JP2020099627A - Medical image processing device, medical image processing method, and program - Google Patents

Abstract

To combine luminance uniformity and sharpness in a captured image with noise uniformity in the image.SOLUTION: A medical image processing device of an embodiment comprises a separation unit and a luminance correction unit. The separation unit separates a magnetic resonance signal collected from an analyte into a first component and a second component. The luminance collection unit applies mutually different luminance correction on each of a first image based on the first component and a second image based on the second component separated by the separation unit.SELECTED DRAWING: Figure 1

Description

Embodiments of the present invention relate to a medical image processing apparatus, a medical image processing method, and a program.

BACKGROUND ART Conventionally, in medical devices used for image diagnosis and the like, various studies have been conducted for the purpose of improving image quality and shortening imaging time. For example, in an MRI (Magnetic Resonance Imaging) apparatus, there is a technique for obtaining a high-quality image in a short time by forming a multi-channel by using a plurality of receiving coils and reconstructing a signal obtained from the plurality of receiving coils. Are known. When a plurality of receiving coils are used in this way, the brightness of the obtained image may be non-uniform due to the sensitivity difference between the coils.

In the conventional method, brightness correction is performed to optimize the brightness of the image, but as a result of increasing the brightness of the part with low coil sensitivity (that is, the dark part), the noise components included in that part are also amplified. As a result, the noise in the imaging cross section becomes non-uniform, and the image quality may deteriorate. In addition, the image sharpness may be lowered in a portion having a high coil sensitivity. Further, when parallel imaging is performed in the MRI apparatus, noise tends to be generated in a portion where the independence between receiving coils is low, and when luminance correction is performed on this portion, the noise may be amplified.

US Pat. No. 7,978,896

The problem to be solved by the present invention is to satisfy both the uniformity of brightness and the uniformity of sharpness and noise in a captured image.

The medical image processing apparatus according to the embodiment includes a separation unit and a brightness correction unit. The separation unit separates the magnetic resonance signal collected from the subject into a first component and a second component. The brightness correction unit performs different brightness correction on each of the first image based on the first component and the second image based on the second component separated by the separation unit.

The figure which shows an example of the medical image generation apparatus 100 which concerns on 1st Embodiment. The figure which shows an example of arrangement|positioning of the RF coil 108 with which the medical image generation apparatus 100 which concerns on 1st Embodiment is equipped. FIG. 6 is a diagram showing an example of a state of an MR image P on which brightness correction has not been performed according to the first embodiment. 6 is a flowchart showing a flow of a series of processes of the processing circuit 130 according to the first embodiment. The figure which shows an example of the mode of the g-factor in MR image P which concerns on the modification of 1st Embodiment. The figure which shows an example of the medical image generation apparatus 200 which concerns on 2nd Embodiment. 6 is a flowchart showing a flow of a series of processes of the processing circuit 130 according to the second embodiment.

Hereinafter, a medical image processing apparatus, a medical image processing method, and a program according to embodiments will be described with reference to the drawings.

(First embodiment)
FIG. 1 is a diagram showing an example of a medical image generation apparatus 100 according to the first embodiment. The medical image generation apparatus 100 (medical apparatus) includes, for example, an MRI apparatus and the like. The MRI apparatus, for example, applies a magnetic field to a subject (for example, a human body), receives an electromagnetic wave generated from hydrogen atomic nuclei in the subject by a magnetic resonance phenomenon by a coil, and reconstructs a signal based on the received electromagnetic wave. As a result, a medical image (MR image) is generated. In the following description, as an example, the medical image generating apparatus 100 will be described as an MRI apparatus.

[Example of Configuration of Medical Image Generating Device (MRI Device)]
As shown in FIG. 1, the medical image generating apparatus 100 includes, for example, a static magnetic field magnet 101, a gradient magnetic field coil 102, a gradient magnetic field power source 103, a bed 104, a bed control circuit 105, an RF coil 108, and a transmitter. The circuit 107, the receiving circuit 109, the sequence control circuit 110, and the console device 120 are provided.

The static magnetic field magnet 101 is a magnet formed in a hollow and substantially cylindrical shape. The static magnetic field magnet 101 generates a uniform static magnetic field in the internal space. The static magnetic field magnet 101 is, for example, a permanent magnet or a superconducting magnet. The gradient magnetic field coil 102 is a coil formed in a hollow and substantially cylindrical shape, and is arranged inside the static magnetic field magnet 101. The gradient magnetic field coil 102 is formed by combining three coils corresponding to x, y, and z axes that are orthogonal to each other. The z-axis direction represents the longitudinal direction of the top plate 104a of the bed 104, and the x-axis direction is orthogonal to the z-axis direction and parallel to the floor of the room where the medical image generating apparatus 100 is installed. And the y-axis direction represents the axial direction that is the direction perpendicular to the floor surface. The three coils corresponding to the respective axial directions individually receive currents from the gradient magnetic field power source 103 to generate gradient magnetic fields whose magnetic field strength changes along the x, y, and z axes. The z-axis direction is the same as the static magnetic field.

