. 14 (a) to 14 (c), the profile of the position corresponding to the low-luminance region 1312 of the OCT projection image Ph is indicated by the circles 1401, 1402, 1403. _{In the above processing, the target acquisition unit 323 has a profile of the first schematic parameter S 2d} (x, y) in which the profile of the position corresponding to the low-luminance region 1312 of the OCT projection image Ph surrounded by the circle 1402 is surrounded by the circle 1401. The correction coefficient C (x, y) is determined so as to approach. The circle 1403 shows the profile of the correction coefficient C (x, y) at the position corresponding to the low-luminance region 1312.

In step S1260, the data correction unit 324 uses the correction coefficient H (z) in the depth direction determined in step S1220 and the correction coefficient C (x, y) in the in-plane direction calculated in step S1250 to set the parameters of the tomographic data. to correct. Specifically, assuming that the original three-dimensional volume data (tomographic data) is Io (x, y, z) and the corrected tomographic data is Ic (x, y, z), the data correction unit 324 is set to Equation 14 Correct the fault data parameters according to.

Since the subsequent processing is the same as that of the third embodiment, the description thereof will be omitted. According to the OCTA image generation process according to the present embodiment, it is possible to generate an OCTA image which is a motion contrast image in which the occurrence of light and darkness due to the strength of the tomographic data as shown in FIG. 13 (f) is suppressed.

As described above, the control unit 300 according to the present embodiment includes an acquisition unit 310, a target acquisition unit 323, a data correction unit 324, and an image generation unit 320. The acquisition unit 310 acquires a plurality of tomographic data indicating tomographic information of the fundus obtained based on the measurement light controlled to scan the same position of the fundus of the eye E to be inspected. The target acquisition unit 323 calculates the first approximate parameter obtained by smoothing the parameter used for calculating the motion contrast in the tomographic data in the first dimension or converting it by a morphology operation. In addition, the target acquisition unit 323 calculates a second approximate parameter obtained by smoothing or transforming a parameter in the tomographic data in a second dimension lower than the first dimension by a morphology operation. After that, the target acquisition unit 323 acquires the correction coefficient by calculating the first schematic parameter and the second schematic parameter. In this embodiment, the brightness of the tomographic data is used as the parameter. The data correction unit 324 corrects the parameter of at least one tomographic data among the plurality of tomographic data by using the correction coefficient. The image generation unit 320 generates a motion contrast image based on the motion contrast calculated using the plurality of tomographic data including the at least one tomographic data corrected by the data correction unit 324.

More specifically, the target acquisition unit 323 calculates the first approximate parameter by smoothing the parameter in the tomographic data corresponding to the front surface of the fundus in the two-dimensional direction or converting it by a morphology calculation. Further, the target acquisition unit 323 calculates the second approximate parameter by converting the parameter in the tomographic data corresponding to the front surface of the fundus into the main scanning direction (one-dimensional direction) of the measurement light by smoothing or morphology calculation. After that, the target acquisition unit 323 acquires the correction coefficient by dividing the first schematic parameter by the second schematic parameter.

Here, if the parameters in the tomographic data are smoothed or morphologically calculated, it can be converted into a parameter in which the difference between the values of the parameters of the adjacent tomographic data is reduced. Therefore, the target acquisition unit 323 calculates the first post-conversion parameter obtained by converting the parameter in the tomographic data so as to reduce the difference between the values of the adjacent parameters in the first direction and the second direction according to the above configuration. be able to. In addition, the target acquisition unit 323 can calculate the second post-conversion parameter obtained by converting the parameter in the tomographic data so that the difference between the values of the adjacent parameters is reduced in the first direction. After that, the target acquisition unit 323 can acquire the correction coefficient by dividing the first post-conversion parameter obtained as the first approximate parameter by the second post-conversion parameter obtained as the second approximate parameter. can.

According to the control unit 300 according to the present embodiment, the parameters in the tomographic data, which is the original data for generating the motion contrast, are corrected based on the approximate value distribution of the parameters. Therefore, the strength of each parameter can be suppressed. Therefore, it is possible to generate an OCTA image which is a motion contrast image in which the occurrence of light and darkness due to the strength of the tomographic data is suppressed.

