JP5355240B2 - Image processing apparatus, magnetic resonance imaging apparatus, and image management system - Google Patents

Description

  The present invention relates to an image processing apparatus, a magnetic resonance imaging apparatus, and an image management system.

  Conventionally, as an image diagnostic technique for evaluating the properties and vulnerability of vascular plaques, Gray Scale-IVUS (Intravascular Ultrasound), VH-IVUS (Virtual Histology-IVUS), IB-IVUS (Integrated Backscatter) -IVUS), OCT (Optical Coherence Tomography), CAS (Coronary Angioscopy). However, these methods take an image for plaque evaluation from within a blood vessel, and it is necessary to insert a catheter into the subject. For this reason, there are problems such as a heavy burden on the subject and a local blood vessel observation range.

  In view of this, an image processing apparatus has been devised that analyzes the structure of a blood vessel using an image taken by a medical image diagnostic apparatus such as an X-ray diagnostic apparatus or an X-ray CT apparatus (for example, see Patent Documents 1, 2, or 3) ). For example, this image processing apparatus automatically extracts a blood vessel region from an image taken using a medical image diagnostic apparatus, calculates a stenosis rate of the blood vessel, and corrects the blood vessel region.

JP 2004-283373 A JP 2006-75602 A JP 2006-246941 A

  However, it is difficult to support the vulnerability analysis of vascular plaque with the above-described known technology. In particular, an image captured by a magnetic resonance imaging apparatus may have a signal value range that varies depending on an imaging sequence, a magnetic field condition, or the like, or a contrast resolution between a blood vessel peripheral tissue and a plaque region may not be high. For this reason, it is difficult to automatically extract a blood vessel region including plaque.

  The present invention has been made in view of the above, and is an image processing apparatus, a magnetic resonance imaging apparatus, and an image that can support vulnerability analysis of a vascular plaque using MR images without performing catheter insertion. The purpose is to provide a management system.

  In order to solve the above-described problems and achieve the object, according to the first aspect of the present invention, an image processing apparatus inputs a first image captured by a first imaging method using a magnetic resonance imaging apparatus. First image input means, second image input means for inputting a second image picked up by a second image pickup method different from the first image pickup method using a magnetic resonance imaging apparatus, A vascular plaque region extracting unit that extracts a vascular plaque region indicating a vascular plaque from each of the first image input by the first image input unit and the second image input by the second image input unit; Among the vascular plaque regions extracted by the vascular plaque region extracting means, regions where the signal value is equal to or smaller than a predetermined threshold value in both the first image and the second image are stabilized. A stable plaque area extracting means for extracting as an unstable plaque area; and an unstable plaque estimating means for estimating an unstable plaque area using the stable plaque area extracted by the stable plaque area extracting means. To do.

  According to a seventh aspect of the present invention, the magnetic resonance imaging apparatus is different from the first imaging method in which the first image input means for inputting the first image captured by the first imaging method is different from the first imaging method. Second image input means for inputting a second image picked up by the second image pickup method, input by the first image input means by the first image input means, and input by the second image input means A vascular plaque region extracting means for extracting a vascular plaque region indicating a vascular plaque from each of the second images, and the first image and the first of the vascular plaque regions extracted by the vascular plaque region extracting means. A stable plaque region extracting unit that extracts a region where the signal value is equal to or less than a predetermined threshold in both of the two images as a stable plaque region, and extraction by the stable plaque region extracting unit Using the stable plaque area, characterized by comprising a vulnerable plaque estimating means for estimating a vulnerable plaque area.

  According to an eighth aspect of the present invention, there is provided an image management system for causing an image output device to display an image picked up by a magnetic resonance imaging apparatus, wherein the image is picked up by a first image pickup method using the magnetic resonance imaging apparatus. A first image input means for inputting the first image, and a second image input by a second imaging method different from the first imaging method using the magnetic resonance imaging apparatus. A blood vessel plaque region indicating a blood vessel plaque from each of the second image input means, the first image input by the first image input means, and the second image input by the second image input means. Out of the blood vessel plaque region extracted by the blood vessel plaque region extracting unit and the blood vessel plaque region extracting unit, the first image and the second image are extracted. The stable plaque region is extracted using the stable plaque region extracting means for extracting the region where the signal value is equal to or less than a predetermined threshold as the stable plaque region, and the stable plaque region extracted by the stable plaque region extracting means. An unstable plaque estimation means for estimation and an image output means for outputting the unstable plaque region estimated by the unstable plaque estimation means to the image output device are provided.

  According to the first or second aspect of the present invention, it is possible to support vulnerability analysis of vascular plaque using MR images without performing catheter insertion.

FIG. 1 is a diagram illustrating an overall configuration of the MRI apparatus according to the first embodiment. FIG. 2 is a block diagram illustrating a functional configuration of the control unit according to the first embodiment. FIG. 3 is a flowchart illustrating the flow of processing performed by each unit included in the control unit according to the first embodiment. FIG. 4 is a flowchart illustrating a flow of estimation of the vascular plaque region according to the first embodiment. FIG. 5 is a diagram illustrating an example of blood vessel plaque region estimation according to the first embodiment. FIG. 6 is a flowchart illustrating a flow of extraction of unstable plaque candidates according to the first embodiment. FIG. 7 is a diagram illustrating an example of extraction of unstable plaque candidates according to the first embodiment. FIG. 8 is a diagram illustrating a display example of unstable plaque candidates. FIG. 9 is a block diagram illustrating the functional configuration of the control unit according to the second embodiment. FIG. 10 is a flowchart illustrating a flow of processing performed by each unit included in the control unit according to the second embodiment. FIG. 11 is a flowchart illustrating a flow of estimating a blood vessel plaque region according to the second embodiment. FIG. 12 is a diagram illustrating an example of blood vessel plaque region estimation according to the second embodiment. FIG. 13 is a block diagram illustrating the functional configuration of the control unit according to the third embodiment. FIG. 14 is a flowchart illustrating the flow of processing performed by each unit included in the control unit according to the third embodiment. FIG. 15 is a block diagram illustrating the functional configuration of the control unit according to the fourth embodiment. FIG. 16 is a flowchart illustrating a flow of processing performed by each unit included in the control unit according to the fourth embodiment. FIG. 17 is a flowchart illustrating a specific flow of a high-risk blood vessel according to the fourth embodiment. FIG. 18 is a diagram illustrating an example of specifying a high-risk blood vessel according to the fourth embodiment. FIG. 19 is a diagram illustrating a display example of high-risk blood vessels and unstable plaque candidates. FIG. 20 is a diagram illustrating the overall configuration of the image management system according to the fifth embodiment. FIG. 21 is a flowchart of the process procedure of the image management system according to the fifth embodiment.

  Embodiments of an image processing apparatus, a magnetic resonance imaging apparatus, and an image management system according to the present invention will be described below in detail with reference to the drawings. Hereinafter, the magnetic resonance imaging apparatus is referred to as an “MRI (Magnetic Resonance Imaging) apparatus”, and an image captured by the MRI apparatus is referred to as an “MR image”.

