WO2016208503A1

WO2016208503A1 - Image diagnosing device and method

Info

Publication number: WO2016208503A1
Application number: PCT/JP2016/068059
Authority: WO
Inventors: 昌宏荻野; 喜実野口; 毅倫村瀬; 博幸望月; 高橋　哲彦
Original assignee: 株式会社日立製作所
Priority date: 2015-06-22
Filing date: 2016-06-17
Publication date: 2016-12-29
Also published as: JP6423094B2; JPWO2016208503A1

Abstract

The present invention enables image display to make an early appropriateness decision of a photographed image in a high-speed photography system. An image diagnosing device 100 is provided with: a signal processing unit 109 which renders a reception signal obtained from a subject 101 into an image by an image reconstruction process, and which determines whether the image quality of the obtained image satisfies a predetermined threshold value; and an image display unit 111 which displays the image determined by the signal processing unit to satisfy the threshold value, on a threshold value by threshold value basis. A plurality of threshold values is set to display the image in a stepwise manner. With respect to an output image, a specific region is set, and a preferred image reconstruction process is implemented in the specific region set to generate an image.

Description

Diagnostic imaging apparatus and method

The present invention relates to a diagnostic imaging apparatus, and more particularly to a technique for generating a high-speed, high-quality image in a magnetic resonance imaging apparatus.

Magnetic Resonance Imaging (hereinafter referred to as MRI) equipment observes the intensity distribution of nuclear magnetic resonance (Nuclear ， Magnetic Resonance, hereinafter referred to as NMR) of the hydrogen nuclei (protons) in the human body, and displays a contrast image of the human cross section. It is a medical image diagnostic technique to obtain. Compared with CT (Computed Tomography), which obtains cross-sectional images by irradiating X-rays and other radiation, MRI has no radiation exposure and is superior in safety to the human body, mainly in soft tissues such as the brain. It is characterized by excellent drawing. However, it generally requires several tens of minutes per specimen, and the abdomen and lungs must be stopped for several tens of seconds, and the burden on the patient is large. . In recent years, compression sensing (hereinafter referred to as CS) has attracted attention as a technique for realizing the high throughput, that is, the reduction of photographing time (see Patent Document 1 and Non-Patent Document 1).

On the other hand, the current MRI system displays a confirmation image for confirming the suitability of imaging in a shorter time in contrast-enhanced MRA (Magnetic Resonance Angiography) imaging using the PI (Parallel Imaging) method, which is the mainstream speed-up method. There is a technology (see Patent Document 2).

US Patent 7,646,924 JP 2010-012294 A

The above-mentioned CS is a theory that restores video from a small amount of observation data, and research has progressed and expanded rapidly since around 2006. However, image reconstruction processing in CS is generally computationally intensive, and photography itself is Even if it ends at high speed, there is a problem that it takes time until image display. On the other hand, the speed-up method disclosed in Patent Document 2 does not currently support a photographing apparatus using CS.

An object of the present invention is to solve the above-described problems and provide an image diagnostic apparatus and an image generation method capable of displaying an image for early determination of adaptation / non-adaptation of a captured image in a high-speed imaging system. is there.

In order to achieve the above object, according to the present invention, an image diagnostic apparatus, wherein a received signal obtained from a subject is imaged by an image reconstruction technique, and the image quality of the obtained image satisfies a predetermined threshold value. An image diagnostic apparatus having a configuration including a signal processing unit that determines whether or not and an image display unit that displays an image determined to satisfy a predetermined threshold by the signal processing unit for each threshold is configured.

In order to achieve the above object, according to the present invention, there is provided an image diagnostic apparatus, wherein a received signal obtained from a subject is imaged by an image reconstruction process, and an image is output, and a signal processing unit Constitutes an image diagnostic apparatus configured to include an image display unit that displays an image output from the image processing unit and a storage unit that stores calculation time information of image reconstruction processing in the signal processing unit.

