JP2017056288A - Ultrasonic diagnostic apparatus and image capturing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To acquire a high-quality image by high-speed imaging, in an MRI(Magnetic Resonance Imaging) apparatus and the like to which compressive sensing is applied.SOLUTION: An ultrasonic diagnostic apparatus includes an observation part 100 that, when having observed one of two points point-symmetrical with respect to the origin, does not observe the other point in high frequency component observation in a K-space of an MRI apparatus; and a reconfiguration part 106 that reconfigures an image from a K-space component observed by the observation part. In a reconfiguration process of the reconfiguration part, image correction processing based on an observation pattern in the observation part is included.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a magnetic resonance imaging apparatus and the like, and particularly to a high-speed and high-quality image imaging technique.

  Magnetic Resonance Imaging (MRI) is a method of imaging in vivo information using the nuclear magnetic resonance (NMR) phenomenon. CT (Computed Tomography) is a tomographic image. It is possible to visualize physical information that cannot be obtained by CT. There is no radiation exposure. However, due to its characteristics, there is a problem that the inspection time is long. Generally, several tens of minutes are required for each specimen, and the abdomen and lungs must be breathed for several tens of seconds. . As one of the techniques, high-speed imaging called parallel imaging is used, but there are many problems such as deterioration in image quality and increase in noise as the speed-up rate increases.

  In recent years, application of a technique called compressed sensing (CS) to an MRI apparatus has been studied (Non-Patent Document 1). CS uses the sparsity of signals to restore the original signal with high accuracy from sparse observation results. Here, the sparse observation is to perform observation of a data amount smaller than the reconstructed data amount. What kind of observation pattern is used in compressed sensing is one important point. In general, a random binary matrix, a weighted random matrix, a radial pattern, a spiral pattern, or a parallel line pattern is often used.

Lustig et al., “Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging,” Magnetic Resonance in Medicine, 58 1182-1195, 2007. G. Plonka, J. Ma, Curvelet-wavelet regularized Split Bregman method for compressed sensing, International Journal of Wavelets, Multiresolution and Information Processing, 79-110,2011

  In the CS observation pattern described above, for example, when performing three-dimensional imaging with an MRI apparatus, the signal acquisition trajectory can be selected randomly (two-dimensional), but the relevance with image quality is unclear. But there are no clear rules. In the case of two-dimensional imaging, since the trajectory change is only in the phase encoding direction (one-dimensional), for example, a parallel line-like observation pattern is assumed, and there is a problem in compression rate or restoration performance. Also, the image quality of an image restored by CS is greatly influenced by parameters at the time of reconstruction, but it is very difficult to appropriately control those parameter values.

  An object of the present invention is to provide an image pickup apparatus and method that can solve these problems.

  In order to achieve the above object, according to the present invention, there is provided an image capturing apparatus for capturing an image of a subject, an observation unit for observing the subject and outputting observation data, and an image from observation data from the observation unit. A reconstruction unit that reconstructs the observation unit, the observation unit acquires observation data based on an observation pattern that performs sparse observation, and the reconstruction unit performs image correction processing on the observation data based on the observation pattern An imaging device is provided.

  Further, in the present invention, in order to achieve the above object, an image imaging apparatus for capturing an image of a subject, comprising: an observation unit that observes the subject and outputs observation data of K space; A reconstruction unit that reconstructs an image from the observation data of the K space, the observation unit acquires K space observation data based on the observation pattern that performs sparse observation, and in the observation of high frequency components in the K space, Provided is an image pickup apparatus in which when one of two points symmetrical with respect to the origin is observed, the other is not observed.

  Further, in the present invention, in order to achieve the above object, an image imaging method for imaging an image of a subject, wherein observation data of the subject is acquired based on an observation pattern for performing sparse observation, and the image is obtained from the observation data. The present invention provides an image capturing method for performing image correction processing based on an observation pattern for observation data when reconstructing.

  According to the present invention, a high-quality image can be acquired with high-speed imaging in an image imaging device.