  In the following embodiment, a case where a fat suppression T1-weighted image, a 3D-FASE image, and a Black Blood image are used will be described. Here, the “3D-FASE image” is a three-dimensional image photographed by a high-speed SE method called FASE (Fast Advanced Spin Echo). A “Black Blood image” is an image captured by the Black Blood method, which is an imaging method that lowers the luminance value of blood, and the luminance value decreases in a blood vessel or a tissue rich in blood flow. It is drawn.

  First, the overall configuration of the MRI apparatus according to the first embodiment will be described. FIG. 1 is a diagram illustrating the overall configuration of the MRI apparatus 100 according to the first embodiment. As shown in FIG. 1, this MRI apparatus 100 includes a static magnetic field magnet 1, a gradient magnetic field coil 2, a gradient magnetic field power source 3, a bed 4, a bed control unit 5, a transmission RF coil 6, a transmission unit 7, a reception RF coil 8, A receiving unit 9, a sequence control unit 10, and a computer system 20 are provided.

  The static magnetic field magnet 1 is a magnet formed in a hollow cylindrical shape, and generates a uniform static magnetic field in an internal space. As the static magnetic field magnet 1, for example, a permanent magnet, a superconducting magnet or the like is used.

  The gradient magnetic field coil 2 is a coil formed in a hollow cylindrical shape, and is disposed inside the static magnetic field magnet 1. The gradient magnetic field coil 2 is formed by combining three coils corresponding to the X, Y, and Z axes orthogonal to each other, and these three coils are individually supplied with current from a gradient magnetic field power source 3 to be described later. In response, a gradient magnetic field whose magnetic field strength changes along the X, Y, and Z axes is generated. The Z-axis direction is the same direction as the static magnetic field. The gradient magnetic field power supply 3 is a device that supplies current to the gradient magnetic field coil 2.

  Here, the gradient magnetic fields of the X, Y, and Z axes generated by the gradient magnetic field coil 2 correspond to, for example, the slice selection gradient magnetic field Gs, the phase encoding gradient magnetic field Ge, and the readout gradient magnetic field Gr, respectively. . The slice selection gradient magnetic field Gs is used to arbitrarily determine an imaging section. The phase encoding gradient magnetic field Ge is used to change the phase of the magnetic resonance signal in accordance with the spatial position. The readout gradient magnetic field Gr is used for changing the frequency of the magnetic resonance signal in accordance with the spatial position.

  The couch 4 includes a couchtop 4a on which the subject P is placed. Under the control of a couch controller 5 described later, the couchtop 4a is placed in the cavity of the gradient magnetic field coil 2 with the subject P placed thereon. Insert into (imaging port). Usually, the bed 4 is installed such that the longitudinal direction is parallel to the central axis of the static magnetic field magnet 1. The couch controller 5 is a device that controls the couch 4 under the control of the controller 30 and drives the couch 4 to move the couchtop 4a in the longitudinal direction and the vertical direction.

  The transmission RF coil 6 is arranged inside the gradient magnetic field coil 2 and receives a high frequency pulse from the transmission unit 7 to generate a high frequency magnetic field. The transmission unit 7 is a device that transmits a high-frequency pulse corresponding to the Larmor frequency to the transmission RF coil 6.

  The reception RF coil 8 is disposed inside the gradient coil 2 and receives a magnetic resonance signal radiated from the subject P due to the influence of the high-frequency magnetic field. When receiving the magnetic resonance signal, the reception RF coil 8 outputs the magnetic resonance signal to the receiving unit 9.

  The receiving unit 9 generates k-space data based on the magnetic resonance signal output from the receiving RF coil 8. Specifically, the receiving unit 9 generates k-space data by digitally converting the magnetic resonance signal output from the reception RF coil 8. The k-space data includes PE (Phase Encode) direction, RO (Read Out) direction, SE (Slice Encode) direction by the above-described slice selection gradient magnetic field Gs, phase encoding gradient magnetic field Ge, and readout gradient magnetic field Gr. Information on the spatial frequency of the direction is associated. When the k-space data is generated, the receiving unit 9 transmits the k-space data to the sequence control unit 10.

  The sequence control unit 10 scans the subject P by driving the gradient magnetic field power source 3, the transmission unit 7, and the reception unit 9 based on the sequence information transmitted from the computer system 20. Here, the sequence information refers to the strength of the power supplied from the gradient magnetic field power supply 3 to the gradient magnetic field coil 2 and the timing of supplying the power, the strength of the RF signal transmitted from the transmitter 7 to the transmission RF coil 6 and the RF signal. Information defining the procedure for performing the scan, such as the timing at which the reception unit 9 detects the magnetic resonance signal.

  The sequence control unit 10 drives the gradient magnetic field power source 3, the transmission unit 7, and the reception unit 9 to scan the subject P. As a result, when the k-space data is transmitted from the reception unit 9, the sequence control unit 10 stores the k-space data. Transfer to the computer system 20.

  The computer system 20 performs overall control of the MRI apparatus 100, data collection, image reconstruction, and the like. The computer system 20 particularly includes an interface unit 21, an image reconstruction unit 22, a storage unit 23, an input unit 24, a display unit 25, and a control unit 30.

  The interface unit 21 controls input / output of various signals exchanged with the sequence control unit 10. For example, the interface unit 21 transmits sequence information to the sequence control unit 10 and receives k-space data from the sequence control unit 10. Upon receiving the k-space data, the interface unit 21 stores each k-space data in the storage unit 23 for each subject P.

  The image reconstruction unit 22 performs post-processing, that is, reconstruction processing such as Fourier transform, on the k-space data stored in the storage unit 23, thereby obtaining spectrum data or images of desired nuclear spins in the subject P. Generate data.

  The storage unit 23 stores the k-space data received by the interface unit 21 and the image data generated by the image reconstruction unit 22 for each subject P. The storage unit 23 stores images captured by various imaging methods such as the Black Blood method, the delayed contrast method, and the Tagging method.

  The input unit 24 receives various instructions and information input from the operator. As the input unit 24, a pointing device such as a mouse or a trackball, a selection device such as a mode change switch, or an input device such as a keyboard can be used as appropriate.

  The display unit 25 displays various types of information such as spectrum data or image data under the control of the control unit 30. As the display unit 25, a display device such as a liquid crystal display can be used.

  The control unit 30 includes a CPU, a memory, and the like (not shown) and performs overall control of the MRI apparatus 100. Specifically, the control unit 30 generates sequence information based on the imaging conditions input from the operator via the input unit 24 and transmits the generated sequence information to the sequence control unit 10 to perform scanning. The image reconstruction is performed based on k-space data sent from the sequence control unit 10 as a result of scanning.