Furthermore, in order to achieve the above object, according to the present invention, there is provided an image generation method in an image diagnostic apparatus, which is obtained by imaging a received signal obtained from a subject by an image reconstruction method. Provided is an image generation method for determining whether or not the image quality satisfies a predetermined threshold and displaying an image determined to satisfy the predetermined threshold on an image display unit for each threshold.

According to the present invention, an image obtained by image reconstruction processing can be evaluated and judged in advance, and it is possible to know at an early stage whether or not imaging optimal for clinical use has been achieved, thereby improving the examination workflow in the medical field. can do.

It is a figure which shows an example of the functional block of the MRI apparatus based on Example 1. FIG. FIG. 3 is a functional block diagram of a signal processing unit according to the first embodiment. 3 is a flowchart of image reconstruction processing according to the first embodiment. It is a figure for demonstrating the undersampling of K space based on Example 1. FIG. FIG. 6 is a diagram illustrating an example of a flowchart of image reconstruction processing according to the first embodiment. FIG. 10 is a diagram illustrating another example of the flowchart of the image reconstruction process according to the first embodiment. FIG. 3 is an image diagram of image display according to the first embodiment. FIG. 6 is a functional block diagram of an MRI apparatus according to a second embodiment. 10 is a flowchart of image reconstruction processing according to the second embodiment. FIG. 10 is an image diagram of image display according to the second embodiment. FIG. 10 is a functional block diagram illustrating a signal processing unit according to a modification of the second embodiment. FIG. 10 is an image view of image display according to a modified example of Embodiment 2. FIG. 6 is a functional block diagram of an MRI apparatus according to a third embodiment. It is a figure for demonstrating an example of the database based on Example 3. FIG. FIG. 10 is a diagram for explaining an example of a convergence curve model according to a third embodiment. FIG. 10 is a functional block diagram illustrating a signal processing unit according to a third embodiment. 12 is a flowchart of image reconstruction processing according to the third embodiment. FIG. 10 is a functional block diagram of convergence curve prediction processing according to a third embodiment. FIG. 12 is an image diagram of image display according to a modification example of Example 3.

Hereinafter, embodiments of the present invention will be sequentially described with reference to the drawings.

Example 1 is an example of an MRI apparatus as an image diagnostic apparatus. In other words, the present embodiment is an image diagnostic apparatus, in which a received signal obtained from a subject is imaged by image reconstruction processing, and signal processing for determining whether or not the image quality of the obtained image satisfies a predetermined threshold value An image diagnostic apparatus configured to include an image display unit configured to display, for each threshold, an image determined to satisfy a predetermined threshold by the signal processing unit, and an image generation method in the image diagnostic apparatus, The diagnostic imaging apparatus images a received signal obtained from a subject by an image reconstruction method, determines whether the image quality of the obtained image satisfies a predetermined threshold, and determines that the predetermined threshold is satisfied. 3 is an example of an image generation method for displaying a displayed image on an image display unit for each threshold value.

FIG. 1 is a diagram showing an example of functional blocks of the MRI apparatus of the present embodiment. As shown in the figure, the MRI apparatus 100 according to the first embodiment includes a magnet 102 that generates a static magnetic field around a subject 101, a gradient magnetic field coil 103 that generates a gradient magnetic field in a space, and a high-frequency magnetic field in this region. An RF coil 104 for detecting the MR signal generated by the subject 101, a gradient magnetic field power source 106 for supplying power to the gradient magnetic field coil, an RF transmitter 107 for controlling the RF coil 104, and the RF coil 105 A signal detection unit 108 that detects an MR signal from the signal processing unit 108, a signal processing unit 109 that processes a signal detected by the signal detection unit 108, an image processing unit 110 that processes an image obtained from the signal processing unit 109, and an image processing An image display unit 111 that displays an image processed by the unit 110 and a control unit 112 that controls the overall operation are provided.