It is a block diagram which shows an example of the whole structure of the MRI apparatus based on Example 1-3. BRIEF DESCRIPTION OF THE DRAWINGS It is a figure for demonstrating the observation method of the MRI apparatus based on Example 1. FIG. It is a figure which shows an example of the observation pattern of the MRI apparatus based on Example 1. FIG. It is a figure which shows the reconstruction process flow of the MRI apparatus based on Example 1. FIG. It is a figure for demonstrating the correction process based on the observation pattern of the MRI apparatus based on Example 1. FIG. 3 is an example of a horizontal edge smoothing filter of the MRI apparatus according to the first embodiment. It is the figure which smoothed the parallel linear observation pattern of the MRI apparatus based on Example 1. FIG. FIG. 6 is a diagram illustrating an MRI apparatus observation method according to a second embodiment. It is a figure which shows an example of the observation pattern of the MRI apparatus based on Example 2. FIG. It is a figure which shows the reconstruction process flow of the MRI apparatus based on Example 3. FIG. It is a figure which shows an example of the screen shown to a user based on Example 3. FIG. FIG. 10 is a block diagram illustrating an example of an overall configuration of an ultrasonic diagnostic apparatus according to a fourth embodiment. FIG. 10 is a block diagram illustrating an example of an overall configuration of a CT apparatus according to a fifth embodiment.

  Embodiments of an image pickup apparatus according to the present invention will be described below with reference to the drawings. First, one configuration example of an MRI apparatus, which is a type of image pickup apparatus, commonly used in Embodiment 1-3 using FIG. Will be explained. As is clear from FIG. 1, the MRI apparatus is roughly divided into an observation unit 100 that observes the subject and a reconstruction unit 106 that reconstructs the image of the observed subject.

  The observation unit 100 includes a static magnetic field generation system 102, a gradient magnetic field generation system 103, a transmission system 104, a reception system 105, a sequencer 107, and a central processing unit (CPU) 108. The static magnetic field generation system 102 generates a uniform magnetic field in the space around the subject 101. A permanent magnet or a normal or superconducting magnetic field generating means is arranged. The gradient magnetic field generation system 103 includes a gradient magnetic field coil 109 and a gradient magnetic field power source 110 that drives the gradient magnetic field coil 109, and applies a gradient magnetic field to the subject 101.

  The sequencer 107 is a control unit that repeatedly applies a high-frequency magnetic field pulse (RF pulse) and a gradient magnetic field pulse in a predetermined pulse sequence. The sequencer 107 operates under the control of the CPU 108 and performs various processes necessary for collecting tomographic image data of the subject 101. Is transmitted to the transmission system 104, the gradient magnetic field generation system 103, and the reception system 105. The transmission system 104 includes a high-frequency generator 111, a modulator 112, an amplifier 113, and a high-frequency coil 114a, and irradiates an RF pulse that causes nuclear magnetic resonance on the atomic nuclear spins of the subject 101. The reception system 105 includes a high-frequency coil 114b, an amplifier 115, a quadrature detector 116, and an A / D converter 117. The reception system 105 receives an echo signal emitted by nuclear magnetic resonance of a nuclear spin and transmits it to the reconstruction unit 106. To do.

  The reconstruction unit 106 includes an image processing unit 118, an input unit 119 including a keyboard, mouse, touch panel, buttons, etc., an output unit 120 including a display, a printer, etc., a magnetic disk, an optical disk, etc. The storage device 121 stores a program. When data is input from the receiving system 105, the image processing unit 118 reconstructs an image, causes the output unit 120 to display the image, and records the image in the storage device 121. As shown in FIG. 1, the image processing unit 118 can be realized by program processing by the CPU 108. However, a central processing unit (CPU) different from the CPU 108 may be installed in the reconstruction unit 106, It is also possible to configure with dedicated hardware for image processing.