  Next, a functional configuration of the control unit 30 according to the first embodiment will be described. FIG. 2 is a block diagram illustrating a functional configuration of the control unit 30 according to the first embodiment. As shown in FIG. 2, the control unit 30 particularly includes an image input unit 31, a vascular plaque extraction unit 32, a vascular plaque analysis unit 33, and an image output unit 34.

  The image input unit 31 inputs the fat suppression T1-weighted image and the 3D-FASE image reconstructed by the image reconstruction unit 22. Specifically, the image input unit 31 includes a fat suppression T1 weighted image input unit 31a and a 3D-FASE image input unit 31b.

  The vascular plaque extraction unit 32 extracts a vascular plaque region indicating a vascular plaque from each of the fat suppression T1-weighted image and the 3D-FASE image input by the image input unit 31. Specifically, the vascular plaque extraction unit 32 includes a vascular lumen extraction unit 32a, a vascular lumen boundary line extraction unit 32b, and a plaque region estimation unit 32c.

  The vascular plaque analysis unit 33 sets, as a stable plaque region, a region in which the signal value is equal to or less than a predetermined threshold in both the fat suppression T1-weighted image and the 3D-FASE image among the vascular plaque regions extracted by the vascular plaque extraction unit 32. Extract. The vascular plaque analysis unit 33 estimates an unstable plaque region using the extracted stable plaque region. Specifically, the vascular plaque analysis unit 33 includes a vascular plaque region input unit 33a, an analysis image synthesis unit 33b, a stable plaque region extraction unit 33c, an unstable plaque candidate extraction unit 33d, and a plaque instability calculation unit 33e. .

  The image output unit 34 outputs the unstable plaque region estimated by the vascular plaque analysis unit 33 to the display unit 25. Specifically, the image output unit 34 includes a display image input unit 34a, a vascular plaque instability analysis result synthesis unit 34b, a geometric condition setting unit 34c, a display condition setting unit 34d, and an image display unit 34e.

  Next, processing performed by each unit included in the control unit 30 according to the first embodiment will be described. FIG. 3 is a flowchart illustrating the flow of processing performed by each unit included in the control unit 30 according to the first embodiment. In addition, although the case where the blood vessel lumen is extracted from the fat suppression T1 weighted image is described here, the blood vessel lumen can also be extracted from the 3D-FASE image.

  As shown in FIG. 3, in the control unit 30 according to the first embodiment, first, the fat suppression T1-weighted image input unit 31a reads a fat suppression T1-weighted image (step S01). Subsequently, the vascular plaque extraction unit 32 estimates a vascular plaque region in the fat suppression T1-weighted image read by the fat suppression T1-weighted image input unit 31a (step S02).

  Here, the estimation of the vascular plaque region according to the first embodiment will be described in detail. FIG. 4 is a flowchart illustrating a flow of estimation of the vascular plaque region according to the first embodiment. FIG. 5 is a diagram illustrating an example of blood vessel plaque region estimation according to the first embodiment.

  As shown in FIG. 4, when estimating the vascular plaque region, the fat suppression T1-weighted image input unit 31a reads the fat suppression T1-weighted image (step S11). Subsequently, as shown in FIG. 5A, the blood vessel lumen extraction unit 32a uses a threshold process or the like to generate a high signal from the fat suppression T1-weighted image read by the fat suppression T1-weighted image input unit 31a. The blood vessel lumen region depicted in (1) is extracted (step S12).

  Subsequently, the blood vessel lumen boundary line extraction unit 32b extracts a blood vessel core line from the blood vessel lumen region extracted by the blood vessel lumen extraction unit 32a. Thereafter, the blood vessel lumen boundary line extraction unit 32b extracts a lumen boundary line (closed curve) perpendicular to the extracted blood vessel core line at regular intervals (step S13).

  Subsequently, as shown in FIG. 5 (b), the plaque region estimation unit 32c performs shape change or area change of the lumen boundary along the blood vessel core line extracted by the blood vessel lumen boundary extraction unit 32b. A prominent location is detected as a “defect location” in which the shape of the outer wall of the blood vessel including the plaque could not be depicted (step S14). Thereafter, as shown in FIG. 5C, the plaque region estimation unit 32c estimates the plaque region by interpolating the defect portion based on the shape of the lumen boundary around the detected defect portion (step). S15).

  Returning to FIG. 3, after estimating the plaque region, the plaque region estimation unit 32c extracts only the estimated vascular plaque region from the fat suppression T1-weighted image read by the fat suppression T1-weighted image input unit 31a (step S03).

  On the other hand, the 3D-FASE image input unit 31b reads the 3D-FASE image (step S04). The vascular plaque region input unit 33a inputs the vascular plaque region extracted by the plaque region estimation unit 32c.

  Then, the analysis image composition unit 33b extracts only the vascular plaque region input by the vascular plaque region input unit 33a from the 3D-FASE image read by the 3D-FASE image input unit 31b (step S05).

  After the vascular plaque region is extracted from each of the fat suppression T1-weighted image and the 3D-FASE image, the stable plaque region extraction unit 33c determines that the signal value in the vascular plaque region of the fat suppression T1-weighted image and the 3D-FASE image Both parts with low signals are extracted, and the extracted parts are labeled as “stable plaques”.

  Further, the unstable plaque candidate extraction unit 33d extracts the remaining vascular plaque region that is not labeled as a stable plaque by the stable plaque region extraction unit 33c as an “unstable plaque candidate” (step S06). This processing is based on the idea that the signal value of the unstable plaque is not constant in the MR image, but the stable plaque stably exhibits a low signal.

  Here, extraction of unstable plaque candidates according to the first embodiment will be described in detail. FIG. 6 is a flowchart illustrating a flow of extraction of unstable plaque candidates according to the first embodiment. FIG. 7 is a diagram illustrating an example of extraction of unstable plaque candidates according to the first embodiment.

  As shown in FIG. 6, in Example 1, the plaque region estimation unit 32c reads the fat suppression T1-weighted image input unit 31a (Step S21), and extracts a blood vessel plaque region from the read fat suppression T1-weighted image ( Step S22). Then, as shown in FIG. 7A, the plaque region estimation unit 32c extracts, as a low signal region, a region whose signal value is equal to or less than a predetermined threshold value from the extracted blood vessel plaque region (step S23). Further, the plaque region estimation unit 32c extracts a region where the signal value exceeds a predetermined threshold as a high signal region.

  Subsequently, the analysis image composition unit 33b reads the 3D-FASE image (step S24), and extracts a blood vessel plaque region from the read 3D-FASE image (step S25). Then, as shown in FIG. 7B, the stable plaque region extraction unit 33 c detects a region whose signal value is equal to or less than a predetermined threshold among the vascular plaque regions extracted by the analysis image composition unit 33 b as a low signal. Extracted as a region (step S26). Further, the stable plaque region extraction unit 33c extracts a region where the signal value exceeds a predetermined threshold as a high signal region.