The gradient magnetic field power source 106, the RF transmission unit 107, and the signal detection unit 108 are controlled by the control unit 112 according to a time chart generally called a pulse sequence. In the signal processing unit 109, the MR signal detected by the signal detection unit 108 is converted into an image signal. The image processing unit 110 performs three-dimensional (3D) rendering processing, enlargement / reduction processing, and the like on the image signal from the signal processing unit 109. Here, the signal processing unit 109, the image processing unit 110, and the control unit 112 can be realized by programs executed by a central processing unit (CPU) of a normal computer. The signal processing unit 109 may be realized by dedicated hardware instead of executing part or all of it by a program. As the image display unit 111, the above-described computer display can be used.

FIG. 2 is a diagram illustrating an example of functional blocks showing details of the signal processing unit 109 in the MRI apparatus of the present embodiment. As shown in the figure, the signal processing unit 109 includes a Fourier inverse transform unit (IFFT) 201 to which the received signal from the signal detection unit 108 is input, an image reconstruction processing unit 202, and Sum Of the reconstructed image for each channel. The image composition processing unit 203 performs square processing and the like, and includes a filtering processing unit 202 such as edge enhancement and noise reduction. A control signal 205 described in the second embodiment is input to the image reconstruction processing unit 202.

The image reconstruction processing unit 202 is preferably realized by the above-described program processing by the CPU, and performs a process of increasing the dimension of the data obtained with a low dimension. FIG. 3 shows an example of a high-dimensional program processing flow that constitutes the image reconstruction processing unit 202. The image reconstruction processing unit 202 reconstructs a sharp high-dimensional image by solving the cost minimization problem. At this time, any solution can be used as a solution for the cost minimization problem. For example, cost minimization and sequential optimization using the Split-Bregman method (Non-Patent Document 2) can be considered. As shown in the processing flow of FIG. 3, there are Loop1 that repeats Steps S101 to S106 and Loop2 that repeats Steps S101 to S109. In the following, loop 1 will be described for the (k + 1) th time and Loop2 will be described for the (i + 1) th time.

In FIG. 3, S101 is an L2 norm minimizing step for minimizing the square error. Specifically, the following equation (1) is calculated, and an estimation result image u ^{k + 1} is calculated.

Here, f ⁱ is the K space image updated by the previous iteration (Loop2 i), Φ is the observation process represented by Fourier transform and observation pattern, and Φ ^T is the inverse transform of Φ. The observation pattern is a process of undersampling data in the K space as shown in FIG. 4, for example. In FIG. 4, the white circle indicates the observation position of the data actually acquired.

I _N is a unit matrix in which all elements are 1, and is an array of the same size as f ⁱ . Further, u _c ^{^k,} u _w ^k sparse evaluation result component of the immediately preceding (Loop k _{^{_{th), b c k, b w}}} k is a variation component calculated immediately before (Loop1 k th). μ is a positive constant as a parameter. For example, S102 is an orthogonal transformation step using Wavelet, DCT, or Curvelet, S103 is an L1 norm minimization step for evaluating the sparseness of the coefficient after the orthogonal transformation, and S104 is an inverse transformation step of the orthogonal basis S102. Specifically, the steps so far are calculated by solving the optimization problems of the following equations (2) and (3).

Here, Ψ _c and Ψ _w are Curvelet transform and Wavelet transform, respectively. Ν _c ^{k + 1} and ν _w ^{k + 1} are the Curvelet coefficient and Wavelet coefficient, respectively, and Λ _c and Λ _w are constants as parameters. In this embodiment, Curvelet transform and Wavelet transform are used as orthogonal transform, but other than this may be used.

For the equations (2) and (3), any solution may be used, but in this embodiment, the soft shrinkage method is used. That is, by performing the Curvelet inverse transform and Wavelet inverse transform on both sides of Equations (2) and (3), Equations (4) and (5) are obtained below.