  Next, an observation method of echo signals received by the reception system 105 in the observation unit 100 of the MRI apparatus of FIG. 1, that is, K space data will be described with reference to FIG. In FIG. 2, 201 is a K space to be observed (a space that is a Fourier transform of image data in real space), 202 is the origin of the K space 201, 203 is a low frequency component region near the origin, and 204 is a low frequency near the axis. A component region 205 indicates a high-frequency component region. For high-speed imaging, it is necessary to reduce the number of data points to be observed. In general, in CS, it is better to observe the observation space at random. This is for observing the signal component evenly. However, in the K space, if the low-frequency component 203 or 204 near the origin is not observed closely, a large fluctuation occurs in the contrast and brightness of the reconstructed image. Or, for 204, all elements are observed. As an observation pattern by such an observation method, a parallel line 301, a radial 302, a random 303 as shown in FIG.

  Next, processing contents of the image processing unit 118 of the MRI apparatus in FIG. 1 will be described. As described above, the image processing unit 118 is preferably realized by program processing by the CPU. FIG. 4 shows a processing flow realized by the image processing unit 118 by program processing or the like. When the observation pattern described with reference to FIG. 3 is used, artifacts such as fake edges and blurring may occur as compared with the case where all elements are observed. Therefore, the image processing unit 118 reconstructs a sharp image without artifacts by solving the cost minimization problem. Any method of solving the minimization problem to be used may be used, but in the embodiment described below, a cost minimization method using the Split Bregman method (Non-Patent Document 2) will be described.

  The first embodiment is an image capturing apparatus that captures an image of a subject. The observation unit 100 observes the subject and outputs observation data. The reconstruction unit reconstructs an image from observation data from the observation unit. The observation unit 100 acquires observation data based on an observation pattern for performing sparse observation, and the reconstruction unit 106 performs image correction processing based on the observation pattern for the observation data. It is an Example of the MRI apparatus which is.

  As shown in FIG. 4, the image processing unit 118 of the present embodiment reconstructs an image from the observed K space by repeatedly executing Step ST401 to Step ST407. As described above, the image processing unit 118 performs cost minimization and sequential optimization using the Split Bregman method. In this embodiment, the result corrected by the image correction process is used in the estimation update process.

As shown in FIG. 4, first, in step ST400, all elements u ⁰ , u _C ⁰ , u _w ⁰ , b _C ⁰ , and b _w ⁰ are initialized to 0. Thereafter, step ST401 to step ST407 are repeatedly executed.

Hereinafter, the k + 1th iteration will be described. In step ST401, equation (1) is calculated, and an estimation result u ^{k + 1} is calculated.

Here, f ^k is the K space updated by the previous (k-th) iteration, Φ is an observation process by Fourier transform and observation pattern, and Φ ^T is an inverse transform of Φ. I _N is an array having all elements of 1 and the same size as f ^k . U _C ^k , u _w ^k , b _C ^k , and b _w ^k are change components calculated in the immediately preceding (k-th) iteration. Μ is a positive constant as a parameter.

Next, in step ST402, u _C ^{k + 1} and u _w ^{k + 1} are calculated by equations (2) and (3).

Here, Ψ _c ^T and Ψ _w ^T are a Curvelet inverse transform and a Wavelet inverse transform, respectively. This uses Curvelet transform and Wavelet transform, but other than this, TV (Total Variation), Ridgelet transform, etc. may be used. Moreover, you may use combining these.

S _C and S _w represents a process called Soft - shrinkage. Each expression for S _C and S _w are all elements (4), the processing shown by (5). Here, Ψ _c and Ψ _w are Curvelet transform and Wavelet transform, respectively. Λ is a constant as a parameter.

Next, in step ST403, b _C ^{k + 1} and b _w ^{k + 1} are calculated using equations (6) and (7).

In step ST404, as shown in the equation (8), the correction process G based on the observation pattern is performed on the estimation result uk ^{+ 1} as described above. That is, in the estimation update process, the result corrected by the correction process G is used. Thereby, the MRI apparatus of the present embodiment can improve the image quality of the reconstructed image.

  Here, as an observation method in the observation unit 100 of the present embodiment, correction processing G based on the observation pattern 301 when using the horizontal parallel observation pattern 301 shown in FIG. 3 will be described with reference to FIG. explain.