  Then, as shown in FIG. 7C, the stable plaque region extraction unit 33c defines a portion that becomes a low signal region in both the fat suppression T1-weighted image and the 3D-FASE image as “stable plaque” ( Step S27).

  On the other hand, as shown in FIG. 7C, the unstable plaque candidate extraction unit 33d has a high signal area in both the fat suppression T1-weighted image and the 3D-FASE image, and on the other hand, the low A portion that becomes the signal region and the other becomes the high signal region is extracted as an “unstable plaque candidate” (step S28). That is, the unstable plaque candidate extraction unit 33d estimates the remainder obtained by removing the stable plaque defined by the stable plaque region extraction unit 33c from the vascular plaque region extracted by the analysis image synthesis unit 33b as an unstable plaque candidate. . And the unstable plaque candidate extraction part 33d labels only the extracted unstable plaque candidate, as shown in (d) of FIG.

  Returning to FIG. 3, after the unstable plaque candidate is extracted, the plaque instability calculation unit 33e calculates the instability for quantifying the vulnerability of the plaque from the signal value of the extracted unstable plaque candidate. calculate.

  Thereafter, the display image input unit 34 a reads an arbitrary MR image (hereinafter referred to as “MR morphological image”) that can display the vascular morphology from the storage unit 23. For example, the display image input unit 34a reads a two-dimensional image, a three-dimensional image, an MPR cross-sectional image, an image displaying an MPR cross-section on the three-dimensional image, and the like as an MR form image. The MR morphological image read here is desirably an image captured within the same examination as the fat suppression T1-weighted image and 3D-FASE image read by the image input unit 31. This eliminates the need for detailed image alignment processing.

  Subsequently, the vascular plaque instability analysis result synthesis unit 34b performs unstable plaque on the MR morphological image read by the display image input unit 34a based on the instability calculated by the plaque instability calculation unit 33e. An image in which candidates are highlighted is generated (step S07).

  FIG. 8 is a diagram illustrating a display example of unstable plaque candidates. As shown in FIG. 8, for example, the vascular plaque instability analysis result synthesis unit 34b generates an image in which the position / range of the unstable plaque is highlighted on an image showing the vascular morphology.

  Thereafter, the geometric condition setting unit 34c inputs geometric conditions such as an operator or preset viewpoint position and rotation settings, and the display condition setting unit 34d inputs display conditions such as color / opacity settings. Then, the image display unit 34e displays the image generated by the vascular plaque instability analysis result synthesis unit 34b according to the geometric conditions input by the geometric condition setting unit 34c and the display conditions input by the display condition setting unit 34d. It is displayed on the display unit 25.

  As described above, in the first embodiment, the fat suppression T1-weighted image input unit 31a reads the fat suppression T1-weighted image, and the 3D-FASE image input unit 31b reads the 3D-FASE image. In addition, the vascular plaque extraction unit 32 extracts a vascular plaque region from each of the read fat suppression T1-weighted image and 3D-FASE image. In addition, the vascular plaque analysis unit 33 extracts, as a stable plaque region, a region in which the signal value is equal to or less than a predetermined threshold in both the fat suppression T1-weighted image and the 3D-FASE image, among the extracted vascular plaque regions. Furthermore, the vascular plaque analysis unit 33 estimates an unstable plaque region using the extracted stable plaque region. Therefore, according to the first embodiment, it is possible to support vulnerability analysis of vascular plaque using MR images without performing catheter insertion.

  By the way, in the above-described first embodiment, the case where the blood vessel lumen is extracted from the fat suppression T1-weighted image or the 3D-FASE image has been described, but the present invention is not limited to this. For example, a Black Blood image may be used as an image for extracting a blood vessel lumen. In the Black Blood image, the inside of the blood vessel is drawn black, so that the blood vessel lumen can be extracted with higher accuracy.

  Therefore, in the following, as a second embodiment, a case where a blood vessel lumen is extracted from a Black Blood image will be described. The MRI apparatus according to the second embodiment basically has the same configuration as that shown in FIG. 1, and only the processing performed by the control unit of the computer system is different. The function of the control unit according to the second embodiment will be described.

  First, the configuration of the control unit 40 according to the second embodiment will be described. FIG. 9 is a block diagram illustrating a functional configuration of the control unit 40 according to the second embodiment. Here, for convenience of explanation, functional units that play the same functions as the respective units shown in FIG. 2 are given the same reference numerals, and detailed descriptions thereof are omitted.

  As shown in FIG. 9, the control unit 40 according to the second embodiment particularly includes an image input unit 41, a vascular plaque extraction unit 42, a vascular plaque analysis unit 33, and an image output unit 34. That is, the image input unit 41 and the blood vessel plaque extraction unit 42 are different from the configuration shown in FIG.

  The image input unit 41 inputs the fat-suppressed T1-weighted image, the 3D-FASE image, and the Black Blood image that are reconstructed by the image reconstruction unit 22. Specifically, the image input unit 41 includes a fat suppression T1-weighted image input unit 31a, a 3D-FASE image input unit 31b, and a Black Blood image input unit 41c. That is, the Black Blood image input unit 41c is different from the configuration shown in FIG.

  The vascular plaque extraction unit 42 extracts a vascular plaque region indicating a vascular plaque from each of the fat suppression T1-weighted image, the 3D-FASE image, and the Black Blood image input by the image input unit 41. Specifically, the vascular plaque extraction unit 42 includes a vascular lumen extraction unit 42a, a vascular lumen boundary line extraction unit 32b, and a plaque region estimation unit 32c. That is, the blood vessel lumen extraction unit 42a is different from the configuration shown in FIG.

  Next, processing performed by each unit included in the control unit 40 according to the second embodiment will be described. FIG. 10 is a flowchart illustrating a flow of processing performed by each unit included in the control unit 40 according to the second embodiment.

  As shown in FIG. 10, in the control unit 40 according to the second embodiment, first, the Black Blood image input unit 41c reads a Black Blood image (step S31). Subsequently, the vascular plaque extraction unit 42 estimates a vascular plaque region in the Black Blood image read by the Black Blood image input unit 41c (step S32).

  Here, the estimation of the vascular plaque region according to the second embodiment will be described in detail. FIG. 11 is a flowchart illustrating a flow of estimating a blood vessel plaque region according to the second embodiment. FIG. 12 is a diagram illustrating an example of blood vessel plaque region estimation according to the second embodiment.

  As shown in FIG. 11, in the second embodiment, the Black Blood image input unit 41c reads a Black Blood image (step S41). Subsequently, the blood vessel lumen extraction unit 42a extracts a blood vessel lumen region from the Black Blood image read by the Black Blood image input unit 41c as shown in FIG. (Step S42).

  Subsequently, the blood vessel lumen boundary line extraction unit 32b extracts a blood vessel core line from the blood vessel lumen region extracted by the blood vessel lumen extraction unit 42a. Thereafter, the blood vessel lumen boundary line extraction unit 32b extracts a lumen boundary line (closed curve) perpendicular to the extracted blood vessel core line at regular intervals (step S43).