In Equations (4) and (5), Ψ _c ^T and Ψ _w ^T are a Curvelet inverse transform and a Wavelet inverse transform, respectively. S _c and S _w represents the soft shrinkage process. For S _c and S _w , the processing shown by the following equations (6) and (7) is performed for all elements.

Λ where λ is a constant as a parameter.

S105 is a fluctuation amount calculating step for calculating a difference from the image calculated in the immediately preceding loop. Specifically, the fluctuation amounts b _c ^{k + 1} and b _w ^{k + 1} are calculated using the following equations (8) and (9).

S106 is a condition step for determining whether or not the sparseness is equal to or higher than a predetermined level. Here, the sparseness may be defined at any level. For example, the fluctuation amount is equal to or lower than a predetermined threshold ( The condition is that there is almost no fluctuation and convergence). S107 is a threshold setting update step in the L2 norm minimizing S101 step and the L1 norm minimizing S103 step. Specifically, the values of the parameters μ and λ are updated. As described above, the signal processing unit 109 performs L2 norm minimization, L1 norm minimization, and threshold update in L2 norm minimization and L1 norm minimization as image reconstruction processing.

S108 is a step of performing Fourier transform on the estimation result image u ^{k + 1} calculated by the equation (1), updating data other than the actually acquired part based on the equation (10), and generating a reconstructed image. is there.

F where F is the Fourier transform.

The meaning of Equation (10) indicates that the pixel value at the observation position (white circle) in the observation pattern shown in Fig. 4 is copied as it is, and the other pixels are updated.

S109 is a condition step for determining whether the image quality of the reconstructed image is equal to or higher than a predetermined threshold. Here, the predetermined threshold value may be any value. For example, a condition of whether or not a predetermined number of loops has been turned can be considered. That is, the signal processing unit 109 determines whether or not the image quality of the obtained image satisfies a predetermined threshold value. As the predetermined threshold value, for example, it is possible to use the number of repetitions of the image reconstruction process. In addition, a difference value from the immediately preceding image in the image reconstruction process, an image dispersion value obtained by the signal processing unit 109, or the like can be used.

In the processing flow of Fig. 3, Loop1 corresponds to a process for increasing data sparseness, and Loop2 corresponds to a process for improving image restoration performance. The number of loops depends largely on the target data, but Loop1 is on the order of ~ 10 times and Loop2 is on the order of several hundred times. In general, when image reconstruction processing with the processing flow shown in Fig. 3 is performed, depending on the processing platform, it is expected that it will take several minutes to obtain the final estimated image output. The

Therefore, in the diagnostic image generation apparatus of the present embodiment, the processing flow as shown in FIGS. 5A and 5B is performed, and if the condition is met (Yes), an intermediate image is output, and the image processing unit 110 The intermediate image is displayed on the image display unit 111. 5A and 5B, the same processes as those in FIG. 3 are denoted by the same reference numerals as those in FIG. In the processing flow of FIG. 5A according to the present embodiment, if the result of the condition 2 for determining the end of Loop2 in S109 is No, condition 3 for performing another determination in S110 is further added. As the determination condition of S110, for example, a predetermined number of loops smaller than the number of loops of S109, a difference value from the previous image, or an estimated image result may be determined using a new evaluation index. Good. As an example of this evaluation index, threshold determination using a variance value of an estimated image can be considered as described above.

Alternatively, as shown in FIG. 5B, when the result of the condition 1 for determining the end of Loop1 is No, another determination condition S111 can be added. As a condition of S111, threshold determination may be performed based on data sparseness, for example, a number other than 0 of the Curvelet coefficient and Wavelet coefficient. That is, the image when it has a state having only a coefficient equal to or less than a predetermined threshold value is displayed in an intermediate manner. Several types of predetermined threshold values in the condition of S111 may be set and displayed step by step.