Further, in this embodiment, the target acquisition unit 323 acquires the correction coefficient using the tomographic data after the roll-off correction processing that compensates for the signal attenuation in the depth direction based on the roll-off characteristic of the photographing optical system 200 that photographs the fundus. do. Therefore, it is possible to generate an OCTA image which is a motion contrast image in which the influence of signal attenuation in the depth direction based on the roll-off characteristic of the photographing optical system 200 is also reduced.

In this embodiment, the calculation of the correction coefficient H (z) in the depth direction is stopped in step S1220, and the original three-dimensional volume data Io (x, y, z) is corrected according to Equation 14 in step S1260. The case of doing so was explained. However, the timing of roll-off correction is not limited to this. For example, the three-dimensional volume data Ih (x, y, z) subjected to roll-off correction according to the equation 12 is generated in step S1220, and the three-dimensional volume data Ih (x, y, z) is generated in step S1250. The correction process according to the equation 15 may be performed.

Further, in this embodiment, the roll-off correction is performed together with the correction for reducing the low-luminance region, but the roll-off correction may not be performed. In this case, step S1220 is omitted, and in step S1260, the correction coefficient H (z) in the depth direction is omitted. Further, in step S1230 and step S1240, the first schematic parameter and the second schematic parameter are calculated based on the three-dimensional volume data that has not been roll-off corrected.

Further, in the present embodiment, the OCT projection image Ph is generated and the correction coefficient C (x, y) is generated as a two-dimensional coefficient, but the present invention is not limited to this. For example, the approximate value distribution of the parameters of the tomographic data obtained by smoothing the three-dimensional volume data in three dimensions may be set as the first approximate parameter. In this case, it is obtained by smoothing the tomographic data in the two-dimensional directions of the x-axis direction and the depth (z-axis) direction so that the correction effect on the brightness (brightness difference in the y-axis direction) of the brightness to be corrected is high. The approximate value distribution of the parameters of the tomographic data is set as the second approximate parameter. Then, a three-dimensional correction coefficient may be generated based on the value obtained by dividing the first schematic parameter by the second schematic parameter to correct the tomographic data.

In this example, the smoothing process was performed as an example of the process of calculating the approximate value distribution, but the process of calculating the approximate value distribution is not limited to this. The approximate value distribution calculation process in the present specification includes, for example, any known approximate value distribution calculation process such as a smoothing process using an FFT and a process using a morphology operation. For example, instead of the smoothing process, a Closing process (a process of executing the minimum value operation after the maximum value operation), which is a kind of morphology operation, may be performed, and the parameters after the morphology process may be set as approximate parameters. For example, when the width of the low-luminance region is wide, it is possible to prevent the approximate parameter from being set excessively low by setting the approximate parameter based on the Closing process. Further, as the process of calculating the approximate value distribution, a smoothing process, a morphology operation, or the like may be combined and executed.

Further, in this embodiment, the brightness and darkness that occur only in the sub-scanning direction has been described as an example of the variation pattern of the parameter to be corrected, but the variation pattern of the parameter that can be corrected is not limited to this. If the fluctuation pattern shows a smaller local change with respect to the first schematic parameter, the fluctuation pattern occurring at an arbitrary position or direction can be corrected. In this case, the variation pattern of the parameter of an arbitrary position or direction is corrected by changing the position or direction in which the smoothing or morphology calculation is performed when calculating the second approximate parameter according to the variation pattern to be corrected. can do.

_{Further, in this embodiment, the first schematic parameter S 2d} (x, y) is _{divided by the second schematic parameter S 1d} (x, y) as an operation for obtaining the correction coefficient C (x, y). However, the operation is not limited to division, and any operation may be performed. For example, difference processing may be performed as arithmetic processing.