The gradient magnetic field power supply 103 supplies a current to the gradient magnetic field coil 102. Here, the gradient magnetic fields of the x, y, and z axes generated by the gradient magnetic field coil 102 respectively correspond to, for example, the slice selection gradient magnetic field Gs, the phase encoding gradient magnetic field Ge, and the readout gradient magnetic field Gr. .. The slice selection gradient magnetic field Gs is used to arbitrarily determine an imaging cross section. The phase encoding gradient magnetic field Ge is used to change the phase of the magnetic resonance signal according to the spatial position. The readout gradient magnetic field Gr is used to change the frequency of the magnetic resonance signal according to the spatial position.

The bed 104 includes a top plate 104a on which the subject OB is placed. Under the control of the bed control circuit 105, the bed 104 inserts the top plate 104a into the cavity (imaging port) of the gradient magnetic field coil 102 with the subject OB placed thereon. Usually, the bed 104 is installed so that the longitudinal direction thereof is parallel to the central axis of the static magnetic field magnet 101. Under the control of the console device 120, the bed control circuit 105 drives the bed 104 to move the table 104a in the longitudinal direction and the vertical direction.

The RF coil 108 is supplied with an RF (Radio Frequency) pulse from the transmission circuit 107 and generates a high frequency magnetic field. The transmission circuit 107 supplies the RF coil 108 with an RF pulse corresponding to the Larmor frequency determined by the type of target nucleus and the strength of the magnetic field. Further, the RF coil 108 receives a magnetic resonance signal emitted from the subject OB under the influence of the high frequency magnetic field. Upon receiving the magnetic resonance signal, the RF coil 108 outputs the received magnetic resonance signal to the receiving circuit 109.

The RF coil 108 is, for example, a coil for the whole body that is housed in a pedestal of the MRI apparatus and configured to surround the subject OB, or a local coil that is provided for each imaging region of the subject OB. Hereinafter, a local coil will be described as an example of the RF coil 108, but it is not intended to limit the type of the RF coil 108. Further, the transmission and the reception may be performed by different RF coils, and the RF coil 108 may be configured for both transmission and reception. In the first embodiment, the RF coil 108 is a coil array having a plurality of coil elements.

FIG. 2 is a diagram showing an example of the arrangement of the RF coil 108 provided in the medical image generation apparatus 100 according to the first embodiment. FIG. 2 shows an example in which the RF coil 108 includes eight coil elements 108a to 108h. These coil elements 108a to 108h are arranged so as to surround the subject OB. Each of the coil elements 108 a to 108 h receives the magnetic resonance signal emitted from the subject OB and outputs it to the receiving circuit 109.

The reception circuit 109 detects the magnetic resonance signals output by the coil elements 108a to 108h, and generates magnetic resonance data based on the detected magnetic resonance signals. For example, the reception circuit 109 generates magnetic resonance data by digitally converting the magnetic resonance signal, and transmits the magnetic resonance data to the sequence control circuit 110. The receiving circuit 109 may be provided on the gantry device side including the static magnetic field magnet 101, the gradient magnetic field coil 102, and the like.

The sequence control circuit 110 drives the gradient magnetic field power supply 103, the transmission circuit 107, and the reception circuit 109 based on the sequence information output from the console device 120, and thereby images the subject OB. The sequence information is information that defines the procedure for performing the imaging process. The sequence information includes the magnitude of the current supplied by the gradient magnetic field power supply 103 to the gradient magnetic field coil 102 and the timing of supplying the current, the strength of the RF pulse transmitted by the transmission circuit 107 to the RF coil 108, the timing of applying the RF pulse, and the reception. It includes information that defines the timing at which the circuit 109 detects the magnetic resonance signal.

The sequence control circuit 110 drives the gradient magnetic field power supply 103, the transmission circuit 107, and the reception circuit 109, and when receiving a magnetic resonance signal from the reception circuit 109, transfers the received magnetic resonance signal to the console device 120.

The console device 120 controls the entire medical image generating apparatus 100 and collects magnetic resonance signals. The console device 120 includes, for example, an input interface 122, a display 124, a processing circuit 130, and a memory (storage) 150.