FIG. 6 is a diagram showing a display image of an intermediate image displayed on the image display unit 111 when the predetermined number of loops is set to 1, 10, 30, 50, 100 as the determination condition of S110 in FIG. 5A. . That is, the signal processing unit 109 outputs a plurality of images that satisfy each of the plurality of threshold values, and the image display unit 111 displays the plurality of images on the same screen. As described above, in the image generating apparatus according to the present embodiment, the intermediate image is displayed step by step, so that the waiting time until the display to the user can be reduced, and confirmation and determination according to each intermediate image step can be performed. It becomes possible. For example, as shown in FIG. 6, in the stage where the number of loops is small and the estimated image is blurred, the position of the imaging position is confirmed, the next part is confirmed whether a predetermined organ is reflected, and finally the tissue is confirmed. As a result, for example, when a part or the like cannot be confirmed at an intermediate stage, it is possible to perform an operation such as re-taking, and an improvement in the inspection workflow can be expected because there is no need to wait until the final image output.

As described above, according to the present embodiment, the configuration in which the intermediate image of the image reconstruction process is output reduces the waiting time until the user displays an image, and whether or not the photographing condition is correct is determined at an early stage. It can be recognized and judged, and the inspection workflow can be improved.

Next, as a second embodiment, a diagnostic image generation apparatus that allows a user to specify a region for an intermediate image of reconstruction processing will be described. More specifically, this embodiment receives a signal from a subject, images the received signal through image reconstruction processing, and determines whether the image quality satisfies a predetermined threshold, The signal processing unit includes an image display unit that displays, for each threshold, an image that is determined to satisfy a predetermined threshold by the processing unit, and a user interface unit that enables interactive input from the user. Example of an image diagnostic apparatus configured to set a predetermined area on an image in accordance with an input from a user interface unit with respect to an image displayed on the section, and to perform optimal image processing on the predetermined area set It is.

FIG. 7 is a diagram illustrating an example of functional blocks of the MRI apparatus according to the present embodiment. The MRI apparatus 700 according to the present embodiment basically has the same configuration as that of the first embodiment, but further includes a user interface (UI) unit 701 such as a touch panel to enable interactive input from the user. Hereinafter, the configuration of the present embodiment will be described focusing on the configuration different from the first embodiment.

In FIG. 7, a UI unit 701 is an interface that realizes user input such as a touch panel. Information on a region designated on the display image is sent to the image reconstruction processing unit 202 of the signal processing unit 109 via the control unit 112. It is sent as a control signal 205. The functional configuration of the signal processing unit 109 in FIG. 7 is the same as that in FIG. FIG. 8 is a processing flow of the image reconstruction processing unit 202 in the signal processing unit 109. In FIG. 8, the same processes as those in FIGS. 5A and 5B are denoted by the same reference numerals as those in FIG.

In FIG. 8, when the user designates an area via the UI unit 701 for the intermediate image output through step S110 (S201), the image reconstruction processing unit 202 sets the area according to this designation (S202), Loop1 processing is performed so that the L1 norm of the image area that has been set is minimized. As a region setting method, for example, a predetermined pixel area around a point designated on the intermediate image by the UI unit 701 is set. That is, the signal processing unit 108 sets a rectangular area for a predetermined pixel centered on a user input point on the touch panel as an area.

Steps S101 to S105 after the area specification are changed to processing for solving the optimization problem of the following equations (11) and (12).

Here, N is the designated region, and ν _cN and ν _wN are the Curvelet coefficient and Wavelet coefficient of the region N, respectively. When the Curvelet coefficient and Wavelet coefficient of the entire image are ν _cALL and ν _wALL , respectively, the relations of equations (13) and (14) are established.

That is, the optimization is performed so that the L1 norm in the designated area N is minimized, more specifically, the Curvelet coefficient and the Wavelet coefficient in the designated area are sparse. The signal processing unit 109 performs L1 norm minimization processing in the region as image reconstruction processing suitable for the set region.

In addition, when the soft shrinkage method is used as a method of solving equations (10) and (11), equations (15) and (16) are solved.