In this embodiment, the configuration for correcting the tomographic data for the entire three-dimensional volume data has been described, but the correction process is not limited to this. For example, the correction process may be performed only on the tomographic data included in the generation range of the OCTA image which is the motion contrast image to be generated. In this case, only the tomographic data included in the OCTA image generation range may be used for the calculation of the correction coefficient. Therefore, when generating the OCTA projection image, only the tomographic data included in the generation range of the OCTA image can be used. In such a case, the number of data used for the calculation is reduced, so that the amount of calculation can be reduced.

(Example 5)
In Example 4, a case where the parameter of the tomographic data is corrected by using the correction coefficient of the tomographic data calculated by using two kinds of schematic parameters of the three-dimensional volume data has been described. On the other hand, in Example 5, a motion contrast image in which the occurrence of light and darkness due to the strength of the tomographic data is suppressed by correcting the threshold value applied to the tomographic data at the time of calculating the motion contrast by using the same correction coefficient. Will be described in the case of generating. Specifically, after calculating the correction coefficient C (x, y) as in the fourth embodiment, the noise threshold Dth is corrected by dividing the noise threshold Dth used when calculating the motion contrast by the correction coefficient C (x, y). do. After that, the motion contrast is calculated using the obtained new noise threshold value, and a motion contrast image is generated.

Hereinafter, the control unit according to the present embodiment will be described with reference to FIG. 15, focusing on the difference from the control unit 300 according to the fourth embodiment. FIG. 15 is a flowchart of the OCTA image generation process according to this embodiment. In the flowchart shown in FIG. 15, steps other than steps S1560 and S1570 are the same as the steps of the OCTA image generation process according to the fourth embodiment, and thus the description thereof will be omitted.

補正されたノイズ閾値Ｄｔｈｃは、断層データのパラメータが小さいほど、すなわち、補正係数Ｃ（ｘ，ｙ）が大きいほど、及び深度位置ｚが大きいほど小さくなる。 In this embodiment, when the correction coefficient C (x, y) is calculated in step S1550, the process proceeds to step S1560. In step S1560, the motion contrast generation unit 325 uses the correction coefficient H (z) for roll-off correction and the correction coefficient C (x, y) in the in-plane direction to generate noise for noise mask processing used when calculating the motion contrast. Correct the threshold Dth. Specifically, the motion contrast generation unit 325 corrects the noise threshold value Dth according to the equation 16 and calculates the noise threshold value Dthc.

The corrected noise threshold Dthc becomes smaller as the parameter of the tomographic data is smaller, that is, as the correction coefficient C (x, y) is larger and the depth position z is larger.

In step S1560, the motion contrast generation unit 325 performs noise mask processing on the three-dimensional volume data Io (tomographic data) using the noise threshold Dthc. After that, the motion contrast generation unit 325 calculates the motion contrast using the three-dimensional volume data subjected to the noise mask processing, and generates the motion contrast data.

Subsequent processing is the same as in Example 4, and thus is omitted. The OCTA image generation process according to the present embodiment can also generate an OCTA image which is a motion contrast image in which the occurrence of light and darkness due to the strength of the tomographic data is suppressed.

As described above, the control unit 300 according to the present embodiment includes an acquisition unit 310, a target acquisition unit 323, a data correction unit 324, and an image generation unit 320. The acquisition unit 310 acquires a plurality of tomographic data indicating tomographic information of the fundus obtained based on the measurement light controlled to scan the same position of the fundus of the eye E to be inspected. The target acquisition unit 323 calculates the first approximate parameter obtained by smoothing the parameter used for calculating the motion contrast in the tomographic data in the first dimension or converting it by a morphology operation. In addition, the target acquisition unit 323 calculates a second approximate parameter obtained by smoothing or transforming a parameter in the tomographic data in a second dimension lower than the first dimension by a morphology operation. After that, the target acquisition unit 323 acquires the correction coefficient by calculating the first schematic parameter and the second schematic parameter. In this embodiment, the brightness of the tomographic data is used as the parameter. The data correction unit 324 corrects the threshold value of the threshold processing applied to the parameters of the tomographic data by using the correction coefficient. The image generation unit 320 is a motion contrast image based on the motion contrast calculated using the parameters of at least one tomographic data among the plurality of tomographic data, which has been threshold-processed using the threshold value corrected by the data correction unit 324. To generate.