  Subsequently, as shown in FIG. 12 (b), the plaque region estimation unit 32c performs shape change or area change of the lumen boundary along the blood vessel core line extracted by the blood vessel lumen boundary extraction unit 32b. A prominent location is detected as a “defect location” in which the shape of the outer wall of the blood vessel including the plaque could not be depicted (step S44). Thereafter, as shown in FIG. 12C, the plaque region estimation unit 32c estimates the plaque region by interpolating the defect portion based on the shape of the lumen boundary line around the detected defect portion (step). S45).

  Returning to FIG. 10, after the plaque region is estimated as described above, the vascular plaque region input unit 33a inputs the vascular plaque region extracted by the plaque region estimation unit 32c.

  Further, the fat suppression T1-weighted image input unit 31a reads the fat suppression T1-weighted image (step S33). Then, the analysis image composition unit 33b extracts only the vascular plaque region input by the vascular plaque region input unit 33a from the fat suppression T1 weighted image read by the fat suppression T1 weighted image input unit 31a (step S34). .

  On the other hand, the 3D-FASE image input unit 31b reads the 3D-FASE image (step S35). Then, the analysis image composition unit 33b extracts only the vascular plaque region input by the vascular plaque region input unit 33a from the 3D-FASE image read by the 3D-FASE image input unit 31b (step S36).

  Thereafter, the processes in steps S37 and S38 are the same as the processes in steps S06 and 07 shown in FIG.

  As described above, in the second embodiment, the Black Blood image input unit 41c reads a Black Blood image. Further, the blood vessel plaque extraction unit 42 extracts a blood vessel lumen from the Black Blood image read by the Black Blood image input unit 41c. Then, the vascular plaque extraction unit 42 extracts a vascular plaque region based on the extracted vascular lumen region. Therefore, according to the second embodiment, it is possible to estimate a plaque region with high accuracy by using a Black Blood image having a high contrast resolution between a blood vessel lumen and another tissue.

  In the above-described second embodiment, the case where the fat suppression T1-weighted image, the 3D-FASE image, and the Black Blood image are read has been described. However, each image may be aligned. By performing alignment of the image read by the image input unit 41, it becomes possible to analyze plaque more accurately.

  Therefore, in the following, as a third embodiment, a case where alignment of a fat suppression T1-weighted image, a 3D-FASE image, and a Black Blood image will be described. Note that the MRI apparatus according to the third embodiment basically has the same configuration as that shown in FIG. 1, and only the processing performed by the control unit of the computer system is different. The function of the control unit according to the third embodiment will be described.

  First, the configuration of the control unit 50 according to the third embodiment will be described. FIG. 13 is a block diagram illustrating a functional configuration of the control unit 50 according to the third embodiment. Here, for convenience of explanation, functional units that play the same functions as the respective units shown in FIGS. 2 and 9 are given the same reference numerals, and detailed descriptions thereof are omitted.

  As shown in FIG. 13, the control unit 50 according to the third embodiment particularly includes an image input unit 41, a vascular plaque extraction unit 52, a vascular plaque analysis unit 33, an image output unit 34, and an image registration unit 55. That is, the blood vessel plaque extraction unit 52 and the image registration unit 55 are different from the configurations shown in FIGS.

  The image alignment unit 55 performs alignment of each image based on the blood vessel lumen region of each of the fat suppression T1-weighted image, the 3D-FASE image, and the Black Blood image input by the image input unit 41.

  The vascular plaque extraction unit 52 extracts a vascular plaque region indicating a vascular plaque from each image aligned by the image alignment unit 55. Specifically, the vascular plaque extraction unit 52 includes a vascular lumen extraction unit 52a, a vascular lumen boundary line extraction unit 32b, and a plaque region estimation unit 32c. That is, the blood vessel lumen extraction unit 52a is different from the configuration shown in FIGS.

  Next, processing performed by each unit included in the control unit 50 according to the third embodiment will be described. FIG. 14 is a flowchart illustrating a flow of processing performed by each unit included in the control unit 50 according to the third embodiment.

  As shown in FIG. 14, in the control unit 50 according to the third embodiment, first, the fat suppression T1-weighted image input unit 31a reads a fat suppression T1-weighted image (step S51). Further, the Black Blood image input unit 41c reads a Black Blood image (step S52). Further, the 3D-FASE image input unit 31b reads the 3D-FASE image (step S53).

  Subsequently, the image alignment unit 55 extracts a lumen region from each of the read images. Then, the image alignment unit 55 calculates a rotation matrix and an image warping rate using the extracted lumen region and image feature quantity such as MI (Mutual Information), and based on the calculation result, performs linear alignment processing or nonlinearity. An alignment process is executed (step S54).

  Thereafter, the vascular plaque extraction unit 52 estimates a vascular plaque region in the Black Blood image read by the Black Blood image input unit 41c (step S55). At that time, the blood vessel lumen extraction unit 52a extracts a blood vessel lumen region from the Black Blood image read by the Black Blood image input unit 41c using threshold processing or the like.

  Henceforth, since the process of step S56-S59 is the same as the process of step S34-S38 shown in FIG. 10, description is abbreviate | omitted here.

  As described above, in the third embodiment, the image alignment unit 55 is based on the blood vessel lumen regions of the fat suppression T1-weighted image, the 3D-FASE image, and the Black Blood image input by the image input unit 41. Align each image. Basically, if an image group captured in the same examination is used, alignment between images is unnecessary. However, according to the present embodiment, even when accurate analysis processing is required or when using distorted images, it is possible to support vulnerability analysis of vascular plaque by performing alignment between images. It is.

  By the way, in Examples 1-3, although the case where the unstable plaque candidate was highlighted and displayed on MR form image was demonstrated, this invention is not limited to this. For example, a high-risk blood vessel (hereinafter referred to as “high-risk blood vessel”) may be identified and displayed together with unstable plaque candidates.

  Therefore, hereinafter, as Example 4, a case where a high-risk blood vessel is specified and displayed will be described. Note that the MRI apparatus according to the fourth embodiment basically has the same configuration as that shown in FIG. 1, and only the processing performed by the control unit of the computer system is different. The function of the control unit according to the fourth embodiment will be described.

  First, the configuration of the control unit 60 according to the fourth embodiment will be described. FIG. 15 is a block diagram illustrating a functional configuration of the control unit 60 according to the fourth embodiment. Here, for convenience of explanation, functional units that play the same functions as those shown in FIGS. 2, 9, and 13 are given the same reference numerals, and detailed descriptions thereof are omitted.

  As shown in FIG. 15, the control unit 60 according to the fourth embodiment includes, in particular, an image input unit 41, a vascular plaque extraction unit 42, a vascular plaque analysis unit 33, an image output unit 64, and a high-risk vascular analysis unit 66. . That is, the image output unit 64 and the high-risk blood vessel analysis unit 66 are different from the configurations shown in FIGS. 2, 9, and 13.

