JP2017221273A - Image processing device and magnetic resonance imaging device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an image processing device that can precisely calculate an evaluation value relating to a joint, and to provide a magnetic resonance imaging device that can precisely calculate an evaluation value relating to a joint.SOLUTION: An image processing device includes an acquisition part and an analysis part. The acquisition part acquires three-dimensional data including a joint of a subject collected by magnetic resonance imaging. The analysis part analyzes three-dimensional data in a three-dimensional space and calculates an evaluation value relating to the joint of the subject.SELECTED DRAWING: Figure 1

Description

  Embodiments described herein relate generally to an image processing apparatus and a magnetic resonance imaging apparatus.

  Various types of medical images generated by an image diagnostic apparatus are used to diagnose a joint by a doctor. For example, before performing an operation to reconstruct a torn rotator cuff, a doctor confirms a medical image that includes a torn rotator cuff (a medical image in which a torn rotator cuff is depicted), and the prognosis after the operation. Diagnosis that predicts

Takashi Sashi et al., “MRI Second Edition of Shoulder Joint”, Medical View, p79 column Hiroshi Iwabuchi, et al., “Study of optimal imaging method for measurement of shoulder rotator cuff cross section using MRI”, Vol.29, 263-267 Azusa Takamiya et al., “Relationship between degree of steatosis and improvement of JOA score in patients with rotator cuff tears: ~ Study using fatty degeneration assessment from MRI ~", 48th Annual Meeting of the Japanese Physical Therapy Association (Nagoya) ) ▲ crane ▼ Daisaku Tadashi et al., “Relationship between clinical image, MRI findings, and histopathology in patients with rotator cuff tear”, Yamagata Medical 2014 Gerber C et al., “Correlation of atrophy and fatty infiltration on strength and integrity of rotator cuff repairs: a study in thirteen patients”, J Shoulder Joint Surg, 2007; 16: p. 1973-1982 Goutallier D et al., “Influence of cuff muscle fatty degeneration on anatomic and functional outcomes after simple suture of full-thickness tears”, J Shoulder Elbow Surg, 2003; 12: p.550-554 Murarishiyuki et al., “Is it possible to predict preoperatively whether temporary repair for rotator cuff tear is preoperative?” Shoulder joint, 1999, 23, p.391-395 Katsuhiro Hori et al., "Frequency of fatty degeneration of the torn muscle in complete rotator cuff tears-Observation by CT images", Shoulder joint, 2003, 27, p.455-458 Wada Masahiro et al., "MRI Evaluation of the Splenic Muscle Abdomen after Ruptured Shoulder Rotator Cuff", Shoulder Joint, 2001, 25, p.231-234 Taki Nozaki et al., “Quantitative evaluation of supraspinatus steatosis (infiltration) by MRI using Dixon method”, shoulder joint, 2013, 37, p.455-459

  The problem to be solved by the present invention is to provide an image processing apparatus and a magnetic resonance imaging apparatus capable of accurately calculating an evaluation value related to a joint.

  The image processing apparatus according to the embodiment includes an acquisition unit and an analysis unit. The acquisition unit acquires three-dimensional data including the joint of the subject collected by magnetic resonance imaging. The analysis unit analyzes the three-dimensional data in the three-dimensional space and calculates an evaluation value related to the joint of the subject.

FIG. 1 is a diagram illustrating a configuration example of an image processing apparatus according to the first embodiment. FIG. 2 is a diagram for explaining an example of a measurement ROI setting method. FIG. 3A is a schematic diagram for explaining an example of technical significance when a relative ratio is calculated as an evaluation value. FIG. 3B is a schematic diagram for explaining an example of technical significance when a relative ratio is calculated as an evaluation value. FIG. 4 is a flowchart illustrating a processing procedure of analysis processing performed by the image processing apparatus according to the first embodiment. FIG. 5 is a diagram for explaining an example of processing executed by the analysis function according to the first modification of the first embodiment. FIG. 6 is a flowchart illustrating a processing procedure of analysis processing performed by the image processing apparatus according to the second embodiment. FIG. 7 is a diagram illustrating a configuration example of an MRI apparatus according to the third embodiment.

  Hereinafter, embodiments and modifications of an image processing apparatus and a magnetic resonance imaging (MRI) apparatus will be described in detail with reference to the accompanying drawings. In addition, each embodiment and each modification can be combined suitably.

(First embodiment)
FIG. 1 is a diagram illustrating a configuration example of an image processing apparatus according to the first embodiment. For example, as illustrated in FIG. 1, an image processing apparatus 300 according to the present embodiment is connected to an MRI apparatus 100 that is an example of an image diagnostic apparatus and an image storage apparatus 200 via a network 50. The image processing apparatus 300 may be further connected to another image diagnostic apparatus such as an X-ray CT (Computed Tomography) apparatus, an ultrasonic diagnostic apparatus, or a PET (Positron Emission Tomography) apparatus via the network 50. .

  The MRI apparatus 100 collects image data of a subject using a magnetic resonance phenomenon. That is, the MRI apparatus 100 collects image data of a subject by magnetic resonance imaging. To explain with a specific example, the MRI apparatus 100 collects magnetic resonance data from a subject by executing various imaging sequences based on imaging conditions set by an operator. The MRI apparatus 100 generates two-dimensional or three-dimensional image data by performing image processing such as Fourier transform processing on the collected magnetic resonance data. For example, the MRI apparatus 100 includes a T2W (T2 Weighted) image including the shoulder joint of a subject whose shoulder rotator cuff has been torn and the entire supraspinatus muscle connected to the torn rotator cuff, and a subject to which the shoulder rotator cuff has been torn. Three-dimensional image data of a morphological image such as a T1 image including the entire supraspinatus muscle connected to the shoulder joint of the subject and the torn rotator cuff is generated. In addition, the MRI apparatus 100 collects magnetic resonance data by the water-fat separation method, and from the collected magnetic resonance data, the shoulder joint of the subject to which the shoulder rotator cuff has been torn and the supraspinae connected to the torn rotator cuff. Three-dimensional fat image data including the entire muscle is generated.

  The image storage device 200 stores image data collected by various image diagnostic devices. For example, the image storage apparatus 200 acquires image data from the MRI apparatus 100 via the network 50 and stores the acquired image data in a storage circuit provided inside or outside the apparatus. For example, the image storage device 200 is realized by a computer device such as a server device.

  The image processing apparatus 300 processes image data collected by various image diagnostic apparatuses. For example, the image processing apparatus 300 acquires image data from the MRI apparatus 100 or the image storage apparatus 200 via the network 50 and stores it in a storage circuit provided inside or outside the apparatus. The image processing apparatus 300 performs various image processes on the acquired image data, and outputs the image data before the image processing or after the image processing to a display or the like.