According to the control unit 300 according to the present embodiment, the noise threshold value used in the calculation of the motion contrast is corrected based on the approximate parameters obtained by smoothing or morphologically calculating the tomographic data, so that the threshold value is processed up to a significant signal. That can be reduced. Therefore, it is possible to generate an OCTA image which is a motion contrast image in which the occurrence of light and darkness due to the strength of the tomographic data is suppressed.

In this embodiment, in step S1520, the calculation of the correction coefficient H (z) in the depth direction is stopped, and the noise threshold Dth applied to the three-dimensional volume data Io (x, y, z) in step S1560 is expressed by Equation 16. The case of correction according to is described. However, the target of the roll-off correction is not limited to the noise threshold Dth, and may be applied to the three-dimensional volume data Io (x, y, z). For example, the three-dimensional volume data Ih (x, y, z) for which roll-off correction is performed using the formula 12 in step S1520 is generated, and the noise threshold Dth is corrected according to the following formula 17 in step S1560. Then, in step S1570, the corrected noise threshold Dthc'may be applied to the three-dimensional volume data Ih (x, y, z) after the roll-off correction. The noise threshold Dthc'is smaller as the parameter of the tomographic data is smaller, that is, as the correction coefficient C (x, y) is larger.

Further, in this embodiment, the roll-off correction is performed together with the correction for reducing the low-luminance region, but the roll-off correction may not be performed. In this case, step S1520 is omitted, and in step S1560, the correction coefficient H (z) in the depth direction is omitted. Further, in step S1530 and step S1540, the first schematic parameter and the second schematic parameter are calculated based on the three-dimensional volume data that has not been roll-off corrected.

In this embodiment, the configuration for calculating the correction coefficient using the entire three-dimensional volume data has been described, but the calculation method for the correction coefficient is not limited to this. For example, the correction coefficient may be calculated using only the tomographic data included in the generation range of the OCTA image, which is the motion contrast image to be generated. In this case, only the tomographic data included in the generation range of the OCTA image can be used when generating the OCT projection image. In such a case, the number of data used for the calculation is reduced, so that the amount of calculation can be reduced.

Further, the correction of the data in the above embodiment can be performed for each of the three-dimensional volume data, the B scan data, the A scan data, or a part thereof, depending on the desired configuration. Further, for the calculation of the correction coefficient, for example, different correction coefficients may be obtained for a plurality of three-dimensional volume data, or a common correction coefficient may be obtained.

In Examples 1 to 5, the brightness (power) of the complex number data after FFT included in the three-dimensional volume data is used as a parameter of the tomographic data for generating the motion contrast data. However, the motion contrast generation unit 325 uses the phase information of the complex number data after the FFT, the information of both the brightness and the phase, the information of the real part and the imaginary part, and the like as the parameters based on an arbitrary known method, and the motion contrast. Data may be generated. In these cases, the data correction unit 324 corrects the distribution of information such as phase information used when generating motion contrast data, the information itself, or the noise threshold value. After that, the motion contrast generation unit 325 generates motion contrast data based on the corrected information and the noise threshold value. As a result, the same effect as that of the above embodiment can be obtained.

Further, the data correction unit 324 may correct the distribution of tomographic data, tomographic data, or noise threshold value before wave number conversion, before FFT, or before absolute value conversion. Even in this case, the motion contrast generation unit 325 can generate the motion contrast data based on the brightness and the like of the corrected tomographic data and the noise threshold value, so that the same effect as that of the above embodiment can be obtained. In these cases, the target acquisition unit 323 acquires a target according to the object to be corrected.

Further, in the above embodiment, when the motion contrast data is generated, the motion contrast data is generated by obtaining the decorrelation value Mxy of the two three-dimensional volume data. However, the method of generating motion contrast data is not limited to this. For example, the motion contrast generation unit 325 can generate motion contrast data by any known method such as a method of obtaining a difference or ratio between two three-dimensional volume data. The formula for obtaining the decorrelation value Mxy is not limited to the above formula 1, and may be any known formula.