  The high-risk blood vessel analysis unit 66 thins the blood vessel lumen region extracted by the blood vessel plaque extraction unit 42, and generates blood vessel branch structure information indicating the branch structure of the blood vessel based on the thinned blood vessel lumen region structure. To do. Then, the high-risk blood vessel analysis unit 66 identifies a high-risk blood vessel that is a high-risk blood vessel based on the generated blood vessel branch structure information and the positional relationship of the unstable plaque region estimated by the blood vessel plaque analysis unit 33. . Specifically, the high risk blood vessel analysis unit 66 includes a blood vessel thinning unit 66a, a blood vessel branch point unit 66b, a blood vessel branch structure generation unit 66c, and a high risk blood vessel branch detection unit 66d.

  The image output unit 64 outputs the unstable plaque region estimated by the vascular plaque analysis unit 33 and the high risk blood vessels identified by the high risk blood vessel analysis unit 66 to the display unit 25. Specifically, the image output unit 64 includes a display image input unit 34a, a vascular plaque instability analysis result synthesis unit 64b, a geometric condition setting unit 34c, a display condition setting unit 34d, and an image display unit 34e. That is, the vascular plaque instability analysis result synthesis unit 64b is different from the configuration shown in FIG. 2, FIG. 9, and FIG.

  Next, processing performed by each unit included in the control unit 60 according to the fourth embodiment will be described. FIG. 16 is a flowchart illustrating a flow of processing performed by each unit included in the control unit 60 according to the fourth embodiment.

  As shown in FIG. 16, in the control unit 60 according to the fourth embodiment, first, the fat suppression T1-weighted image input unit 31a reads a fat suppression T1-weighted image (step S61). Further, the Black Blood image input unit 41c reads a Black Blood image (Step S62). In addition, the 3D-FASE image input unit 31b reads the 3D-FASE image (step S63).

  Subsequently, the vascular plaque extraction unit 42 estimates a vascular plaque region in the Black Blood image read by the Black Blood image input unit 41c (step S64). Thereafter, the vascular plaque region input unit 33a inputs the vascular plaque region extracted by the plaque region estimation unit 32c.

  Then, the analysis image composition unit 33b extracts only the vascular plaque region input by the vascular plaque region input unit 33a from the fat suppression T1 weighted image read by the fat suppression T1 weighted image input unit 31a (step S65). .

  On the other hand, the analysis image composition unit 33b extracts only the vascular plaque region input by the vascular plaque region input unit 33a from the 3D-FASE image read by the 3D-FASE image input unit 31b (step). S66).

  Subsequently, the stable plaque region extraction unit 33c extracts a portion where the signal values in the blood vessel plaque region of the fat suppression T1-weighted image and the 3D-FASE image are both low signals, and the extracted portion is labeled as “stable plaque”. To do. Furthermore, the unstable plaque candidate extraction unit 33d extracts pixels that are not labeled as stable plaques by the stable plaque region extraction unit 33c as “unstable plaque candidates” (step S68).

  Further, the high-risk blood vessel analysis unit 66 converts the blood vessel lumen region into the blood vessel branch structure information (step S67), and identifies the high-risk blood vessel based on the blood vessel branch structure information and the positional relationship between the unstable plaque candidates ( Step S69).

  Here, the identification of the high-risk blood vessel according to the fourth embodiment will be described in detail. FIG. 17 is a flowchart illustrating a specific flow of a high-risk blood vessel according to the fourth embodiment. FIG. 18 is a diagram illustrating an example of specifying a high-risk blood vessel according to the fourth embodiment.

  As shown in FIG. 17, in the fourth embodiment, the Black Blood image input unit 41c reads a Black Blood image (step S71). Subsequently, the blood vessel lumen extraction unit 42a extracts a blood vessel lumen region from the Black Blood image read by the Black Blood image input unit 41c using threshold processing or the like (step S72).

  Thereafter, the blood vessel thinning unit 66a thins the blood vessel lumen region extracted by the blood vessel lumen extraction unit 42a as shown in FIGS. 18A and 18B (step S73). At this time, the blood vessel thinning unit 66a uses, for example, a generally known thinning process.

  Subsequently, as shown in FIG. 18B, the blood vessel branch point 66b extracts a blood vessel branch point (intersection of blood vessel core lines) from the structure of the blood vessel lumen region thinned by the blood vessel thinning unit 66a. . Then, the vascular branch structure generation unit 66c generates vascular branch structure information indicating the branch structure of the blood vessel based on the vascular branch point extracted by the vascular branch point unit 66b (step S74).

  Subsequently, the high-risk vascular branch detection unit 66d determines whether or not an unstable plaque candidate exists in the range of an arbitrary branch in the vascular branch structure information generated by the vascular branch structure generation unit 66c. Then, when an unstable plaque candidate exists within the range of an arbitrary branch, the high-risk blood vessel branch detection unit 66d labels the corresponding branch as a high-risk blood vessel as shown in FIG. 18 (c). (Step S75).

  Thereafter, the image output unit 64 highlights the high-risk blood vessels labeled on the MR form image (step S76). At that time, the vascular plaque instability analysis result synthesis unit 64b adds the instability calculated by the plaque instability calculation unit 33e and information on the high-risk blood vessels labeled by the high-risk vascular branch detection unit 66d. Based on this, an image in which high-risk blood vessels and unstable plaque candidates are highlighted on the MR morphological image is generated.

  FIG. 19 is a diagram illustrating a display example of high-risk blood vessels and unstable plaque candidates. As illustrated in FIG. 19, for example, the vascular plaque instability analysis result synthesis unit 64 b generates an image in which the positions and ranges of high-risk blood vessels and unstable plaques are highlighted on an image showing the vascular morphology. .

  As described above, in the fourth embodiment, the high-risk blood vessel analysis unit 66 thins the blood vessel lumen region extracted by the blood vessel plaque extraction unit 42, and based on the thinned blood vessel lumen region structure, Blood vessel branch structure information indicating a blood vessel branch structure is generated. Then, the high-risk blood vessel analysis unit 66 identifies a high-risk blood vessel based on the generated blood vessel branch structure information and the positional relationship of the unstable plaque region estimated by the blood vessel plaque analysis unit 33. Therefore, according to the fourth embodiment, it is possible to explicitly indicate blood vessels that are at a high risk due to unstable plaque, so that it is possible to accurately support examination of treatment methods and treatment methods.

  By the way, in Examples 1-4, although the MRI apparatus was demonstrated, this invention is not necessarily restricted to this. For example, the present invention can be similarly applied to an image management system having various servers and clients connected via a network. Thus, hereinafter, a case where the present invention is applied to an image management system will be described as a fifth embodiment.