  For example, as illustrated in FIG. 1, the image processing apparatus 300 includes an I / F (interface) circuit 310, a storage circuit 320, an input circuit 330, a display 340, and a processing circuit 350.

  The I / F circuit 310 controls transmission and communication of various data transmitted and received between the image processing apparatus 300 and other apparatuses connected via the network 50. For example, the I / F circuit 310 is realized by a network card, a network adapter, a NIC (Network Interface Controller), or the like. For example, the I / F circuit 310 is connected to the processing circuit 350, converts the image data output from the processing circuit 350 into a format conforming to a predetermined communication protocol, and transmits the data to the MRI apparatus 100 or the image storage apparatus 200. Further, the I / F circuit 310 outputs image data received from the MRI apparatus 100 or the image storage apparatus 200 to the processing circuit 350.

  The storage circuit 320 stores various data. For example, the storage circuit 320 is realized by a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, a hard disk, an optical disk, or the like. For example, the storage circuit 320 is connected to the processing circuit 350 and stores the input image data or outputs the stored image data to the processing circuit 350 in accordance with a command sent from the processing circuit 350.

  The storage circuit 320 stores a volume database 320a and a JOA score database 320B. In the volume database 320a, an average value of the volume of the supraspinatus that has not undergone atrophy obtained by clinical practice such as experiments and research is registered in advance. In the JOA score database 320B, a JOA score corresponding to a combination of a muscle atrophy degree and a fatty degeneration ratio is registered in advance for each muscle atrophy degree and a fatty degeneration ratio described later.

  The input circuit 330 accepts various instructions and various information input operations from the operator. For example, the input circuit 330 is realized by a trackball, a switch button, a mouse, a keyboard, a touch panel, or the like. For example, the input circuit 330 is connected to the processing circuit 350, converts an input operation received from the operator into an electrical signal, and outputs the electrical signal to the processing circuit 350.

  The display 340 displays various information and various images. For example, the display 340 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, a touch panel, or the like. For example, the display 340 is connected to the processing circuit 350 and displays images in various formats based on image data output from the processing circuit 350.

  The processing circuit 350 controls each component included in the image processing device 300 in accordance with an input operation output from the input circuit 330. For example, the processing circuit 350 is realized by a processor. For example, the processing circuit 350 inputs the image data output from the I / F circuit 310 to the storage circuit 320. Further, the processing circuit 350 outputs the image data output from the storage circuit 320 to the display 340.

  Under such a configuration, the image processing apparatus 300 according to the present embodiment is operated by, for example, a doctor before an operation for reconstructing a torn shoulder rotator cuff (shoulder rotator cuff tear reconstruction operation) is performed. It is used to diagnose the possibility of reconstruction and postoperative prognosis.

  By the way, arthrography has long been performed as a method for diagnosing a torn rotator cuff, but in recent years it has become possible to evaluate the size, localization and morphology of tears by MRI.

  Whether or not the remaining supraspinatus, subspinous muscle, and tendon can cover the head with sufficient strength before performing the rotator cuff tear reconstruction, and whether the range of motion of the joint can be maintained sufficiently Is predicted in advance by the doctor. Then, based on the prediction result, the doctor determines whether or not the rotator cuff can be reconstructed. Here, whether or not reconstruction is possible needs to be comprehensively evaluated based on age, duration from onset, systemic flexibility, muscle strength, tear size, etc., and there is a treatment outcome evaluation standard (JOA score) as one of the indicators To do.

  Here, a case where the MRI apparatus calculates a muscle atrophy degree indicating a degree of atrophy of the supraspinatus due to the rotator cuff tear as an evaluation value used when obtaining the JOA score will be described. In this case, for example, the MRI apparatus calculates the ratio of the thickness of the supraspinatus muscle connected to the torn rotator cuff and the thickness of the supraspinatus muscle connected to the normal rotator cuff that has not been torn, by muscle atrophy. Calculate as degrees. Here, when the MRI apparatus calculates the thickness of the supraspinatus, the doctor inserts the supraspinous muscle thickness at a location considered to be the maximum diameter of the supraspinatus muscle abdominal on a two-dimensional image including the shoulder. A measurement ROI for measuring the height is set. FIG. 2 is a diagram for explaining an example of a measurement ROI setting method. As shown in the example of FIG. 2, for example, the doctor sets the measurement ROI 22 on the two-dimensional image 20 at a position considered to be the maximum diameter of the supraspinatus muscle. In the image 20, a region 21 is a rotator cuff tear.

  However, due to the discretion of the doctor, for example, depending on the doctor, the measurement ROI may be set at a location that is not the maximum diameter of the supraspinatus. In this case, since the MRI apparatus measures a length other than the maximum diameter as the thickness of the supraspinatus, the thickness of the supraspinatus is smaller than the maximum diameter, and the thickness of the supraspinatus is accurately determined. It cannot be calculated. As a result, the degree of supraspinatus atrophy indicated by the calculated degree of muscular atrophy increases, and as a result of overestimating the atrophy of the supraspinatus, it is impossible to accurately determine whether reconstruction is possible. To do. In particular, when the rotator cuff tears, the supraspinatus connected to the rotator cuff is pulled by losing the stop, and the position of the maximum diameter of the muscular abdomen shifts from the original position. It is not easy for a doctor to specify the position of the maximum diameter of the muscle belly. Another problem is that it is difficult for a doctor to determine which cross-section of a three-dimensional region including the shoulder should be an optimal cross-section of a two-dimensional image used when calculating an evaluation value.

  Therefore, the image processing apparatus 300 according to the present embodiment can accurately calculate the evaluation value related to the joint without performing a difficult task of determining the optimum cross section for calculating the evaluation value to the doctor. It is configured.

  For example, the processing circuit 350 has an acquisition function 351 and an analysis function 352. The acquisition function 351 is an example of an acquisition unit in the claims. The analysis function 352 is an example of an analysis unit in the claims.

  The acquisition function 351 acquires three-dimensional data including the joint of the subject collected by magnetic resonance imaging. Specifically, the acquisition function 351 is a three-dimensional image including the entire supraspinatus muscle connected to the shoulder joint of the subject and the torn rotator cuff from the MRI apparatus 100 or the image storage apparatus 200 via the network 50. Get the data. The acquisition function 351 causes the storage circuit 320 to store the acquired three-dimensional image data.

  Here, the three-dimensional image data acquired by the acquisition function 351 may include both data collected in a 3D sequence and multi-slice data collected in a 2D sequence.