  In FIG. 5, 501 indicates a true horizontal edge image to be reconstructed, and 502 indicates an image of a reconstructed image obtained by the reconstructing unit 106 when the parallel line-shaped observation pattern 301 shown in FIG. 3 is used. Yes. As is clear from the image 502 shown in the figure, the horizontal edge is blurred and is easily reconstructed. This is because the high-frequency component corresponding to the horizontal edge is difficult to obtain in the observation using the parallel line-like observation pattern 301 shown in FIG.

  Therefore, in order to sharpen the horizontal edge of the reconstructed image, in this embodiment, a horizontal edge smoothing process is used based on the observation pattern 301 as the correction process G in the above equation (8). The reconstruction algorithm performs reconstruction so that the estimation result after smoothing approximates the input observation data. Thereby, a sharper horizontal edge can be estimated.

  The horizontal edge smoothing process described above may be any process. For example, as shown in FIG. 6, a one-dimensional moving average filter 601 and a one-dimensional Gaussian filter 602 can be considered.

  In addition, when the radial observation pattern 302 shown in FIG. 3 or the random observation pattern 303 is used instead of the parallel linear observation pattern 301, there is no blur in a specific direction. For example, it is conceivable to apply a two-dimensional Gaussian filter.

  Further, as shown in FIG. 7, the observation pattern may be used after being sufficiently smoothed. In the figure, reference numeral 701 denotes a sufficiently smoothed parallel line observation pattern. This may be used as a filter for the correction process G.

Returning to the processing flow of FIG. 4, step ST405 is a process of updating f ^{k + 1} from the estimation result corrected in step ST404 using equation (9).

Finally, end determination is performed in step ST406. Any reference may be used for the end determination. For example, absolute values and average values of b _C ^{k + 1} and b _W ^{k + 1} , a difference value from the previous iteration, the number of loops k, and the like can be considered. Moreover, you may determine completion | finish using a new evaluation parameter | index with respect to an estimation result. If the end determination is not satisfied, k is updated in step ST407, and the process returns to step ST401. In step ST407, updating so as to gradually increase the parameter μ may be performed. If the end determination is satisfied, u ^{k + 1} is output as a reconstructed image.

  According to the first embodiment described in detail above, the correction process based on the observation pattern in the observation unit is incorporated in the reconstruction process of the reconstruction unit, so that an improvement of several dB is expected in the PSNR (Peak Signal Noise Ratio). . That is, according to the image pickup apparatus of the present embodiment, it is possible to acquire a high-quality image using observation data obtained with an observation pattern that performs sparse observation. In other words, by obtaining less data using observation patterns that perform sparse observation, high-speed imaging is possible, and high-quality images can be reconstructed by performing correction processing based on observation patterns. . In addition, the user's image quality adjustment work can be greatly reduced.

  The second embodiment is an image capturing apparatus that captures an image of a subject, and observes the subject, outputs observation data in K space, and reconstructs the image from the observation data from the observation unit. The observation unit 100 acquires K-space observation data based on observation patterns for performing sparse observation, and is point-symmetric with respect to the origin of the K space in the observation of high-frequency components in the K space. This is an embodiment of an MRI apparatus which is an image pickup apparatus in which either one of the two points is observed and the other is not observed.

  Also in the present embodiment, the overall configuration of the apparatus is represented in FIG. The present embodiment is different from the first embodiment in the observation data observation method that is an echo signal in the observation unit 100. The reconstruction unit 106 is the same as that in the first embodiment.

  FIG. 8 is a diagram schematically illustrating an observation data observation method in the present embodiment. In FIG. 8, the same components as those shown in FIG. 2 are denoted by the same reference numerals and description thereof is omitted. In the figure, reference numerals 801 and 802 are two points that are point-symmetric with respect to the origin 204 of the K space 201. In the present embodiment, the observation unit 100 of the MRI apparatus in FIG. 1 observes one of the points 801 and 802 that are point-symmetric in the high-frequency region 205 with respect to the origin 202 of the K space 201. Observe using an observation pattern that does not observe the other. An example of the observation pattern used in the observation method of this embodiment is shown in FIG. When observing a two-dimensional K space, a parallel observation pattern 901 that is asymmetric with respect to the horizontal axis, a radial observation pattern 902 that observes in an angular direction that does not overlap even if rotated 180 degrees, etc. can be used. . When observing a three-dimensional K space, a random observation pattern 903 that satisfies the above conditions can also be used.