The input interface 122 receives various input operations from the operator, converts the received input operations into electric signals, and outputs the electric signals to the processing circuit 130. For example, the input interface 122 is realized by a mouse, keyboard, trackball, switch, button, joystick, touch panel, or the like. The input interface 122 may be realized by, for example, a user interface such as a microphone that receives a voice input. When the input interface 122 is a touch panel, the display 124 described below may be formed integrally with the input interface 122.

The display 124 displays various information. For example, the display 124 displays an image generated by the processing circuit 130, a GUI (Graphical User Interface) for receiving various input operations from an operator, and the like. For example, the display 124 is an LCD (Liquid Crystal Display), a CRT (Cathode Ray Tube) display, an organic EL (Electroluminescence) display, or the like.

The processing circuit 130 includes, for example, an acquisition function 132, a signal separation function 134 (separation unit), a reconstruction processing function 136, a luminance correction function 138 (luminance correction unit), and an image synthesis function 140 (image synthesis unit). And an output control function 142. The processing circuit 130 realizes these functions by, for example, a hardware processor included in the computer executing a program stored in the memory 150 that is a storage device (storage circuit).

The hardware processor is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an application specific integrated circuit (ASIC), a programmable logic device (for example, a simple programmable logic device (Simple Programmable Logic). Device: SPLD) or a complex programmable logic device (CPLD), field programmable gate array (Field Programmable Gate Array; FPGA), etc. Instead of storing the program in the memory 150, the program may be directly incorporated in the circuit of the hardware processor. In this case, the hardware processor realizes the function by reading and executing the program incorporated in the circuit. The hardware processor is not limited to the one configured as a single circuit, and may be configured as one hardware processor by combining a plurality of independent circuits to realize each function. Also, a plurality of constituent elements may be integrated into one hardware processor to realize each function.

The memory 150 stores, for example, a sensitivity map SM and an MR image of the subject OB (hereinafter, also referred to as MR image P). The sensitivity map SM indicates a spatial reception sensitivity distribution of each coil element 108a to 108h. The sensitivity map SM is created, for example, based on the magnetic resonance signal obtained by the pre-scan performed prior to the execution of the imaging sequence for the subject OB. The sensitivity map SM is used when the brightness of an image is corrected. The sensitivity map SM may be created during the imaging sequence or after execution.

The memory 150 is realized by, for example, a RAM (Random Access Memory), a semiconductor memory element such as a flash memory, a hard disk, an optical disk, or the like. These non-transitory storage media may be realized by other storage devices such as NAS (Network Attached Storage) and external storage server devices connected via the network NW. Further, the memory 150 may include a transitory storage medium such as a ROM (Read Only Memory) or a register.

The acquisition function 132 acquires magnetic resonance data from the sequence control circuit 110. As described above, the magnetic resonance data is generated in the object OB by the nuclear magnetic resonance phenomenon, and the signals of the electromagnetic waves (nuclear magnetic resonance signals) received by the coil elements 108a to 108h are digitized by the receiving circuit 109. It is a thing.

The signal separation function 134 separates the magnetic resonance signal acquired by the acquisition function 132 based on the magnitude of the frequency. The magnetic resonance signal includes a high frequency component and a low frequency component. The high-frequency component mainly includes a signal indicating the outer shape of the object OB as an imaging target, the contour of an object such as an organ located in the imaging cross section of the object OB, and a single shadow. Further, the low frequency component mainly includes a signal indicating the contrast of the image. The signal separation function 134 separates the magnetic resonance signal acquired by the acquisition function 132 into a high frequency component and a low frequency component.

For example, the signal separation function 134 separates a high frequency component and a low frequency component by performing filtering or the like on the magnetic resonance data acquired by the acquisition function 132. For example, the signal separation function 134 separates a frequency component equal to or higher than a predetermined threshold as a high frequency component and a frequency component less than the threshold as a low frequency component. The threshold value may be changed depending on the amount of noise included in the image, the imaged region of the subject OB, or the like, or may be received from the operator.

The reconstruction processing function 136 uses the magnetic resonance data acquired by the acquisition function 132 according to the information given by the slice selection gradient magnetic field Gs, the phase encoding gradient magnetic field Ge, and the readout gradient magnetic field Gr described above. For example, they are arranged two-dimensionally or three-dimensionally. The arranged magnetic resonance data is referred to as k-space data, and the reconstruction processing function 136 generates image data by performing reconstruction processing using Fourier transform or the like on the k-space data. For example, the reconstruction processing function 136 performs an inverse Fourier transform on each of the high frequency component and the low frequency component separated by the signal separation function 134 to obtain a high frequency component image (hereinafter, high frequency component image). , A low-frequency component image (hereinafter, low-frequency component image).