  The analysis function 352 analyzes the three-dimensional data in the three-dimensional space and calculates an evaluation value related to the joint of the subject. For example, the analysis function 352 acquires 3D image data stored in the storage circuit 320. The analysis function 352 analyzes the acquired three-dimensional image data as it is in three dimensions, and is connected to the torn rotator cuff, the volume of the three-dimensional region of the supraspinatus connected to the torn rotator cuff. The volume of a three-dimensional region in which degeneration into fat occurs in the supraspinatus is calculated. The analysis function 352 then calculates the volume of the three-dimensional region of the supraspinatus connected to the torn rotator cuff and the three-dimensional region of the supraspinatus muscle connected to the normal rotator cuff that is not torn. The ratio with the volume is calculated as the degree of muscle atrophy. The analysis function 352 also calculates the ratio of the volume of the three-dimensional region of the supraspinatus connected to the torn rotator cuff and the volume of the three-dimensional region where degeneration to fat has occurred as a fat degeneration ratio. calculate. Note that the degree of muscle atrophy and the rate of fatty degeneration are examples of evaluation values related to the joint of the subject. That is, the analysis function 352 calculates the evaluation value without causing the doctor to set the measurement ROI. Thus, the analysis function 352 according to the first embodiment calculates the evaluation value regardless of the doctor's arbitraryness. Therefore, according to the first embodiment, the evaluation value can be calculated with high accuracy.

  Here, the analysis function 352 according to the first embodiment does not calculate an absolute numerical value such as a tissue length as an evaluation value from the three-dimensional image data, but calculates a relative ratio as an evaluation value. To do. This technical significance will be described. FIG. 3A and FIG. 3B are schematic diagrams for explaining an example of technical significance when a relative ratio is calculated as an evaluation value. FIG. 3A is a schematic diagram showing an example of the form of the supraspinatus 30 when the subject's body posture is such that the supraspinatus does not extend and the supraspinatus has a relatively small diameter. . FIG. 3B is a schematic diagram showing an example of the form of the supraspinatus 30 when the subject's posture is such that the supraspinatus extends and the supraspinatus has a relatively small diameter. Has been.

  3A and 3B show a region 30a denatured into fat. As shown in the example of FIGS. 3A and 3B, the size of the supraspinatus 30 may differ depending on the body position of the subject even though the supraspinous muscle 30 is the same supraspinous muscle. Similarly, as shown in the examples of FIGS. 3A and 3B, the size of the region 30a may differ depending on the posture of the subject, even though the region 30a is the same region denatured to fat. Here, when the size of the region 30a is simply calculated as the evaluation value, as described above, the size varies depending on the posture of the subject, and thus the evaluation value cannot be calculated uniquely. Therefore, in this case, it is difficult to accurately calculate the evaluation value.

  Here, as shown in the examples of FIGS. 3A and 3B, it is considered that even when the posture of the subject is changed, the supraspinatus 30 and the region 30a denatured into fat are deformed at substantially the same rate. Therefore, even if the posture of the subject changes, the ratio between the volume of the supraspinatus 30 and the volume of the region 30a is considered to be substantially constant. Therefore, as described above, the analysis function 352 according to the present embodiment is configured so that the volume of the three-dimensional region of the supraspinatus connected to the torn rotator cuff and the three-dimensional region in which degeneration into fat has occurred. The ratio to the volume is calculated as the fatty denaturation ratio. That is, the analysis function 352 can suppress the influence on the evaluation value due to the posture of the subject, and can uniquely calculate the evaluation value that does not depend on the posture of the subject. Therefore, according to the present embodiment, the evaluation value can be calculated with high accuracy.

  In addition, as described above, the analysis function 352 according to the present embodiment calculates an evaluation value using the three-dimensional image data including the entire supraspinatus muscle connected to the torn rotator cuff. Therefore, according to the present embodiment, the evaluation value can be calculated without performing a difficult task of determining the optimum cross-section for calculating the evaluation value to the doctor. Therefore, according to this embodiment, the effort by the doctor in diagnosis can be reduced.

  The processing functions of the processing circuit 350 have been described above. Here, for example, each processing function described above is stored in the storage circuit 320 in the form of a program executable by a computer. The processing circuit 350 reads each program from the storage circuit 320 and executes each read program, thereby realizing a processing function corresponding to each program. In other words, the processing circuit 350 in a state where each program is read out has each processing function shown in FIG.

  In addition, although an example in which each processing function of the acquisition function 351 and the analysis function 352 is realized by a single processing circuit 350 in FIG. 1, the embodiment is not limited thereto. For example, the processing circuit 350 may be configured by combining a plurality of independent processors, and each processor may implement each processing function by executing each program. In addition, each processing function of the processing circuit 350 may be realized by being appropriately distributed or integrated into a single or a plurality of processing circuits.

  FIG. 4 is a flowchart illustrating a processing procedure of analysis processing performed by the image processing apparatus according to the first embodiment.

  For example, as shown in FIG. 4, in the image processing apparatus 300, the acquisition function 351 acquires three-dimensional image data including the shoulder joint of the subject from the MRI apparatus 100 or the image storage apparatus 200 (step S101). This step S101 is realized, for example, when the processing circuit 350 calls and executes a predetermined program corresponding to the acquisition function 351 from the storage circuit 320. In the present embodiment, the acquisition function 351 is 3D T2W image data collected in a 3D sequence as 3D image data, and the shoulder joint of the subject to which the rotator cuff has been torn and the rotator cuff torn. A case will be described in which three-dimensional T2W image data including the entire supraspinatus connected to is acquired. Since the three-dimensional T2W image data includes the entire supraspinatus, the maximum abdominal surface of the supraspinatus is included. Moreover, since the supraspinatus included in the three-dimensional T2W image data is connected to the torn rotator cuff, there is a possibility that the movement is less than that before the rotator cuff is ripped and atrophy. .

  Subsequently, the analysis function 352 performs a threshold process on the three-dimensional T2W image data, thereby converting the region denatured into fat (region where degeneration into fat occurs: fat denatured region) and not denatured into fat. Then, a segmentation process is performed to extract the tissue region (atrophic muscle tissue region) of the supraspinatus that may be atrophic (step S102). In this segmentation process, a spatially continuous region having a luminance equal to or higher than the threshold is extracted as a fatty degeneration region, and a spatially continuous region having a luminance lower than the threshold is extracted as an atrophic muscle tissue region. . The fatty degeneration region is, for example, a region indicated by a high signal linear image. The fatty degeneration region and the atrophic muscle tissue region are examples of the region of interest described in the claims.