  Since point symmetry exists in the K space, efficient observation can be performed by using the observation method for observing only one of the point symmetry positions as described above. As a result, an improvement in the image quality of an image reconstructed in the image processing unit 118 of the MRI apparatus can be expected. Further, when the observed K space is input to the reconstruction unit 106, a value may be inserted using point symmetry.

  As described above, according to the present embodiment, a high-quality image can be acquired with high-speed imaging with less observation data in the MRI apparatus. In the present embodiment, the reconstruction unit 106 can efficiently perform the observation even when the correction process based on the observation pattern described in the first embodiment is not incorporated in the reconstruction process. Effects unique to the second embodiment can be obtained.

  The third embodiment is an image capturing apparatus, in which an observation unit that observes an image acquires observation data based on an observation pattern that performs sparse observation, and a reconstruction unit performs image correction processing based on the observation pattern. It is an Example of the MRI apparatus of the structure performed in multiple numbers.

  FIG. 10 shows a processing flow of the image processing unit 118 in the third embodiment. In FIG. 10, the same components as those shown in FIG. 4 are denoted by the same reference numerals and description thereof is omitted. The overall configuration of the apparatus is as shown in FIG.

  According to the present embodiment, the image quality of the reconstructed image can be adjusted more easily, and the user's image quality adjustment work can be greatly reduced. This embodiment is different from the first embodiment in that a step ST1001 is newly added to the processing flow of the image processing unit 118 of the reconstruction unit 106 and a plurality of correction processes are performed as shown in FIG. is there. That is, a plurality of correction processes are prepared and executed.

  In general, in order to change the image quality of the reconstructed image, it is necessary to adjust the variable μ in Equation (1) and the constant λ in Equations (4) and (5). , Λ and the reconstructed image have low sensitivity and are different from human subjectivity. In addition, there is a need to set these parameters for each imaging target and part, and depending on the value setting, the image quality of the reconstructed image may be greatly impaired. difficult.

  Therefore, in this embodiment, the user adjusts the image quality of the reconstructed image by adjusting the correction processing in the added step ST1001. An adjustment method using the correction process ST1001 will be described.

First, step ST1001, to the estimation result corrected by the step ST 404, performs any correction processing G ₂ shown in Equation (10).

The user can select, change and adjust the processing contents and parameters of a correction process G ₂ by a keyboard and a mouse, an input unit 119 of FIG. Although correction G ₂ are may be any correction processing, for example, reconstructed image more if you want to sharpening smoothing processing, for example, the Gaussian filter processing or the like performed. Conversely, when it is desired to suppress noise more, sharpening processing, for example, unsharp mask processing, may be performed.

  In this case, the parameter input by the user may be, for example, the size or variance of a Gaussian filter. Also, several parameter sets may be prepared in advance and selected.

  FIG. 11 is an example of an adjustment screen presented to the user using a display or the like that is the output unit 120 of FIG. For example, an item such as sharpness can be adjusted with a numerical value 1101, a check button 1102, a slider 1103, and the like.

In step ST1002 of FIG. 10, f ^{k + 1} is updated by equation (11).

  As described above, according to the present embodiment, the image quality of the reconstructed image can be adjusted more easily, and the user's image quality adjustment work can be greatly reduced.