  First, the overall configuration of the image management system according to the fifth embodiment will be described. FIG. 20 is a diagram illustrating the overall configuration of the image management system according to the fifth embodiment. As shown in FIG. 20, the image management system includes an MRI apparatus 80, a vascular plaque instability analysis apparatus 70, and an image output apparatus 90 connected via a network.

  The MRI apparatus 80 uses the magnetic resonance phenomenon to image the inside of the subject. Specifically, the MRI apparatus 80 includes an imaging condition input unit 81, an image imaging unit 82, an image reconstruction unit 83, a captured image storage unit 84, and a captured image transmission unit 85.

  The imaging condition input unit 81 accepts various imaging conditions input by the operator. The image capturing unit 82 applies an RF pulse and a gradient magnetic field to the subject based on the imaging condition received by the imaging condition input unit 81, thereby collecting magnetic resonance signals emitted from the subject.

  The image reconstruction unit 83 reconstructs an image based on the magnetic resonance signals collected by the image capturing unit 82. The captured image storage unit 84 stores the image reconstructed by the image reconstruction unit 83. The captured image transmission unit 85 transmits the image stored in the captured image storage unit 84 to the vascular plaque instability analysis device 70 together with accompanying information.

  The vascular plaque instability analyzer 70 analyzes the vascular plaque instability based on the MR image captured by the MRI apparatus 80. Specifically, the vascular plaque instability analysis device 70 includes a captured image reception unit 77, an image input unit 71, a vascular plaque extraction unit 42, a vascular plaque analysis unit 33, an image output unit 74, and a display image transmission unit 78. . Here, for convenience of explanation, functional units that play the same functions as the respective units shown in FIG. 9 are given the same reference numerals, and detailed descriptions thereof are omitted.

  The captured image receiving unit 77 receives an image transmitted from the MRI apparatus 80.

  The image input unit 71 inputs an image received by the captured image receiving unit 77. Specifically, the image input unit 71 includes a fat suppression T1-weighted image input unit 71a, a 3D-FASE image input unit 71b, and a Black Blood image input unit 71c.

  Here, the fat suppression T1-weighted image input unit 71a reads the fat suppression T1-weighted image received by the captured image reception unit 77. The 3D-FASE image input unit 71 b reads the 3D-FASE image received by the captured image reception unit 77. The Black Blood image input unit 71 c reads the Black Blood image received by the captured image receiving unit 77.

  The image output unit 74 stores the unstable plaque region estimated by the vascular plaque analysis unit 33 in a storage device (not shown). Specifically, the image output unit 74 includes an image / analysis result synthesis unit 74b, a geometric condition setting unit 34c, a display condition setting unit 34d, and a display image generation unit 74f.

  Here, the image / analysis result synthesis unit 74b is unstable on the MR morphological image based on the analysis result of the vascular plaque instability analysis performed by the vascular plaque analysis unit 33, as in the embodiment described above. Generate images that emphasize plaques and high-risk blood vessels. The display image generation unit 74f displays the image based on the image generated by the image / analysis result combining unit 74b according to the geometric condition input by the geometric condition setting unit 34c and the display condition input by the display condition setting unit 34d. An image is generated and stored in a storage device (not shown).

  The display image transmission unit 78 transmits the display image generated by the image output unit 74 to the image output device 90 in response to a request from the image output device 90.

  The image output device 90 displays the MR image captured by the MRI device 80 and the analysis result by the vascular plaque instability analysis device 70. Specifically, the image output device 90 includes a display image receiving unit 91 and a display output unit 92.

  The display image receiving unit 91 receives the display image transmitted from the vascular plaque instability analyzer 70. The display output unit 92 outputs the display image received by the display image receiving unit 91 to the display device.

  Next, a processing procedure of the image management system according to the fifth embodiment will be described. FIG. 21 is a flowchart of the process procedure of the image management system according to the fifth embodiment.

  As shown in FIG. 21, in the image management system according to the fifth embodiment, first, in the MRI apparatus 80, the image capturing unit 82 performs fat suppression T1 enhancement according to a series of sequences received by the capturing condition input unit 81. An image, a 3D-FASE image, and a Black Blood image are photographed (step S81).

  Subsequently, the captured image storage unit 84 stores each image captured by the image capturing unit 82 in a storage device (not shown) in the MRI apparatus 80 or an external storage device (step S82). Then, the captured image transmission unit 85 transmits the image stored by the captured image storage unit 84 to the vascular plaque instability analyzer 70.

  Thereafter, in the vascular plaque instability analysis device 70, the captured image reception unit 77 receives the image information and supplementary information of each image transmitted from the MRI device 80. Then, the image input unit 71 inputs each image received by the captured image receiving unit 77. Further, the vascular plaque extraction unit 42 and the vascular plaque analysis unit 33 perform vascular plaque instability analysis in the same manner as in the previously described embodiment (step S83).

  Subsequently, the image output unit 74 associates the result of the vascular plaque instability analysis performed by the vascular plaque analysis unit 33 with the image information transmitted from the MRI apparatus 80, and the vascular plaque instability analysis (not shown) is performed. The data is stored in a storage device in the device or an external storage device (step S84).

  Thereafter, when the user activates the application installed in the image output device 90, the display image transmission unit 78 analyzes the vascular plaque instability associated with the display image in response to a request from the image output device 90. The result is read from the storage device and transmitted to the image output device 90. In the image output device 90, when the display image receiving unit 91 receives the plaque instability analysis result, the display output unit 92 outputs the received plaque instability analysis result to the display device (step S85). ).

  As described above, in the fifth embodiment, the fat suppression T1 weighted image input unit 71a reads the fat suppression T1 weighted image received by the shot image reception unit 77, and the 3D-FASE image input unit 71b reads the shot image. The 3D-FASE image received by the receiving unit 77 is read. In addition, the vascular plaque extraction unit 42 extracts a vascular plaque region from each of the read fat suppression T1-weighted image and 3D-FASE image. In addition, the vascular plaque analysis unit 33 extracts, as a stable plaque region, a region in which the signal value is equal to or less than a predetermined threshold in both the fat suppression T1-weighted image and the 3D-FASE image, among the extracted vascular plaque regions. Furthermore, the vascular plaque analysis unit 33 estimates an unstable plaque region using the extracted stable plaque region. Then, the display image transmission unit 78 causes the image output device to output the estimated unstable plaque region. Therefore, according to the fifth embodiment, it is possible to support vulnerability analysis of vascular plaque using MR images without performing catheter insertion. In addition, since unstable plaque analysis processing is automatically performed after image capture, there is less waiting time from the image output device 90 requesting display of the analysis result until the analysis result is displayed, and the throughput of the workflow Can be improved.

  In the above-described embodiment, the case where the fat suppression T1-weighted image, the 3D-FASE image, and the Black Blood image are used has been described. However, the present invention is not limited to this, and can be similarly applied even when an image captured by another imaging method is used.