  Subsequently, the analysis function 352 calculates the volume of the fatty degeneration region extracted in step S102 (step S103). Subsequently, the analysis function 352 calculates the volume of the atrophic muscle tissue region extracted in step S102 (step S104).

  Subsequently, the analysis function 352 calculates a fat degeneration ratio that is a ratio between the volume of the fatty degeneration region calculated in step S103 and the volume of the atrophy muscle tissue region calculated in step S104 (step S105). That is, the analysis function 352 calculates the fatty degeneration ratio by comparing the volume of the fatty degeneration region calculated in step S103 with the volume of the atrophic muscle tissue region calculated in step S104. For example, the analysis function 352 calculates a value obtained by dividing the volume of the fatty degeneration region by the volume of the atrophic muscle tissue region as the fat degeneration ratio. Such a fatty degeneration ratio indicates a ratio of the volume of the steatosis region to the volume of the atrophic muscle tissue region.

  Subsequently, the analysis function 352 acquires the average value of the volume of the supraspinatus that has not been atrophy obtained clinically from the volume database 320a, and the volume of the atrophy muscle tissue region calculated in step S104 and the acquired spine The degree of muscle atrophy, which is a ratio with the average value of the volume of the upper muscle, is calculated (step S106). That is, the analysis function 352 calculates the degree of muscle atrophy by comparing the volume of the atrophied muscle tissue region calculated in step S104 with the acquired average value of the volume of the supraspinatus muscle. For example, the analysis function 352 calculates a value obtained by dividing the volume of the atrophic muscle tissue region by the average value of the volume of the supraspinatus that has not undergone atrophy as the degree of muscle atrophy. The degree of muscle atrophy indicates the ratio of the average value of the volume of the supraspinatus that has not undergone atrophy to the volume of the atrophic muscle tissue region.

  Subsequently, the analysis function 352 acquires the JOA score corresponding to the combination of the fatty degeneration ratio calculated in step S105 and the muscle atrophy calculated in step S106 from the JOA score database 320B, and uses the acquired JOA score. Then, it is determined whether or not reconstruction is possible (step S107), and the processing of the analysis method is terminated. For example, in step S107, the analysis function 352 determines that the rotator cuff can be reconstructed in the operation when the JOA score is a predetermined value or more. In step S107, if the JOA score is less than the predetermined value, the analysis function 352 determines that the rotator cuff cannot be reconstructed in the operation. In this manner, in step S107, the analysis function 352 uses the evaluation value to determine a predetermined evaluation that indicates whether or not the rotator cuff can be reconstructed. In step S107, the analysis function 352 causes the display 340 to display the determination result. Thereby, the doctor who is a user can grasp | ascertain the determination result before an operation. Steps S102 to S107 described above are realized, for example, by the processing circuit 350 calling and executing a predetermined program corresponding to the analysis function 352 from the storage circuit 320.

  The image processing apparatus 300 according to the first embodiment has been described above. As described above, according to the image processing apparatus 300 according to the first embodiment, it is possible to accurately calculate an evaluation value related to a joint.

(First modification of the first embodiment)
In the first embodiment described above, the image processing apparatus 300 acquires the three-dimensional T2W image data collected in the 3D sequence in step S101, and performs threshold processing on the three-dimensional T2W image data in step S102. As described above, the fatty degeneration region and the atrophic muscle tissue region are extracted, the volume of the fatty degeneration region is calculated in step S103, and the volume of the atrophy muscle tissue region is calculated in step S104. However, the image processing apparatus 300 may calculate the volume of the steatosis region and the volume of the atrophic muscle tissue region by other methods. Thus, such an embodiment will be described as a first modification of the first embodiment.

  In step S101, the acquisition function 351 of the image processing device 300 according to the first modification acquires multi-slice data collected in a 2D sequence as three-dimensional image data.

  Then, in step S102, the analysis function 352 of the image processing apparatus 300 according to the first modified example uses pixels as the horizontal axis for each of the plurality of slice image data included in the multi-slice data acquired in step S101. A histogram is generated with the luminance of the pixel and the frequency on the vertical axis indicating the number of pixels having the same luminance.

  FIG. 5 is a diagram for explaining an example of processing executed by the analysis function according to the first modification of the first embodiment. As shown in the example of FIG. 5, the analysis function 352 according to the first modification generates a histogram for each of a plurality (N) of slice image data in step S102. That is, the analysis function 352 generates N histograms.

  In step S102, the analysis function 352 generates a histogram representing the relationship between the luminance and the frequency in the three-dimensional space from the histogram generated for each of the plurality of slice image data. For example, as shown in the example of FIG. 5, the sum of frequencies in N histograms is calculated for each luminance, and a histogram representing the total frequency for each luminance is generated as a histogram 51 in a three-dimensional space. .

  Then, in step S102, the analysis function 352 specifies a peak corresponding to the fatty degeneration region and a peak corresponding to the atrophic muscle tissue region in the luminance distribution indicated by the histogram representing the relationship between the luminance and the frequency in the three-dimensional space. . For example, as illustrated in the example of FIG. 5, the analysis function 352 specifies a peak 52 corresponding to the fatty degeneration region and a peak 53 corresponding to the atrophic muscle tissue region in the luminance distribution.

  Then, in step S102, the analysis function 352 applies a normal distribution curve to the predetermined range including the peak corresponding to the identified fat degeneration region in the luminance distribution indicated by the histogram in the three-dimensional space, and the fat degeneration region. A normal distribution curve corresponding to is obtained. Similarly, in step S102, the analysis function 352 applies a normal distribution curve to a predetermined range including a peak corresponding to the specified atrophic muscle tissue region in the luminance distribution indicated by the histogram in the three-dimensional space, and performs atrophy. A normal distribution curve corresponding to the muscle tissue region is obtained. For example, as shown in the example of FIG. 5, the analysis function 352 applies a normal distribution curve to a predetermined range including the peak 52 to obtain a normal distribution curve 54 corresponding to the fatty degeneration region. As shown in the example of FIG. 5, the analysis function 352 applies a normal distribution curve to a predetermined range including the peak 53 to obtain a normal distribution curve 55 corresponding to the atrophic muscle tissue region.

  Then, in step S103, the analysis function 352 assumes that the maximum value of the frequency in the normal distribution curve corresponding to the fatty degeneration region is fmax1, and the minimum luminance x1 when the frequency is fmax1 / 2 in the normal distribution curve. And the maximum luminance x2. Then, in step S103, the analysis function 352 calculates the sum of the frequencies corresponding to each luminance in the range where the luminance is not less than x1 and not more than x2 (that is, the range corresponding to the full width at half maximum) as the frequency corresponding to the fatty degeneration region. . In step S103, the analysis function 352 multiplies the frequency corresponding to the fatty degeneration region by a predetermined volume value corresponding to one frequency to calculate the volume corresponding to the fatty degeneration region.