The fourth embodiment is an embodiment in the case where the image pickup apparatus is an ultrasonic diagnostic apparatus capable of acquiring high-quality ultrasonic images at high speed.
FIG. 12 is a block diagram illustrating a configuration example of an ultrasonic diagnostic apparatus as an image capturing apparatus according to the fourth embodiment. In FIG. 12, the same components as those shown in FIG. 1 are denoted by the same reference numerals, and the description thereof is omitted. In the figure, an ultrasonic observation unit 1206 constituting an ultrasonic diagnostic apparatus includes an ultrasonic probe 1201, a transmission unit 1202, a reception unit 1203, an ultrasonic transmission / reception control unit 1204, and a phasing addition unit 1205. Composed.

  The transmission unit 1202 repeatedly transmits ultrasonic waves to the subject 101 via the ultrasonic probe 1201 at time intervals. The receiving unit 1203 receives time-series reflected echo signals generated from the subject 101. The ultrasonic transmission / reception control unit 1204 controls the transmission unit 1202 and the reception unit 1203. The phasing addition unit 1205 phasing-adds the received reflected echo signals to generate RF signal frame data in time series. The phasing / adding unit 1205 includes an analog / digital (A / D) converter, and the RF signal frame data is output as observation data to the image processing unit 118 of the reconstruction unit 106. The image processing unit 118 An ultrasonic echo image is generated using the observed data.

  The ultrasonic observation unit 1206 according to the present embodiment transmits a sparse observation result to the image processing unit 118 as observation data. Here, the sparse observation includes, for example, decimating the ultrasonic transmission interval of the transmission unit 1202. The image processing unit 118 performs the reconstruction process shown in the first or third embodiment on the sparse observation result. In the present embodiment, since the reflected echo signal to be observed is an image signal, the same image processing as described in the first embodiment is performed without performing the Fourier transform in the above equation (1). I do.

  According to the ultrasonic diagnostic apparatus of the present embodiment, high-quality ultrasonic images can be acquired at high speed.

Embodiment 5 is an embodiment of a CT apparatus in which the image capturing apparatus can acquire a high-quality CT (Computed Tomography) image at high speed.
FIG. 13 is a block diagram illustrating a configuration example of a CT (Computed Tomography) apparatus as an image capturing apparatus according to the fifth embodiment. Also in this figure, the same components as those shown in FIGS. 1 and 12 are denoted by the same reference numerals and description thereof is omitted.

  In FIG. 13, the CT observation unit 1309 includes an X-ray tube device 1301, a rotating disk 1302, a collimator 1303, an X-ray detector 1306, a data collection device 1307, a bed 1305, and a system control unit 1308. The The X-ray tube device 1301 is a device that irradiates a subject placed on a bed 1305 with X-rays. The collimator 1303 is a device that limits the radiation range of X-rays emitted from the X-ray tube device 1301. The rotating disk 1302 includes an opening 1304 into which a subject placed on a bed 1305 enters, and is equipped with an X-ray tube device 1301 and an X-ray detector 1306, and rotates around the subject.

  The X-ray detector 1306 is a device that measures the spatial distribution of transmitted X-rays by detecting X-rays that are disposed opposite to the X-ray tube device 1301 and transmitted through the subject. The rotating disk 1302 is arranged in the rotating direction, or the rotating disk 1302 is arranged in a two-dimensional arrangement of the rotating direction and the rotating shaft direction. The data collection device 1307 is a device that collects the X-ray dose detected by the X-ray detector 1306 as digital data. The system control unit 1308 controls the rotation of the rotating disk 1302, the up / down / left / right movement of the bed 1305, the electric power input to the X-ray tube device 1301, and the like.

  The CT observation unit 1309 of the CT apparatus of the present embodiment transmits digital data that is a sparse observation result to the image processing unit 118 of the reconstruction unit 106 as observation data. Here, sparse observation means that sufficient observation is not performed in the angular direction when observation is performed using the X-ray tube device 1301 and the X-ray detector 1306. The image processing unit 118 of the reconstruction unit 106 of the present embodiment performs the reconstruction process shown in the first or third embodiment on the sparse observation result to reconstruct a high-quality CT image.

  As described above, according to the present embodiment, a high-quality CT image can be acquired at high speed. Furthermore, it can be expected to reduce the amount of X-ray exposure compared to the prior art.