Alternatively, the signal separation function 134 performs smoothing on an image (hereinafter, “original image”) generated by performing reconstruction processing on the magnetic resonance data acquired by the acquisition function 132 to obtain a smoothed image. To get Then, the signal separation function 134 may obtain the high-frequency component image by subtracting the original image from the smoothed image, and may obtain the low-frequency component image by subtracting the smoothed image from the original image. Further, the signal separation function 134 may wavelet-transform the magnetic resonance data acquired by the acquisition function 132 to generate an image according to the frequency.

The brightness correction function 138 performs brightness correction on each of the high frequency image and the low frequency image based on the magnitude of the frequency. FIG. 3 is a diagram showing an example of a state of the MR image P on which the brightness correction has not been performed. The sensitivity of each coil element 108a to 108h is higher as it is closer to the coil element, and is lower as it is farther from the coil element. For this reason, as shown in FIG. 3, in the MR image P, the luminance is high in the portions close to the coil elements 108a to 108h, and is low in the portions far from the coil elements 108a to 108h. Therefore, brightness correction is performed to increase the brightness of such a low brightness portion.

Here, in the conventional brightness correction, the correction is performed based on the sensitivity value in the sensitivity map prepared in advance. For example, the reciprocal of the sensitivity value in the sensitivity map is multiplied by the brightness of the corresponding pixel in the captured image to perform the correction for increasing the brightness of the low-sensitivity portion. However, when such a brightness correction is performed, the amplification of the noise component accompanying the brightness correction becomes larger in the low sensitivity part (low brightness part) than in the high sensitivity part (high brightness part). .. That is, the noise of the MR image obtained after the brightness correction becomes non-uniform. Therefore, the brightness correction function 138 of the present embodiment performs brightness correction according to the magnitude of the frequency as described below.

The brightness correction function 138 performs brightness correction on the low-frequency image based on the sensitivity value in the sensitivity map prepared in advance. For example, for the low frequency image, the brightness correction function 138 multiplies the brightness of the corresponding pixel in the low frequency image by the reciprocal of the sensitivity value stored in the sensitivity map SM stored in the memory 150 to obtain a portion with low sensitivity. Correction is performed to increase the brightness of. That is, the brightness correction function 138 sets the correction strength for the low frequency component so that the brightness becomes uniform. As for the low frequency component, as a result of the luminance correction, the noise component included in the low sensitivity portion is amplified, but priority is given to uniforming the luminance.

On the other hand, the brightness correction function 138 performs brightness correction on the high frequency image according to the noise distribution. That is, the brightness correction function 138 sets the correction strength for high-frequency components so that in-plane noise and sharpness are kept uniform. As described above, since the component showing the brightness of the image is mainly included in the low frequency component, if the luminance correction is performed on the low frequency image, the luminance uniformity of the finally obtained image is maintained to some extent. Be drunk However, the high frequency components also include a considerable amount of components indicating the lightness and darkness of the image. Therefore, the brightness correction function 138 also performs brightness correction on the high-frequency image based on the sensitivity value in the sensitivity map SM.

That is, the brightness correction function 138 performs different brightness correction on each of the first image based on the first component and the second image based on the second component separated by the signal separation function 134. It should be noted that the brightness correction function 138 may not perform the brightness correction on the high frequency image. That is, the expression that the brightness correction function 138 performs the brightness correction on the high-frequency image according to the noise distribution also includes a mode in which the brightness correction is not performed.

The image synthesizing function 140 generates an image (hereinafter referred to as an MR image P) in which the high frequency image and the low frequency image subjected to the luminance correction by the luminance correcting function 138 are synthesized. In the MR image P, the in-plane luminance is made uniform and the sharpness and noise are made uniform by the above-mentioned luminance correction.

The output control function 142 outputs the MR image P synthesized by the image synthesizing function 140 to the display 124 to display the MR image P. The operator of the medical image generating apparatus 100 can confirm the MR image P displayed on the display 124. The output control function 142 also stores the MR image P in the memory 150. The output control function 142 may output the MR image P to a terminal device or the like connected via the network.

[Processing flow]
Hereinafter, the flow of a series of processes of the processing circuit 130 in the present embodiment will be described with reference to the flowchart. FIG. 4 is a flowchart showing a flow of a series of processes of the processing circuit 130 according to this embodiment. In the following example, the sensitivity map SM is stored in the memory 150 in advance. The process of this flowchart is performed, for example, when the operator of the medical image generating apparatus 100 performs an image capturing process on the subject OB and then operates the input interface 122 to input an image generation instruction.