  The analysis function 352 also sets the minimum luminance x3 when the frequency is fmax2 / 2 in the normal distribution curve when the maximum frequency value in the normal distribution curve corresponding to the atrophic muscle tissue region is set to fmax2 in step S104. And the maximum luminance x4. Then, in step S104, the analysis function 352 calculates the sum of the frequencies corresponding to each luminance in the range where the luminance is not less than x3 and not more than x4 (that is, the range corresponding to the full width at half maximum) as the frequency corresponding to the atrophic muscle tissue region. To do. In step S104, the analysis function 352 multiplies the frequency corresponding to the atrophied muscle tissue region by a predetermined volume value corresponding to one frequency, and calculates the volume corresponding to the atrophic muscle tissue region. Each process after step S105 is the same as that of the first embodiment. That is, the analysis function 352 according to the first modification example calculates an evaluation value using a histogram in a three-dimensional space.

  In the first modification, the acquisition function 351 may acquire the 3D image data collected in the 3D sequence in step S101. In this case, the analysis function 352 may generate a histogram for each slice encoding of the three-dimensional image data in step S102. Then, the analysis function 352 may perform processing similar to the processing described above to generate a histogram in the three-dimensional space, and calculate the evaluation value using the histogram in the three-dimensional space.

  In the first modification, the analysis function 352 may update the threshold used for the threshold processing described above. For example, in the first modification, the analysis function 352 intersects the normal distribution curve corresponding to the fatty degeneration region obtained in step S102 and the normal distribution curve corresponding to the atrophic muscle tissue region obtained in step S102. It is good also considering the brightness | luminance corresponding to as a threshold value used for a threshold value process. That is, the threshold used for threshold processing is updated by two normal distribution curves. Then, the analysis function 352 according to the first modified example performs a threshold process on the three-dimensional image data acquired in step S101 in a spatially continuous manner as in the first embodiment described above. A segmentation process may be performed in which a region having a luminance equal to or higher than a certain threshold is set as a fatty degeneration region and a region having a luminance lower than the threshold that is spatially continuous is extracted as an atrophy muscle tissue region.

(Second modification of the first embodiment)
In the first embodiment, the case has been described in which the image processing apparatus 300 uses the average value of the volume of the supraspinatus obtained from the volume database 320a when calculating the degree of muscle atrophy in step S106. However, when calculating the degree of muscle atrophy, the image processing apparatus 300 calculates the volume of the supraspinatus muscle connected to the ruptured rotator cuff of the subject whose muscle atrophy is to be calculated. The volume of the superior muscle may be used instead of the average value of the volume of the supraspinatus obtained from the volume database 320a. Thus, such an embodiment will be described as a second modification of the first embodiment.

  In step S101, the acquisition function 351 of the image processing apparatus 300 according to the second modification is connected to the shoulder joint of the subject to which the rotator cuff has been torn and the torn rotator cuff, as in the first embodiment. Three-dimensional T2W image data including the entire supraspinatus is acquired. In addition, in step S101, the acquisition function 351 according to the second modification acquires three-dimensional T2W image data including the entire supraspinatus muscle connected to the rotator cuff of the same subject. To do. Here, in the subject, the torn rotator cuff and the unrotated rotator cuff have objectivity. For example, the rotator cuff on the right shoulder and the rotator cuff on the left shoulder are located symmetrically in the human subject. Therefore, if the rotator cuff on the right shoulder is torn and the rotator cuff on the left shoulder is not torn, and if the rotator cuff on the left shoulder is torn and the rotator cuff on the right shoulder is not torn, The torn rotator cuff and the unrotated rotator cuff have objectivity.

  In step S102, the analysis function 352 of the image processing apparatus 300 according to the second modification performs the same processing as that of the first embodiment, and in addition, the spines connected to the unrotated rotator cuff Threshold processing is performed on the three-dimensional T2W image data including the entire upper muscle, and the region of the supraspinatus is extracted. In step S104, the analysis function 352 performs processing similar to that in the first embodiment, and in addition, calculates the volume of the region of the supraspinatus muscle connected to the ruptured rotator cuff extracted in step S102. calculate. In step S106, the analysis function 352 compares the volume of the atrophy muscle tissue region with the volume of the region of the supraspinatus muscle connected to the ruptured rotator cuff calculated in step S104. calculate. For example, in step S106, the analysis function 352 divides the volume of the atrophic muscle tissue region by the volume of the region of the supraspinatus muscle connected to the ruptured rotator cuff calculated in step S104 as the muscle atrophy degree. calculate.

  According to the second modification, the volume of the supraspinatus connected to the rotator cuff and the volume of the supraspinatus connected to the rotator cuff that is not torn are used in the same subject. Therefore, the degree of muscle atrophy can be calculated with high accuracy.

(Third Modification Example According to First Embodiment)
In the first embodiment, the first modified example, and the second modified example described above, the image processing apparatus 300 acquires three-dimensional T2W image data in step S101, and after step S102, the three-dimensional T2W image. The case where an evaluation value is calculated using data has been described. However, the image processing apparatus 300 may calculate the evaluation value by performing the same processing using three-dimensional image data of a type other than the three-dimensional T2W image data. Therefore, such an embodiment will be described as a third modification of the first embodiment.

  For example, the acquisition function 351 of the image processing apparatus 300 according to the third modified example acquires three-dimensional T1 image data in step S101, and the analysis function 352 uses the three-dimensional T1 image data to evaluate the evaluation value. May be calculated. The acquisition function 351 may acquire three-dimensional fat image data in step S101, and the analysis function 352 may calculate an evaluation value using the three-dimensional fat image data.

(Second Embodiment)
In the first embodiment described above, the case where the acquisition function 351 acquires one type of three-dimensional image data (for example, T2W image data) in step S101 has been described. However, the image processing apparatus 300 may acquire a plurality of types of three-dimensional image data having different contrasts. Then, the image processing apparatus 300 may calculate a plurality of evaluation values by analyzing a plurality of types of three-dimensional data. Such an embodiment will be described as a second embodiment.

  FIG. 6 is a flowchart illustrating a processing procedure of analysis processing performed by the image processing apparatus according to the second embodiment.