The processing of these formulas is omitted because it is the same as in the first embodiment, but the processing speed is expected to be improved because only the designated area N is processed.

FIG. 9 is a diagram showing an area designation process and a result image according to the second embodiment. By setting 903 a region N to be viewed in more detail in the intermediate image display 901, an image 902 in which the peripheral region 904 is finally restored with higher accuracy can be obtained. Regarding the designation method of the area N, the touch panel may not be assumed as shown in FIG. 9, but may be designated by, for example, a mouse, a keyboard, or a trackball. Also, a rectangular area or the like may be set directly instead of a single point.

In the processing flow of FIG. 8, since the reconstruction process is optimal for the area N, the reconstructed image of the other areas is not basically guaranteed. In the image 902 of FIG. 9, the image quality outside the designated area is shown as the intermediate image quality, but there is no particular reason.

As described above, according to this embodiment, by providing a user input interface, it is possible to specify an area to be viewed in more detail at the intermediate image display stage, thereby performing an optimal reconstruction process for the specified area. , It is possible to display a more suitable image.

In addition, according to the present embodiment, it is possible to perform optimum reconstruction processing in a specified area, and it is possible to improve the processing speed, and it is possible to contribute by improving the workflow by realizing the inspection time reduction.

FIG. 10 shows a modification of the signal processing unit 109 in the second embodiment. As shown in FIG. 10, in this embodiment, the image reconstruction processing unit 202 is equipped with two reconstruction processes in parallel. In FIG. 10, an image reconstruction process 1 to 1001 performs an optimum reconstruction process for the designated area designated by the control signal 205 in the second embodiment, and the image reconstruction process 2 to 1002 is an entire normal image. Perform reconfiguration processing. With this configuration, the output of the image processing reconstruction process 1 is displayed to improve the efficiency of the captured image judgment and the specified area optimization, and the entire image reconstruction process is performed in parallel with the image reconstruction process 2 1002. By implementing the above, it is possible to generate two types of images in one shooting, and to confirm the reconstructed image of the whole image later.

Also, two designated areas are designated by the control signal 205, and in the image reconstruction process 2 to 1002, optimization of the designated area different from the image reconstruction process 1 to 1001 is performed, and as shown in FIG. A configuration in which a peripheral area 1102 different from the peripheral area 904 is simultaneously displayed by setting different areas 1101 is also conceivable. That is, the signal processing unit 109 can perform image processing for each of a plurality of set areas in parallel. Furthermore, the configuration is not limited to two, and a configuration in which a plurality of designated area optimized images can be displayed by adopting a configuration in which a plurality of image reconstruction processes are operated in parallel is also conceivable.

In the third embodiment, a diagnostic image that makes it possible to predict the time and image quality until image display and provide information to the user based on the tendency of the database and the actual reconstructed image installed in the apparatus in advance. It is a generation device. In other words, the present embodiment is an image diagnostic apparatus, in which a received signal obtained from a subject is imaged by image reconstruction processing, and a signal processing unit that outputs an image and an image that displays an image output by the signal processing unit It is an Example of the image diagnostic apparatus of a structure provided with a display part and the memory | storage part which memorize | stores the calculation time information of the image reconstruction process in a signal processing part.

FIG. 12 is a diagram showing an example of functional blocks of the MRI apparatus of the present embodiment. The MRI apparatus 1200 of the present embodiment basically has the same configuration as that of the first embodiment, but further includes a storage device 1201 such as a memory for holding a database and an HDD. As the storage device 1201, the storage unit of the computer described above can be used. The storage device 1201 accessible from the signal processing unit 109 stores data of CS reconstruction processing in a typical imaging examination. Specifically, for example, as shown in FIG. 13, an image sample and a convergence curve model for reconstruction processing are stored in advance in the database 1301 for each imaging sequence, image type, and imaging region. That is, the storage device 1201 stores a convergence curve model of image reconstruction processing as calculation time information.