First, the acquisition function 132 of the processing circuit 130 acquires a magnetic resonance signal from the sequence control circuit 110 (step S100). Next, the signal separation function 134 separates the magnetic resonance signal acquired by the acquisition function 132 into a high frequency component and a low frequency component after being digitized by the reception circuit 109 (step S102). For example, the signal separation function 134 separates the magnetic resonance data generated by the reception circuit 109 into a high frequency component and a low frequency component by performing filtering or the like. Next, the reconstruction processing function 136 reconstructs the k-space data of each of the high frequency component and the low frequency component separated by the signal separation function 134 to generate a high frequency image and a low frequency image (step S104). ..

Next, the brightness correction function 138 performs brightness correction on the low-frequency image based on the sensitivity value in the sensitivity map stored in the memory 150 (step S106). For example, for the low frequency image, the brightness correction function 138 multiplies the brightness of the corresponding pixel in the low frequency image by the reciprocal of the sensitivity value stored in the sensitivity map SM stored in the memory 150 to obtain a portion with low sensitivity. Correction is performed to increase the brightness of. Next, the brightness correction function 138 performs brightness correction on the high-frequency image according to the noise distribution (step S108).

Next, the image synthesizing function 140 generates an MR image P that is a synthesis of the high frequency image and the low frequency image whose luminance has been corrected by the luminance correcting function 138 (step S110). Next, the output control function 142 outputs the MR image P synthesized by the image synthesizing function 140 to the display 124 to display the MR image P (step S112). With the above, the processing of this flowchart is completed.

According to the first embodiment described above, the magnetic resonance signal obtained from the RF coil is separated into a high frequency component and a low frequency component to generate a high frequency image and a low frequency image, respectively. Then, by making the high-frequency image and the low-frequency image different in brightness correction method, it is possible to achieve both brightness uniformity and sharpness and noise uniformity of the finally obtained captured image.

(Modification of the first embodiment)
Hereinafter, a modified example of the first embodiment will be described. In the above-described first embodiment, the configuration has been described in which the brightness correction function 138 of the processing circuit 130 performs brightness correction according to the magnitude of the frequency. The present modification is intended for a medical image generation apparatus (MRI apparatus) that reconstructs an MR image by parallel imaging, and the brightness correction function 138 adds a g-factor (g factor in parallel imaging) in addition to the magnitude of frequency. (Also referred to as)) is different in that the brightness correction is performed based on the distribution. Therefore, for the configuration and the like, the drawings described in the first embodiment and the related description are cited, and the detailed description is omitted.

Parallel imaging is a method of using a phase array coil composed of a plurality of RF coils and shortening the imaging time by utilizing the coil sensitivity distribution of each phase array coil. In parallel imaging, the coil elements 108a to 108h simultaneously (in parallel) receive magnetic resonance signals emitted from the subject OB. The g-factor in parallel imaging is a factor indicating the ease of separation when separating (developing) image folding (image overlap) in image reconstruction processing. The lower the g-factor, the easier the separation and the higher the independence between coil elements. In addition, the higher the g-factor, the more difficult it is to separate, indicating that the independence between coil elements is low.

FIG. 5 is a diagram showing an example of a state of g-factor in the MR image P. In FIG. 5, the independence between the coil elements 108 is indicated by the shade of color. The g-factor changes depending on the distance from the coil element, for example. Therefore, as shown in FIG. 5, the g-factor may be low in a portion near each coil element 108a to 108h and may be high in a portion far from each coil element 108a to 108h. Note that the g-factor distribution is defined as, for example, a g-map GM and stored in the memory 150.

[Brightness correction function]
The brightness correction function 138 performs brightness correction on each of the high-frequency image and the low-frequency image obtained by the reconstruction processing function 136 based on both the frequency magnitude and the g-factor distribution. When using parallel imaging, noise tends to be amplified in a portion having a high g-factor, that is, a portion having low independence between coil elements. For this reason, if brightness correction is performed on a portion with a high g-factor, noise will be further amplified. For this reason, the brightness correction function 138 lowers the correction intensity and reduces the degree of brightness correction for a portion having a high g-factor. That is, the brightness correction function 138 sets the correction strength so that the correction strength of the portion with a high g-factor is lower than the correction strength of the portion with a low g-factor.

The brightness correction function 138 performs brightness correction on the low-frequency image based on the sensitivity value in the sensitivity map prepared in advance. For example, for the low frequency image, the brightness correction function 138 multiplies the brightness of the corresponding pixel in the low frequency image by the reciprocal of the sensitivity value stored in the sensitivity map SM stored in the memory 150 to obtain a portion with low sensitivity. Correction is performed to increase the brightness of. That is, for the low frequency component, the correction intensity is set so that the luminance of the low frequency image becomes uniform.