  For example, as illustrated in FIG. 6, in the image processing apparatus 300 according to the second embodiment, the acquisition function 351 acquires a plurality of three-dimensional image data with different contrasts from the MRI apparatus 100 or the image storage apparatus 200 ( Step S201). This step S201 is realized, for example, when the processing circuit 350 calls a predetermined program corresponding to the acquisition function 351 from the storage circuit 320 and executes it. In this embodiment, in step S201, the acquisition function 351 uses the shoulder joint of the subject to which the rotator cuff has been torn and the entire supraspinatus connected to the torn rotator cuff as image data of the three-dimensional morphological image. Although the case where three-dimensional T1 image data including is acquired will be described, three-dimensional T2W image data may be acquired. Furthermore, the acquisition function 351 according to the second embodiment also acquires three-dimensional fat image data in step S201.

  Subsequently, the analysis function 352 performs a threshold process on the three-dimensional T1 image data, performs a segmentation process to extract the atrophic muscle tissue region, and performs a threshold process on the three-dimensional fat image data. Then, a segmentation process for extracting the fatty degeneration region and the atrophic muscle tissue region is performed (step S202). In the following description, in order to distinguish between the atrophic muscle tissue region extracted from the three-dimensional T1 image data and the atrophic muscle tissue region extracted from the three-dimensional fat image data, it is extracted from the three-dimensional T1 image data. The atrophied muscle tissue region is expressed as “atrophy muscle tissue region A”, and the atrophied muscle tissue region extracted from the three-dimensional fat image data is expressed as “atrophy muscle tissue region B”.

  Subsequently, the analysis function 352 calculates the volume of the fatty degeneration region extracted in step S202 (step S203). Subsequently, the analysis function 352 calculates the volume of the atrophy muscle tissue region A and the volume of the atrophy muscle tissue region B extracted in step S202 (step S204).

  Subsequently, the analysis function 352 calculates a fat degeneration ratio that is a ratio between the volume of the fatty degeneration region calculated in step S203 and the volume of the atrophic muscle tissue region B calculated in step S204 (step S205). That is, the analysis function 352 calculates the fatty degeneration ratio by comparing the volume of the fatty degeneration region calculated in step S203 with the volume of the atrophic muscle tissue region B calculated in step S204. For example, the analysis function 352 calculates a value obtained by dividing the volume of the steatosis region by the volume of the atrophy muscle tissue region B as the steatosis ratio.

  Subsequently, the analysis function 352 acquires the average value of the volume of the supraspinatus that has not undergone atrophy from the volume database 320a, and the volume of the atrophy muscle tissue region A calculated in step S204 and the acquired volume of the supraspinous muscle. The degree of muscular atrophy, which is a ratio to the average value, is calculated (step S206). That is, the analysis function 352 acquires the average value of the volume of the supraspinatus that has not undergone atrophy from the volume database 320a, and calculates the volume of the atrophy muscle tissue region A calculated in step S204 and the volume of the acquired supraspinous muscle. The degree of muscle atrophy is calculated by comparison with the average value. For example, the analysis function 352 calculates a value obtained by dividing the volume of the atrophic muscle tissue region A by the average value of the volume of the supraspinatus muscle that has not undergone atrophy as the degree of muscle atrophy.

  Subsequently, the analysis function 352 performs a process similar to the process in step S107 in the first embodiment. That is, the analysis function 352 acquires the JOA score corresponding to the fatty degeneration ratio calculated in step S205 and the muscle atrophy calculated in step S206 from the JOA score database 320B, and reconstruction is performed using the acquired JOA score. Whether or not it is possible is determined (step S207), and the analysis process is terminated. Steps S202 to S207 described above are realized, for example, when the processing circuit 350 calls and executes a predetermined program corresponding to the analysis function 352 according to the second embodiment from the storage circuit 320.

  According to the image processing apparatus 300 according to the second embodiment, an evaluation value related to a joint can be calculated with high accuracy, as in the first embodiment.

(Third embodiment)
In the first embodiment, the second embodiment, and the first to third modifications, the embodiment of the image processing apparatus has been described. However, the embodiment of the analysis processing method disclosed in the present application is described here. Not limited. For example, the analysis processing method disclosed in the present application can be implemented by an MRI apparatus. Hereinafter, an embodiment of an MRI apparatus will be described as a third embodiment.

  FIG. 7 is a diagram illustrating a configuration example of an MRI apparatus according to the third embodiment. For example, as shown in FIG. 7, the MRI apparatus 100 includes a static magnetic field magnet 1, a gradient magnetic field coil 2, a gradient magnetic field power source 3, a transmission coil 4, a transmission circuit 5, a reception coil 6, a reception circuit 7, a bed 8, and an input circuit. 9, a display 10, a storage circuit 11, and processing circuits 12-15.

  The static magnetic field magnet 1 is formed in a hollow, substantially cylindrical shape (including one having a cross section perpendicular to the central axis of the cylinder is elliptical), and generates a uniform static magnetic field in the imaging space formed on the inner peripheral side. Let For example, the static magnetic field magnet 1 is realized by a permanent magnet, a superconducting magnet, or the like.

  The gradient magnetic field coil 2 is formed in a hollow, substantially cylindrical shape (including one having a cross section perpendicular to the central axis of the cylinder is elliptical), and is disposed on the inner peripheral side of the static magnetic field magnet 1. The gradient coil 2 has three coils that generate gradient magnetic fields along the x-axis, y-axis, and z-axis that are orthogonal to each other. Here, the x axis, the y axis, and the z axis constitute an apparatus coordinate system unique to the MRI apparatus 100. For example, the x-axis direction is set to the vertical direction, and the y-axis direction is set to the horizontal direction. The direction of the z axis is set to be the same as the direction of the magnetic flux of the static magnetic field generated by the static magnetic field magnet 1.

  The gradient magnetic field power supply 3 individually supplies current to each of the three coils included in the gradient magnetic field coil 2 to generate gradient magnetic fields along the x axis, the y axis, and the z axis in the imaging space. By appropriately generating gradient magnetic fields along the x-axis, y-axis, and z-axis, it is possible to generate gradient magnetic fields along the readout direction, the phase encoding direction, and the slice direction that are orthogonal to each other. Here, the axes along the readout direction, the phase encoding direction, and the slice direction constitute a logical coordinate system for defining a slice region or a volume region to be imaged. Hereinafter, the gradient magnetic field along the readout direction is referred to as a readout gradient magnetic field, the gradient magnetic field along the phase encoding direction is referred to as a phase encoding gradient magnetic field, and the gradient magnetic field along the slice direction is referred to as a slice gradient magnetic field. .