Fig. 14 shows a specific example of the convergence curve model. In each graph shown in FIG. 14, the horizontal axis represents the number of loops of reconstruction processing, the vertical axis represents the image quality index, and in this example, the Peak Signal to Noise Ratio (PSNR) value. As shown in the graphs of FIG. 14, the reconstruction process has almost no change in image quality after a predetermined number of repetitions, that is, there are

points

1401, 1402, and 1403 as threshold values for convergence. This threshold value generally differs depending on each imaging sequence and imaging region. That is, the shapes of the curves up to the convergence points 1401, 1402, and 1403 are different.

FIG. 15 shows an example of the configuration of the signal processing unit 109 of the present embodiment in FIG. 15, the same processes as those in FIG. 2 are denoted by the same reference numerals as those in FIG. The convergence curve prediction processing unit 1501 has a function of comparing the reconstruction processing result in the image reconstruction processing unit 202 with a database in the storage device 1201. That is, the signal processing unit 109 performs pattern matching processing between the convergence curve of the image reconstruction processing on the received signal and the convergence curve model stored in the storage device 1201, and the convergence with the highest matching is obtained from the result of the pattern matching processing. The curve model is the estimated convergence curve of the received signal, and the image display unit 111 displays the estimated convergence curve.

FIG. 16 shows an example of the processing flow of the image reconstruction processing unit 202 in FIG. FIG. 16 is basically the same as the configuration of FIG. 5A in the first embodiment, except that the determination condition 5 S301 outputs an intermediate image when the number of loops is less than a predetermined number. That is, it is configured to output an image at the initial loop stage of the reconstruction process. FIG. 17 shows an example of the functional configuration of the convergence curve prediction processing unit 1501 in FIG. In FIG. 17, the pattern matching processing unit 1701 calculates matching between the image of the reconstruction processing result obtained by the image reconstruction processing unit 202 and the convergence curve model shape in the database. The determination unit 1702 selects an estimated convergence curve from the processing result of the pattern matching processing unit 1701. The pattern matching processing unit 1701 performs a matching process of variance values between each imaging sequence and the convergence curve model for each part in the database 1301 and images up to a predetermined number of loops in imaging. Specifically, the cumulative difference value of the image dispersion values in each loop is calculated. When the image size is N pixels × M pixels, the image dispersion value is expressed by the following equations (17) and (18).

Here, Pi is each pixel value, E is a pixel average value, and Eσ ² is a variance value.

When the variance values of the intermediate image calculated by Equation (18) and the reference image in the database are EIσ ² and ERσ ² , respectively, and the predetermined number of loops is 10, the accumulated difference value Diff Eσ ² of the variance value is It is represented by (19). For the reference image, a variance value may be calculated in advance and stored in the storage device 1201 as one of data in the database 1301 in FIG. 13, for example.

Diff Eσ ² with all the references in the database is calculated, and the determination unit 1702 displays the convergence curve model of the reference image having the smallest value among them as an estimated convergence curve. In other words, the convergence curve tendency is predicted from the evaluation value (dispersion value) of the first few to several tens of intermediate images, and the convergence curve model that appears to be close is displayed.

FIG. 18 (a) shows an example of a display image on the screen of the image display unit 111 of the apparatus of this embodiment. Thus, since the time until image display can be roughly grasped, for example, an inquiry may be displayed as shown in FIG. 18B, and the user may select whether or not to continue the process.

According to the present embodiment, it is possible to intuitively know the prediction of the reconstruction processing time by displaying the estimated convergence curve, and the user can grasp the inspection time in advance.

According to the present invention described in detail above, the image obtained by the reconstruction process can be evaluated and judged in advance, and it can be confirmed at an early stage whether or not the optimum imaging for clinical use has been performed. The workflow can be improved.

In addition, this invention is not limited to the above-mentioned 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 a certain embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of a certain embodiment. In addition, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment.