Further, in the above embodiment, the acquisition unit 310 acquires the interference signal acquired by the photographing optical system 200, the tomographic data generated by the image generation unit 320, and the three-dimensional volume data generated by the reconstruction unit 321. However, the configuration in which the acquisition unit 310 acquires these signals is not limited to this. For example, the acquisition unit 310 may acquire these signals from a server or an imaging device connected to the control unit via a LAN, WAN, the Internet, or the like.

The eye to be inspected is taken as an example of the object to be inspected, but the object to be inspected is not limited to this. For example, the object to be inspected may be the skin or organ of the subject. At this time, the OCT apparatus according to the above embodiment can be applied to medical equipment such as an endoscope in addition to the ophthalmic apparatus.

Further, in the above embodiment, the fiber optical system using a coupler is used as the dividing means, but a spatial optical system using a collimator and a beam splitter may be used. Further, the configuration of the photographing optical system 200 is not limited to the above configuration, and a part of the configuration included in the photographing optical system 200 may be a configuration separate from the photographing optical system 200.

Further, in the above embodiment, the configuration of the Michelson interferometer is used as the interference optical system of the OCT apparatus, but the configuration of the interference optical system is not limited to this. For example, the interferometric optical system of the OCT apparatus 1 may have the configuration of a Mach-Zehnder interferometer.

Further, in the above-described embodiment, the spectral domain OCT (SD-OCT) device using the SLD as a light source has been described as the OCT device, but the configuration of the OCT device according to the present invention is not limited to this. For example, the present invention can be applied to any other type of OCT device such as a wavelength sweep type OCT (SS-OCT) device using a wavelength sweep light source capable of sweeping the wavelength of emitted light.

(Other Examples)
The present invention supplies a program that realizes one or more functions of the above-described embodiment to a system or device via a network or storage medium, and one or more processors in the computer of the system or device reads and executes the program. It can also be realized by the processing to be performed. It can also be realized by a circuit (for example, ASIC) that realizes one or more functions.

Although the present invention has been described above with reference to the examples, the present invention is not limited to the above examples. The present invention also includes inventions modified to the extent not contrary to the gist of the present invention, and inventions equivalent to the present invention. In addition, the above-mentioned Examples and Modifications can be appropriately combined as long as they do not contradict the gist of the present invention.

300: Control unit (information processing device), 310: Acquisition unit (first acquisition means), 320: Image generation unit (generation means), 323: Target acquisition unit (second acquisition means), 324: Data correction unit (correction) means)

Claims (14)