On the other hand, the brightness correction function 138 uses both the sensitivity map SM and the g map GM for a high frequency image, and performs brightness correction according to the noise distribution. In this way, for the high-frequency component, the correction strength for the brightness correction different from that for the low-frequency component is set so that the in-plane noise and the sharpness are made uniform.

According to the modification of the first embodiment described above, the magnetic resonance signal obtained from the RF coil is separated into a high frequency component and a low frequency component, and a high frequency image and a low frequency image are respectively obtained. To generate. Then, by changing the correction method between the high-frequency image and the low-frequency image, it is possible to achieve both the uniformity of brightness of the finally obtained captured image and the uniformity of sharpness and noise. Furthermore, for high-frequency images, by performing brightness correction in consideration of the influence of g-factor distribution in parallel imaging, it is possible to further improve the accuracy of brightness uniformity and sharpness and noise uniformity of the captured image. ..

(Second embodiment)
The second embodiment will be described below. In the above-described first embodiment, the signal separation function 134 of the processing circuit 130 separates the magnetic resonance signal acquired by the acquisition function 132 into a high-frequency component and a low-frequency component, and a reconstruction processing function. The configuration has been described in which 136 generates a high frequency image and a low frequency image, respectively. The present embodiment is different in that the processing circuit 130 includes a denoising function 144 that generates an image from which noise is removed from an image obtained from a magnetic resonance signal, instead of (or in addition to) the signal separation function 134 described above. Therefore, for the configuration and the like, the drawings described in the first embodiment and the related description are cited, and the detailed description is omitted.

[Example of Configuration of Medical Image Generating Device (MRI Device)]
FIG. 6 is a diagram illustrating an example of the medical image generation apparatus 200 according to the second embodiment. As shown in FIG. 6, the processing circuit 130 includes, for example, an acquisition function 132, a reconstruction processing function 136, a brightness correction function 138, an image composition function 140, an output control function 142, and a denoising function 144 (separation unit). ) And. The processing circuit 130 realizes these functions, for example, by a hardware processor executing a program stored in the memory 150, which is a storage device (storage circuit).

The denoising function 144 performs noise removal processing on an image (hereinafter referred to as an original image) obtained by reconstructing the magnetic resonance signal acquired by the acquisition function 132 by the reconstruction processing function 136, and removes noise from the original image. An image from which components have been removed (hereinafter referred to as a denoising image) is generated. Further, the denoising function 144 generates an image having only a noise component (hereinafter referred to as a noise image) by taking a difference between the denoising image and the original image. For example, the denoising function 144 generates a noise image by subtracting the denoising image from the original image.

For example, the denoising function 144 uses the denoising image generation model M stored in the memory 150 to generate a denoising image. When a certain image is input, the denoising image generation model M extracts a feature relating to a noise component included in the image (or a feature other than the noise component), and a denoising image not including the noise component (or It is a model learned to output a (noise image). Alternatively, when a certain image is input, the denoising image generation model M learns to extract the features related to the noise component and the features other than the noise component included in the image, and output the denoising image and the noise image. It may be a customized model. That is, the denoising image generation model M may be a model learned to output at least one of a denoising image and a noise image of an image when the image is input.

The denoising image generation model M includes, for example, one or more DNNs (Deep Neural Networks(s)). The denoising image generation model M has connection information indicating how the neurons (units) included in each of the input layer, one or more hidden layers (intermediate layers), and the output layer that form each DNN are connected to each other. And weight information about how many coupling coefficients are given to the data input/output between the coupled neurons. The connection information is, for example, information such as the number of neurons included in each layer, information specifying the type of neuron to which each neuron is connected, an activation function that realizes each neuron, and a gate provided between neurons in the hidden layer. including. The activation function that realizes the neuron may be, for example, a function (ReLU function or ELU function) that switches the operation according to the input code, or may be a sigmoid function, a step function, or a hyperpolygon tangent function. It may be an identity function. The gate selectively passes or weights the data transmitted between the neurons, depending on, for example, the value returned by the activation function (eg, 1 or 0). The coupling coefficient is a parameter of the activation function. For example, in a hidden layer of a neural network, when data is output from a neuron of a certain layer to a neuron of a deeper layer, a weight given to the output data. including. Further, the coupling coefficient may include a bias component unique to each layer.

The denoising image generation model M may be generated, for example, by machine learning of a relationship between a captured image acquired in the past and a denoising image of the captured image as learning data. The denoised image generation model M may be stored in the memory 150 of the medical image generation apparatus 200 after being generated by a device (not shown) that performs a learning process, for example. The processing circuit 130 of the medical image generating apparatus 200 may have a learning function so that the denoising image generating model M is generated in the medical image generating apparatus 200.