  In addition, this invention is not limited to an above-described Example, Various modifications are included. For example, the above-described embodiments have been described in detail for better understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment.

  Further, the above-described configurations, functions, processing units, and the like have been described with reference to an example of creating a CPU program that realizes a part or all of them. Needless to say, it may be realized by hardware.

  100 observation unit, 101 subject, 102 static magnetic field generation system, 103 gradient magnetic field generation system, 104 transmission system, 105 reception system, 106 reconstruction unit, 107 sequencer, 108 central processing unit (CPU), 109 gradient magnetic field coil, 110 Gradient magnetic field power supply, 111 high frequency generator, 112 modulator, 113 amplifier, 114 high frequency coil, 115 amplifier, 116 quadrature phase detector, 117 A / D converter, 118 image processing unit, 119 input unit, 120 output unit, 121 Storage device, 201 K space, 202 origin, 203 low frequency area near the origin, 204 axis low frequency area, 205 high frequency area. 301 Horizontal parallel observation pattern, 302 Radial observation pattern, 303 Random observation pattern, 501 True horizontal edge image, 502 Reconstructed horizontal edge image, 601 Moving average filter, 602 Gaussian filter, 701 Smoothed Observation points, 801 observation points, 802 observation points that are symmetrical to observation point 801 with respect to origin 202, 901 parallel line observation patterns, 902 radial observation patterns, 903 random observation patterns, 1201 ultrasonic probe 1202 transmitter, 1203 receiver, 1204 ultrasonic transmission / reception controller, 1205 phasing and adding unit, 1206 ultrasonic observation unit, 1301 X-ray tube device, 1302 rotating disk, 1303 collimator, 1304 opening, 1305 bed, 1306 X-ray detector, 1307 data acquisition device, 1308 system control unit, 1309 CT observation unit.

Claims (5)

An ultrasound diagnostic apparatus for imaging a subject,
An observation unit that performs observation of the subject using an ultrasonic probe and outputs observation data;
A reconstruction unit for reconstructing an ultrasonic echo image from the observation data from the observation unit;
With
The observation unit is
Obtain the observation data based on the observation pattern that performs sparse observation,
The reconstruction unit includes:
A correction process based on the observation pattern is included for the observation data,
The observation pattern of the observation unit is a parallel line,
The correction processing of the reconstruction unit includes horizontal edge smoothing processing,
An ultrasonic diagnostic apparatus. An ultrasound diagnostic apparatus for imaging a subject,
An observation unit that performs observation of the subject using an ultrasonic probe and outputs observation data;
A reconstruction unit for reconstructing an ultrasonic echo image from the observation data from the observation unit;
With
The observation unit is
Acquire the observation data based on an observation pattern that performs sparse observation at least partially at random,
The reconstruction unit includes:
A correction process based on the observation pattern is included for the observation data,
The observation pattern of the observation unit is a radial radial direction that does not overlap each other even if rotated 180 degrees,
The correction process of the reconstruction unit includes a smoothing process.
An ultrasonic diagnostic apparatus. The ultrasonic diagnostic apparatus according to claim 1, wherein:
The reconstruction unit includes a plurality of the correction processes.
An ultrasonic diagnostic apparatus. An image imaging method for imaging a subject using an ultrasonic diagnostic apparatus,
Obtain observation data of the subject based on the observation pattern for sparse observation,
When reconstructing an ultrasonic echo image from the observation data, a correction process based on the observation pattern is performed on the observation data,
The observation pattern is a parallel line,
The correction process includes a horizontal edge smoothing process,
An image capturing method characterized by the above. An image imaging method for imaging an image of a subject using an ultrasonic diagnostic apparatus,
Obtain observation data of the subject based on an observation pattern that performs sparse observation at least partially at random,
When reconstructing an ultrasonic echo image from the observation data, a correction process based on the observation pattern is performed on the observation data,
The observation pattern is a radial radial direction that does not overlap each other even if rotated 180 degrees,
The correction process includes a smoothing process.
An image capturing method characterized by the above.

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