  As described above, according to the above embodiment, rather than starting the vascular plaque stability analysis from the start of the diagnostic work, the analysis process is automatically performed after image capturing, so the waiting time is small, The effect of improving the throughput of the workflow can be expected.

  In addition, according to the above embodiment, since it is possible to automatically perform vascular plaque vulnerability assessment using MR images, which has been difficult in the past, catheter examinations and CT examinations can be avoided. Can be reduced. In addition, since vascular plaque candidates having a high risk of rupture can be automatically displayed, application determination such as CAS (Carotid Artery Stenting) can be efficiently performed. As a result, it is possible to increase the chance of shifting from the conventionally implemented CEA (Carotid Endarterestomy) to a less invasive CAS.

DESCRIPTION OF SYMBOLS 100 MRI apparatus 1 Static magnetic field magnet 2 Gradient magnetic field coil 3 Gradient magnetic field power supply 4 Bed 4a Top plate 5 Bed control part 6 Transmission RF coil 7 Transmission part 8 Reception RF coil 9 Reception part 10 Sequence control part 20 Computer system 21 Interface part 22 Image Reconstruction unit 23 Storage unit 24 Input unit 25 Display unit 30, 40, 50, 60 Control unit 31, 41 Image input unit 31a Fat suppression T1-weighted image input unit 31b 3D-FASE image input unit 32, 42, 52 Blood vessel plaque extraction Units 32a, 42a, 52a Blood vessel lumen extraction unit 32b Blood vessel lumen boundary line extraction unit 32c Plaque region estimation unit 33 Blood vessel plaque analysis unit 33a Blood vessel plaque region input unit 33b Analysis image composition unit 33c Stable plaque region extraction unit 33d Unstable Plaque candidate extraction unit 33e Plaque instability calculation unit 34, 64 Image output unit 34a Display image input unit 34b, 64b Vascular plaque instability analysis result synthesis unit 34c Geometric condition setting unit 34d Display condition setting unit 34e Image display unit 41c Black Blood image input unit 55 Image alignment unit 66 High Risk blood vessel analysis unit 66a Blood vessel thinning unit 66b Blood vessel branch point unit 66c Blood vessel branch structure generation unit 66d High risk blood vessel branch detection unit 70 Blood vessel instability analysis device 71 Image input unit 71a Fat suppression T1-weighted image input unit 71b 3D- FASE image input unit 71c Black Blood image input unit 74 Image output unit 74b Image / analysis result synthesis unit 74f Display image generation unit 77 Captured image reception unit 78 Display image transmission unit 80 MRI apparatus 81 Imaging condition input unit 82 Image capture unit 83 Image Reconstruction unit 84 Captured image storage unit 85 Captured image transmission unit 90 Image output Device 91 display the image receiving unit 92 display output unit

Claims (8)

First image input means for inputting a first image captured by a first imaging method using a magnetic resonance imaging apparatus;
Second image input means for inputting a second image captured by a second imaging method different from the first imaging method using a magnetic resonance imaging apparatus;
Blood vessel plaque region extraction means for extracting a blood vessel plaque region indicating a blood vessel plaque from each of the first image inputted by the first image input means and the second image inputted by the second image input means. When,
Of the vascular plaque regions extracted by the vascular plaque region extracting means, a stable plaque region that extracts, as a stable plaque region, a region having a signal value equal to or smaller than a predetermined threshold value in both the first image and the second image. Extraction means;
An image processing apparatus comprising: an unstable plaque estimation unit that estimates an unstable plaque region using the stable plaque region extracted by the stable plaque region extraction unit.   The unstable plaque estimation means determines the remaining vascular plaque area that has not been extracted as the stable plaque area by the stable plaque area extraction means from the vascular plaque area extracted by the vascular plaque area extraction means. The image processing apparatus according to claim 1, wherein the image processing apparatus estimates the plaque. A blood vessel lumen extracting unit for extracting a blood vessel lumen region from the first image input by the first image input unit or the second image input by the second image input unit;
3. The image processing apparatus according to claim 1, wherein the vascular plaque region extracting unit extracts the vascular plaque region based on the vascular lumen region extracted by the vascular lumen extracting unit. . A third image input means for inputting a third image picked up by the Black Blood method for picking up an image so that the luminance value of blood is lowered using a magnetic resonance imaging apparatus;
Blood vessel lumen extraction means for extracting a blood vessel lumen region from the third image input by the third image input means;
3. The image processing apparatus according to claim 1, wherein the vascular plaque region extracting unit extracts the vascular plaque region based on the vascular lumen region extracted by the vascular lumen extracting unit. .   The image processing apparatus according to claim 1, further comprising an image alignment unit configured to align each image based on a blood vessel lumen region of the input image. A blood vessel branch structure generating unit that thins the blood vessel lumen region extracted by the blood vessel lumen extracting unit, and generates blood vessel branch structure information indicating a branch structure of the blood vessel based on the thinned structure of the blood vessel lumen region; ,
A high-risk blood vessel that is a high-risk blood vessel is identified on the basis of the vascular branch structure information generated by the vascular branch structure corrective means and the positional relationship of the unstable plaque region estimated by the unstable plaque estimation means The image processing apparatus according to claim 1, further comprising: a high-risk blood vessel detection unit that performs First image input means for inputting a first image imaged by the first imaging method;
Second image input means for inputting a second image picked up by a second image pickup method different from the first image pickup method;
Blood vessel plaque region extraction means for extracting a blood vessel plaque region indicating a blood vessel plaque from each of the first image inputted by the first image input means and the second image inputted by the second image input means. When,
Of the vascular plaque regions extracted by the vascular plaque region extracting means, a stable plaque region that extracts, as a stable plaque region, a region having a signal value equal to or smaller than a predetermined threshold value in both the first image and the second image. Extraction means;
A magnetic resonance imaging apparatus comprising: an unstable plaque estimation unit that estimates an unstable plaque region using the stable plaque region extracted by the stable plaque region extraction unit. An image management system for displaying an image captured by a magnetic resonance imaging apparatus on an image output apparatus,
First image input means for inputting a first image imaged by a first imaging method using the magnetic resonance imaging apparatus;
Second image input means for inputting a second image picked up by a second image pickup method different from the first image pickup method using the magnetic resonance imaging apparatus;
Blood vessel plaque region extraction means for extracting a blood vessel plaque region indicating a blood vessel plaque from each of the first image inputted by the first image input means and the second image inputted by the second image input means. When,
Of the vascular plaque regions extracted by the vascular plaque region extracting means, a stable plaque region that extracts, as a stable plaque region, a region having a signal value equal to or smaller than a predetermined threshold value in both the first image and the second image. Extraction means;
An unstable plaque estimation means for estimating an unstable plaque area using the stable plaque area extracted by the stable plaque area extraction means, and an unstable plaque area estimated by the unstable plaque estimation means as the image output device. An image management system comprising: an image output means for outputting the image to the computer.

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