  Each gradient magnetic field is superimposed on the static magnetic field generated by the static magnetic field magnet 1 and used to give spatial position information to a magnetic resonance (MR) signal. Specifically, the readout gradient magnetic field gives the MR signal position information along the readout direction by changing the frequency of the MR signal in accordance with the position in the readout direction. Further, the phase encoding gradient magnetic field changes the phase of the MR signal along the phase encoding direction, thereby giving position information in the phase encoding direction to the MR signal. The slice gradient magnetic field is used to determine the direction, thickness, and number of slice areas when the imaging area is a slice area, and according to the position in the slice direction when the imaging area is a volume area. By changing the phase of the MR signal, position information along the slice direction is given to the MR signal.

  The transmission coil 4 is formed in a hollow, substantially cylindrical shape (including one having a cross section perpendicular to the central axis of the cylinder is elliptical), and is disposed inside the gradient magnetic field coil 2. The transmission coil 4 applies an RF (Radio Frequency) pulse output from the transmission circuit 5 to the imaging space.

  The transmission circuit 5 outputs an RF pulse corresponding to the Larmor frequency to the transmission coil 4. For example, the transmission circuit 5 includes an oscillation circuit, a phase selection circuit, a frequency conversion circuit, an amplitude modulation circuit, and an RF amplification circuit. The oscillation circuit generates an RF pulse having a resonance frequency unique to a target nucleus placed in a static magnetic field. The phase selection circuit selects the phase of the RF pulse output from the oscillation circuit. The frequency conversion circuit converts the frequency of the RF pulse output from the phase selection circuit. The amplitude modulation circuit modulates the amplitude of the RF pulse output from the frequency conversion circuit according to, for example, a sinc function. The RF amplification circuit amplifies the RF pulse output from the amplitude modulation circuit and outputs the amplified RF pulse to the transmission coil 4.

  The receiving coil 6 is attached to the subject S placed in the imaging space, and receives an MR signal radiated from the subject S under the influence of the RF magnetic field applied by the transmitting coil 4. The receiving coil 6 outputs the received MR signal to the receiving circuit 7. For example, a dedicated coil is used for the receiving coil 6 for each part to be imaged. The dedicated coil here is, for example, a receiving coil for the head, a receiving coil for the spine, a receiving coil for the abdomen.

  The receiving circuit 7 generates MR signal data based on the MR signal output from the receiving coil 6, and outputs the generated MR signal data to the processing circuit 13. For example, the reception circuit 7 includes a selection circuit, a preamplifier circuit, a phase detection circuit, and an analog / digital conversion circuit. The selection circuit selectively inputs the MR signal output from the receiving coil 6. The pre-amplifier circuit amplifies the MR signal output from the selection circuit. The phase detection circuit detects the phase of the MR signal output from the preceding amplifier. The analog-digital conversion circuit generates MR signal data by converting the analog signal output from the phase detector into a digital signal, and outputs the generated MR signal data to the processing circuit 13.

  Here, an example in which the transmission coil 4 applies an RF pulse and the reception coil 6 receives an MR signal will be described, but the forms of the transmission coil and the reception coil are not limited thereto. For example, the transmission coil 4 may further have a reception function for receiving MR signals. Moreover, the receiving coil 6 may further have a transmission function for applying an RF magnetic field. When the transmission coil 4 has a reception function, the reception circuit 7 also generates MR signal data from the MR signal received by the transmission coil 4. When the reception coil 6 has a transmission function, the transmission circuit 5 also outputs an RF pulse to the reception coil 6.

  The bed 8 includes a top plate 8a on which the subject S is placed, and when the subject S is imaged, the top plate 8a enters the imaging space formed inside the static magnetic field magnet 1 and the gradient magnetic field coil 2. Insert. For example, the bed 8 is installed so that the longitudinal direction is parallel to the central axis of the static magnetic field magnet 1.

  The input circuit 9 receives various instructions and various information input operations from the operator. For example, the input circuit 9 is realized by a trackball, a switch button, a mouse, a keyboard, a touch panel, or the like. The input circuit 9 is connected to the processing circuit 15, converts an input operation received from the operator into an electrical signal, and outputs the electrical signal to the processing circuit 15.

  The display 10 displays various information and various images. For example, the display 10 is realized by a liquid crystal monitor, a CRT (Cathode Ray Tube) monitor, a touch panel, or the like. The display 10 is connected to the processing circuit 15, converts various information and various image data sent from the processing circuit 15 into electric signals for display, and outputs them.

  The storage circuit 11 stores various data. For example, the storage circuit 11 stores MR signal data and image data for each subject S. For example, the storage circuit 11 is realized by a semiconductor memory device such as a RAM (Random Access Memory) or a flash memory, a hard disk, an optical disk, or the like. The storage circuit 11 also stores a volume database 11a and a JOA score database 11b. The volume database 11a has a function similar to that of the volume database 320a described in the first embodiment, the second embodiment, or each modification described above. The JOA score database 11b has the same function as the JOA score database 320b described in the first embodiment, the second embodiment, or each modification described above.

  The processing circuit 12 has a bed control function 13A. For example, the processing circuit 12 is realized by a processor. The bed control function 13 </ b> A is connected to the bed 8, and controls the operation of the bed 8 by outputting a control electric signal to the bed 8. For example, the bed control function 13A receives from the operator an instruction to move the top board 8a in the longitudinal direction, the up-down direction, or the left-right direction via the input circuit 9, and moves the top board 8a according to the received instruction. The drive mechanism of the top board 8a which the bed 8 has is operated.

  The processing circuit 13 has an execution function 13a. For example, the processing circuit 13 is realized by a processor. The execution function 13a executes various pulse sequences. Specifically, the execution function 13a executes various pulse sequences by driving the gradient magnetic field power supply 3, the transmission circuit 5, and the reception circuit 7 based on the sequence execution data output from the processing circuit 15.

  Here, the sequence execution data is information defining a pulse sequence indicating a procedure for collecting MR signal data. Specifically, the sequence execution data includes the timing at which the gradient magnetic field power supply 3 supplies current to the gradient coil 2 and the strength of the supplied current, the strength of the RF pulse current supplied to the transmission coil 4 by the transmission circuit 5, and the like. This is information defining supply timing, detection timing at which the receiving circuit 7 detects an MR signal, and the like.

  The execution function 13a receives MR signal data from the receiving circuit 7 as a result of executing various pulse sequences, and stores the received MR signal data in the storage circuit 11. The set of MR signal data received by the execution function 13a is arranged two-dimensionally or three-dimensionally according to the position information given by the readout gradient magnetic field, phase encoding gradient magnetic field, and slice gradient magnetic field. Thus, it is stored in the memory circuit 11 as data constituting the k space.