The brightness correction function 138 performs brightness correction on the denoised image generated by the denoising function 144 based on the sensitivity value in the sensitivity map SM prepared in advance. For example, for the denoising image, the brightness correction function 138 multiplies the reciprocal of the sensitivity value in the sensitivity map SM stored in the memory 150 by the brightness of the corresponding pixel in the denoising image to obtain the brightness of the low sensitivity part. Correction to increase. That is, for the denoised image, the correction intensity is set so that the luminance of the denoised image is uniform. Since the denoised image does not include a noise component, the noise is not amplified even if the luminance correction is performed. On the other hand, the brightness correction function 138 does not perform brightness correction on the noise image generated by the denoising function 144.

The image synthesizing function 140 obtains an MR image P by synthesizing the denoising image whose luminance is corrected by the luminance correcting function 138 and the noise image generated by the denoising function 144. The in-plane luminance of the MR image P is made uniform by the luminance correction of the denoised image. Further, since the noise component included in the MR image P is not subjected to the brightness correction, the sharpness and the uniformity of the noise are maintained. The image combining function 140 does not need to combine all the noise components included in the noise image with the denoised image, and may appropriately adjust the degree of the noise component to be combined.

[Processing flow]
Hereinafter, the flow of a series of processes of the processing circuit 130 in the present embodiment will be described with reference to the flowchart. FIG. 7 is a flowchart showing the flow of a series of processes of the processing circuit 130 in this embodiment. In the following example, it is assumed that the sensitivity map SM and the denoising image generation model M are stored in the memory 150 in advance. The process of this flowchart is performed, for example, when the operator of the medical image generating apparatus 200 performs an image capturing process on the subject OB and then operates the input interface 122 to input an image generation instruction.

First, the acquisition function 132 of the processing circuit 130 acquires a magnetic resonance signal from the sequence control circuit 110 (step S200). Next, the reconstruction processing function 136 reconstructs the original image from the magnetic resonance signal acquired by the acquisition function 132 (step S202).

Next, the denoising function 144 uses the denoising image generation model M stored in the memory 150 to generate a denoising image (step S204). For example, the denoising function 144 generates the denoising image by inputting the original image reconstructed by the reconstruction processing function 136 into the denoising image generation model M. Next, the denoising function 144 generates a noise image by calculating the difference between the denoising image and the original image (step S206).

Next, the brightness correction function 138 performs brightness correction on the denoised image generated by the denoising function 144 based on the sensitivity value in the sensitivity map SM stored in the memory 150 (step S208). This ensures the uniformity of the in-plane luminance signal for the denoised image. On the other hand, the brightness correction function 138 does not perform brightness correction on the noise image generated by the denoising function 144.

Next, the image synthesizing function 140 synthesizes the denoising image whose luminance has been corrected by the luminance correcting function 138 and the noise image generated by the denoising function 144 to obtain the MR image P (step S210). Next, the output control function 142 outputs the MR image P combined by the image combining function 140 to the display 124 and displays the MR image P (step S212). With the above, the processing of this flowchart is completed.

According to the second embodiment described above, the denoising image and the noise image are respectively generated from the original image obtained from the magnetic resonance signal obtained from the RF coil. Then, by performing the brightness correction only on the denoised image, it is possible to achieve both the uniformity of the brightness of the finally obtained captured image and the uniformity of the sharpness and the noise.

In the second embodiment described above, if the noise distribution included in the original image is not uniform, the noise in the finally obtained captured image may remain nonuniform. In this case, after artificially adding noise to the original image so that the noise distribution becomes uniform, denoising processing is performed to even out the noise in the finally obtained captured image. Good. Further, in the second embodiment described above, the brightness correction function 138 may perform the brightness correction based on the g-factor distribution in parallel imaging as in the modification of the first embodiment. Good.

In the above-described embodiment, the case where the brightness correction process for the image is performed when the captured image is generated when the subject OB is captured is described, but the present invention is not limited to this. For example, the original image obtained from the magnetic resonance signal may be stored in the memory 150, and the above-described brightness correction may be performed on the original image afterwards. Further, in this case, the brightness correction may be performed using the B1 sensitivity distribution or the like without using the sensitivity map. Alternatively, the luminance may be corrected by estimating the luminance distribution from the original image.

Further, in the above-described embodiment, the medical image generation apparatuses 100 and 200 have been described as having a configuration for performing brightness correction, but the present invention is not limited to this. For example, a magnetic resonance signal (or an original image) is transmitted to another processing device (medical image processing device) connected to the medical image generation devices 100 and 200 via a network, and the other processing device side The above-mentioned brightness correction may be performed. The network NW may include, for example, a WAN (Wide Area Network), a LAN (Local Area Network), the Internet, a dedicated line, a wireless base station, a provider, and the like.