In addition, the above-described configuration, function, processing unit, etc. have been described as an example of creating a program that realizes a part or all of them with a CPU. Needless to say, it may be realized by hardware.

100 MRI apparatus 101 Subject 102 Magnet 103 Gradient magnetic field coil 104 RF coil 105 RF coil 106 Gradient magnetic field power source 107 RF transmission unit 108 Signal detection unit 109 Signal processing unit 110 Image processing unit 111 Image display unit 112 Control unit 201 Inverse Fourier transform unit 202 Image reconstruction processing unit 203 Image composition processing unit 204 Filtering processing unit 701 UI unit 901 Intermediate image display 902 Restored image 903 Setting area 904 Peripheral area 1001 Image reconstruction process 1
1002 Image reconstruction process 2
1200 MRI device 1201 Storage device 1301 Database 1501 Convergence curve prediction processing 1701 Pattern matching processing unit 1702 Determination unit

Claims

A diagnostic imaging device,
A signal processing unit that images a received signal obtained from a subject by image reconstruction processing and determines whether the image quality of the obtained image satisfies a predetermined threshold;
An image display unit that displays, for each of the threshold values, the image determined to satisfy the threshold value by the signal processing unit,
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 1,
The threshold value is the number of repetitions of the image reconstruction process in the signal processing unit.
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 1,
The threshold value is a difference value from the image of the immediately preceding process in the image reconstruction process of the signal processing unit.
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 1,
The threshold value is a variance value of the image obtained by the signal processing unit.
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 1,
The signal processing unit outputs a plurality of images satisfying each of the plurality of threshold values, and the image display unit displays the plurality of images on the same screen.
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 1,
The signal processing unit performs L2 norm minimization, L1 norm minimization, and L2 norm minimization, threshold updating in the L1 norm minimization, as the image reconstruction process.
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 1,
A user interface that enables interactive input,
The signal processing unit sets an area on the image in response to an input from the user interface unit with respect to the image displayed on the image display unit, and executes an image reconstruction process suitable for the set area Do,
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 7,
The user interface unit is a touch panel, and the signal processing unit sets a rectangular area for a predetermined pixel centered on a user input point by the touch panel as the area.
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 7,
The signal processing unit performs L1 norm minimization processing in the region as the image reconstruction processing suitable for the set region.
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 7,
The signal processing unit processes image processing for each of the plurality of set regions in parallel.
An image diagnostic apparatus characterized by that.
A diagnostic imaging device,
A signal processing unit that images a received signal obtained from a subject by image reconstruction processing and outputs an image;
An image display unit for displaying the image output by the signal processing unit;
A storage unit for storing calculation time information of the image reconstruction processing in the signal processing unit,
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 11,
The calculation time information stored in the storage unit is a convergence curve model of the image reconstruction processing by the signal processing unit,
An image diagnostic apparatus characterized by that.
The diagnostic imaging apparatus according to claim 11,
The signal processing unit performs a pattern matching process between the convergence curve of the image reconstruction process for the received signal and the convergence curve model stored in the storage unit, and the most matching result is obtained from the result of the pattern matching process. The high convergence curve model is the estimated convergence curve of the received signal, and the image display unit displays the estimated convergence curve.
An image diagnostic apparatus characterized by that.
An image generation method in an image diagnostic apparatus,
The diagnostic imaging apparatus includes:
The received signal obtained from the subject is imaged by an image reconstruction method, and it is determined whether the image quality of the obtained image satisfies a predetermined threshold,
Displaying the image determined to satisfy the predetermined threshold on the image display unit for each threshold;
An image generation method characterized by the above.
15. The image generation method according to claim 14, wherein
The diagnostic imaging apparatus includes:
A region is set on the image in response to an input from the user interface unit that enables interactive input with respect to the image displayed on the image display unit, and an image reconstruction process suitable for the set region is executed. Do,
An image generation method characterized by the above.