A first acquisition means for acquiring a plurality of tomographic data indicating information on the tomography of the fundus obtained based on the measurement light controlled to scan the same position of the fundus.
The first schematic parameter obtained by converting the parameters used for calculating the motion contrast in the tomographic data in the first dimension and the parameter in the tomographic data converted in the second dimension lower than the first dimension. The second acquisition means for acquiring the correction coefficient by the calculation with the second approximate parameter,
And correcting means for correcting at least one of the tomographic data of the plurality of the tomographic data by using the correction coefficient,
Based on the auxiliary Tadashisa the said at least one of said plurality of motion contrast calculated using the fault data including the tomographic data, and generating means for generating a motion contrast image,
Information processing device. A first acquisition means for acquiring a plurality of tomographic data indicating information on the tomography of the fundus obtained based on the measurement light controlled to scan the same position of the fundus.
The first schematic parameter obtained by converting the parameters used for calculating the motion contrast in the tomographic data in the first dimension and the parameter in the tomographic data converted in the second dimension lower than the first dimension. The second acquisition means for acquiring the correction coefficient by the calculation with the second approximate parameter,
Using the correction coefficient, a correction means for correcting the threshold value of the threshold processing applied to the parameters of the tomographic data, and
A generation means for generating a motion contrast image based on a motion contrast calculated by using a parameter of at least one tomographic data among a plurality of the tomographic data, which has been threshold-processed using the corrected threshold value.
Information processing device. A first obtaining unit that acquire a tomographic data indicative of the fault information of the fundus obtained on the basis of the controlled measuring light to scan the eye bottom,
Said first and summary parameters of Rupa parameters put on the tomographic data converted in the first dimension, second schematic parameters the parameters in the tomographic data converted in a second dimension lower than the first dimension the operation of, and the second obtaining means for obtaining a correction factor,
And correction means for correcting the previous Symbol fault data,
Generating means for generating the images using tomographic data pre SL correction,
Information processing device. A first acquisition means for acquiring a plurality of tomographic data indicating information on the tomography of the fundus obtained based on the measurement light controlled to scan the same position of the fundus.
The correction coefficient is calculated by calculating the first approximate parameter obtained by converting the parameter related to motion contrast in the first dimension and the second approximate parameter obtained by converting the parameter in the second dimension lower than the first dimension. The second acquisition means to acquire and
A correction means for correcting the tomographic data and
A generation means for generating an image using the corrected tomographic data, and
Information processing device. The second acquisition means is
The parameter in the tomographic data corresponding to the front surface of the fundus is converted in the two-dimensional direction to calculate the first schematic parameter.
The information processing apparatus according to any one of claims 1 to 4, wherein the parameter in the tomographic data corresponding to the front surface of the fundus is converted in a one-dimensional direction to calculate the second schematic parameter. The information processing apparatus according to claim 5 , wherein the one-dimensional direction is the axial direction of the main scan of the measurement light. The information processing apparatus according to any one of claims 1 to 6 , wherein the calculation is a calculation of dividing the first schematic parameter by the second schematic parameter. The second acquisition means acquires the correction coefficient by using the tomographic data after the roll-off correction processing that compensates for the signal attenuation in the depth direction based on the roll-off characteristic of the photographing apparatus for photographing the fundus. The information processing apparatus according to any one of 7 to 7. The information processing apparatus according to any one of claims 1 to 8, wherein the conversion is a smoothing or morphology operation. Acquiring a plurality of tomographic data showing the information of the tomography of the fundus obtained based on the measurement light controlled to scan the same position of the fundus, and
The first schematic parameter obtained by converting the parameters used for calculating the motion contrast in the tomographic data in the first dimension and the parameter in the tomographic data converted in the second dimension lower than the first dimension. Obtaining the correction coefficient by calculation with the second approximate parameter
And correcting at least one of the tomographic data of the plurality of the tomographic data by using the correction coefficient,
To generate a motion contrast image based on the motion contrast calculated using the plurality of tomographic data including the corrected at least one tomographic data.
Information processing methods, including. Acquiring a plurality of tomographic data showing the information of the tomography of the fundus obtained based on the measurement light controlled to scan the same position of the fundus, and
The first schematic parameter obtained by converting the parameters used for calculating the motion contrast in the tomographic data in the first dimension and the parameter in the tomographic data converted in the second dimension lower than the first dimension. Obtaining the correction coefficient by calculation with the second approximate parameter
Using the correction coefficient, the threshold of the threshold processing applied to the parameter of the tomographic data is corrected, and
To generate a motion contrast image based on the motion contrast calculated using the parameters of at least one tomographic data among the plurality of the tomographic data, which has been threshold-processed using the corrected threshold value.
Information processing methods, including. And to get the tomographic data indicative of the fault information of the fundus obtained on the basis of the controlled measuring light to scan the eye bottom,
Calculation of the first schematic parameter obtained by converting the parameter in the fault data in the first dimension and the second schematic parameter obtained by converting the parameter in the fault data in a second dimension lower than the first dimension. Accordingly, the obtaining the correction factor,
Correcting the fault data and
To generate an image using the corrected tomographic data,
Information processing methods , including. Acquiring a plurality of tomographic data showing the information of the tomography of the fundus obtained based on the measurement light controlled to scan the same position of the fundus, and
The correction coefficient is calculated by calculating the first approximate parameter obtained by converting the parameter related to motion contrast in the first dimension and the second approximate parameter obtained by converting the parameter in the second dimension lower than the first dimension. To get and
Correcting the fault data and
To generate an image using the corrected tomographic data,
Information processing methods, including. A program that, when executed by a processor, causes the processor to perform each step of the information processing method according to any one of claims 10 to 13.

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