Any of the embodiments described above can be expressed as follows.
Storage for storing programs,
And a processor,
The processor, by executing the program,
The magnetic resonance signal collected from the subject is separated into a first component and a second component,
Different brightness corrections are performed on each of the separated first image based on the first component and second image based on the second component.
The medical image processing apparatus configured as described above.

Any of the embodiments described above can be expressed as follows.
Generated by reconstructing a magnetic resonance signal collected from the subject based on a model learned to output at least one of a denoised image and a noisy image of the image when input. A separation unit that separates the original image into a noise image containing a noise component and a denoising image not containing a noise component,
A brightness correction unit that performs brightness correction on the denoised image separated by the separation unit;
A medical image processing apparatus comprising:

According to at least one embodiment described above, a signal separation function 134 (a denoising function 144) for separating a magnetic resonance signal collected from the object OB into a first component and a second component, and a signal separation function. A brightness correction function 138 that performs different brightness corrections on the low-frequency image (denoise image) based on the first component and the high-frequency image (noise image) based on the second component, which are separated by 134 (the denoising function 144). The provision of and makes it possible to achieve both the uniformity of brightness in the captured image and the uniformity of sharpness and noise.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. The embodiments and their modifications are included in the scope of the invention and the scope of the invention, and are also included in the scope of the invention described in the claims and the scope of equivalents thereof.

100, 200... Medical image generating device, 101... Static magnetic field magnet, 102... Gradient magnetic field coil, 104... Bed, 105... Bed control circuit, 107... Transmission circuit, 108... RF coil, 108a-h... Coil element, 109... Receiving circuit, 110... Sequence control circuit, 120... Console device, 122... Input interface, 124... Display, 130... Processing circuit, 132... Acquisition function, 134... Signal separation function, 136... Reconstruction processing function, 138... Luminance correction Functions, 140... Image combining function, 142... Output control function, 144... Denoise function, 150... Memory

Claims (14)

A separation unit that separates the magnetic resonance signal collected from the subject into a first component and a second component;
A brightness correction unit that performs different brightness corrections on each of the first image based on the first component and the second image based on the second component separated by the separation unit;
A medical image processing apparatus comprising: The first component is a low frequency component of the magnetic resonance signal,
The second component is a high frequency component of the magnetic resonance signal,
The medical image processing apparatus according to claim 1. The brightness correction unit sets a correction intensity for the first image such that the in-plane brightness of the first image is uniform.
The medical image processing apparatus according to claim 1. The brightness correction unit sets a correction intensity for the second image based on a noise distribution in a plane of the second image,
The medical image processing apparatus according to any one of claims 1 to 3. The image processing apparatus further includes an image synthesis unit that generates a captured image by synthesizing the first image and the second image whose brightness has been corrected by the brightness correction unit.
The medical image processing apparatus according to claim 1. The brightness correction unit performs the brightness correction based on a sensitivity distribution of a medical device that images the subject.
The medical image processing apparatus according to claim 1. The brightness correction unit performs the brightness correction on the second image based on both the sensitivity distribution and the g-factor distribution of the medical device that performs parallel imaging.
The medical image processing apparatus according to claim 6. The brightness correction unit sets the correction strength so that the correction strength of the high g-factor portion is lower than the correction strength of the low g-factor portion,
The medical image processing apparatus according to claim 7. The first component is a component that does not include a noise component extracted in a noise removal process performed on an image obtained from the magnetic resonance signal,
The second component is the noise component,
The medical image processing apparatus according to claim 1. The separating unit includes a denoising image based on the first component and a second denoising image based on a model learned to output at least one of a denoising image and a noise image of the image when the image is input. Generate a noise image based on the components,
The brightness correction unit performs the brightness correction on the denoised image generated by the separation unit,
The medical image processing apparatus according to claim 9. The separating unit is
By inputting an original image based on magnetic resonance signals collected from the subject into the model, the denoising image is generated,
The noise image is generated by taking the difference between the original image and the denoised image,
The medical image processing apparatus according to claim 10. An image synthesizing unit configured to generate a captured image by synthesizing the denoised image whose luminance is corrected by the luminance correcting unit and the noise image generated by the separating unit,
The medical image processing apparatus according to claim 10. Computer
The magnetic resonance signal collected from the subject is separated into a first component and a second component,
Different luminance corrections are performed on each of the separated first image based on the first component and second image based on the second component.
Medical image processing method. On the computer,
The magnetic resonance signal collected from the subject is separated into a first component and a second component,
Causing each of the separated first image based on the first component and second image based on the second component to perform different brightness corrections,
program.