  The processing circuit 14 has an image generation function 14a. For example, the processing circuit 14 is realized by a processor. The image generation function 14 a generates an image based on the MR signal data stored in the storage circuit 11. Specifically, the image generation function 14a reads the MR signal data stored in the storage circuit 11 by the execution function 13a, and performs post-processing, that is, reconstruction processing such as Fourier transform, on the read MR signal data. Generate. Further, the image generation function 14 a stores the image data of the generated image in the storage circuit 11. In the present embodiment, the image generation function 14a is connected to a T2W image including the entire supraspinatus muscle connected to the shoulder joint and torn rotator cuff of the subject, or to the shoulder joint and torn rotator cuff of the subject. 3D image data of a morphological image such as a T1 image including the entire supraspinatus is generated. Further, the image generation function 14a includes the entire supraspinatus muscle connected to the shoulder joint of the subject and the torn rotator cuff from the MR signal data collected by executing the pulse sequence of the water / fat separation method. Three-dimensional fat image data is generated.

  That is, in the present embodiment, the execution function 13a and the image generation function 14a allow the T2W image including the entire shoulder joint of the subject and the supraspinatus muscle connected to the torn rotator cuff, and the joint and tear of the subject's shoulder. Three-dimensional image data of a morphological image such as a T1 image including the entire supraspinatus muscle connected to the rotator cuff is collected. In addition, the execution function 13a and the image generation function 14a collect three-dimensional fat image data including the entire supraspinatus muscle connected to the shoulder joint of the subject and the torn rotator cuff. The execution function 13a and the image generation function 14a are examples of the collection unit described in the claims.

  The processing circuit 15 performs overall control of the MRI apparatus 100 by controlling each component included in the MRI apparatus 100. For example, the processing circuit 15 is realized by a processor. For example, the processing circuit 15 receives input of various parameters related to the pulse sequence from the operator via the input circuit 9, and generates sequence execution data based on the received parameters. Then, the processing circuit 15 executes the various pulse sequences by transmitting the generated sequence execution data to the processing circuit 13. Further, for example, the processing circuit 15 reads out image data of an image requested by the operator from the storage circuit 11 and outputs the read image to the display 10.

  The configuration example of the MRI apparatus 100 according to the present embodiment has been described above. Under such a configuration, the MRI apparatus 100 is configured to be able to calculate the evaluation value with high accuracy without performing a difficult task of determining the optimum cross section for calculating the evaluation value to the doctor. Has been.

  Specifically, the processing circuit 15 has an analysis function 15a.

  The analysis function 15a has the same function as the analysis function 352 described in the first embodiment, the second embodiment, or each modification described above. However, the acquisition function 351 in the first embodiment acquires three-dimensional image data from the MRI apparatus 100 or the image storage apparatus 200, whereas the analysis function 15a according to the present embodiment includes a three-dimensional image from the storage circuit 11. Get image data.

  In this embodiment, the input circuit 9, the display 10, and the storage circuit 11 further have the functions of the input circuit 330, the display 340, and the storage circuit 320 described in the first embodiment or each modification described above.

  The analysis function 15a that is a processing function of the processing circuit 15 has been described above. Here, for example, the analysis function 15a described above is stored in the storage circuit 11 in the form of a program executable by a computer. The processing circuit 15 reads the program corresponding to the analysis function 15a from the storage circuit 11 and executes the read program, thereby realizing the analysis function 15a corresponding to the program. In other words, the processing circuit 15 that has read the program has the analysis function 15a shown in FIG.

  The analysis function 15a included in the processing circuit 15 may be realized by being appropriately distributed among a plurality of processing circuits.

  With such a configuration, according to the third embodiment, as in the first embodiment, the evaluation value can be obtained without performing a difficult task of determining the optimum cross section for calculating the evaluation value to the doctor. Can be calculated with high accuracy.

  Further, the term “processor” used in each of the above-described embodiments is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an application specific integrated circuit (ASIC), or a programmable. Means circuits such as logic devices (for example, Simple Programmable Logic Device (SPLD), Complex Programmable Logic Device (CPLD), and Field Programmable Gate Array (FPGA)) To do. Here, instead of storing the program in the storage circuit, the program may be directly incorporated in the circuit of the processor. In this case, the processor realizes the function by reading and executing the program incorporated in the circuit. In addition, each processor of the present embodiment is not limited to being configured as a single circuit for each processor, but may be configured as a single processor by combining a plurality of independent circuits to realize its function. Good.

  According to at least one embodiment or at least one modification described above, an evaluation value related to a joint can be calculated with high accuracy.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example 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. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

300 Image Processing Device 350 Processing Circuit 351 Acquisition Function 352 Analysis Function

Claims (10)

An acquisition unit for acquiring three-dimensional data including a joint of a subject collected by magnetic resonance imaging;
An analysis unit that analyzes the three-dimensional data in a three-dimensional space and calculates an evaluation value related to the joint of the subject;
An image processing apparatus comprising: The analysis unit calculates a volume of a region of interest by analyzing the three-dimensional data in a three-dimensional space, and calculates the evaluation value using the volume.
The image processing apparatus according to claim 1. The analysis unit calculates the evaluation value by comparing the volume and a volume of a region having symmetry with the region of interest in the subject.
The image processing apparatus according to claim 2. The analysis unit calculates the evaluation value by comparing the volume and the volume of the region of interest obtained clinically.
The image processing apparatus according to claim 2. The analysis unit generates a histogram representing a relationship between brightness and frequency in the three-dimensional space by analyzing the three-dimensional data in the three-dimensional space, and uses the histogram in the three-dimensional space to generate the evaluation value To calculate,
The image processing apparatus according to claim 1. The acquisition unit acquires three-dimensional fat image data collected by a water fat separation method,
The analysis unit calculates the fatty degeneration ratio as the evaluation value by analyzing the three-dimensional fat image data.
The image processing apparatus according to claim 1. The acquisition unit acquires three-dimensional morphological image data,
The analysis unit calculates a muscle atrophy degree as the evaluation value by analyzing the three-dimensional morphological image data.
The image processing apparatus according to claim 1. The analysis unit uses the evaluation value to determine evaluation regarding the joint.
The image processing apparatus according to claim 1. The acquisition unit acquires a plurality of three-dimensional data having different contrasts,
The analysis unit calculates a plurality of evaluation values by analyzing the plurality of three-dimensional data, and determines the evaluation using the plurality of evaluation values.
The image processing apparatus according to claim 8. A collection unit that collects three-dimensional data related to the region of interest including the joint of the subject;
An analysis unit that analyzes the three-dimensional data in a three-dimensional space and calculates an evaluation value related to the joint of the subject;
A magnetic resonance imaging apparatus comprising: