JP2014046109A - Medical image processor - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a medical image processor capable of simply calculating intravascular blood flow information at a high speed.SOLUTION: A medical image processor of an embodiment includes a 2D image generation part, and a calculation part. The 2D image generation part generates a two-dimensional tomographic image of a blood vessel in which a feature of a lesioned part in a blood vessel included in three-dimensional medical image data is reflected, from the three-dimensional medical image data. Then, the calculation part calculates fluid information in the blood vessel by applying calculation hydrodynamics to the two-dimensional image generated by the 2D image generation part.

Description

  Embodiments described herein relate generally to a medical image processing apparatus.

  Conventionally, CT (Computed Tomography) angiography, angiography, and other contrast examinations are used in the diagnosis of ischemic heart disease that develops due to stenosis of the coronary artery and insufficient blood flow. However, it is difficult to determine whether or not to perform percutaneous coronary intervention (PCI) only by information obtained from these contrast examinations. PCI is a treatment method for increasing blood flow by dilating a stenotic lesion using a catheter, and a stent may be placed in the dilated lesion.

  As a method used for determining whether or not to perform PCI, for example, a method of measuring a fractional flow reserve (FFR) is known. FFR is the ratio of the coronary artery pressure on the peripheral side to the coronary artery pressure on the aorta side across the lesion site. For example, when the FFR is “0.80 or less”, it is determined to perform PCI. Here, since the measurement of FFR requires an invasive treatment using a pressure wire, it takes time and effort.

  Therefore, in recent years, a method for calculating FFR by a computational fluid dynamic technique using 3D data of coronary arteries has been proposed. However, the above-described prior art is not a practical method because it requires a long time calculation.

JP 2012-081254 A

  The problem to be solved by the present invention is to provide a medical image processing apparatus that makes it possible to calculate blood flow information in a blood vessel simply and at high speed.

  The medical image processing apparatus according to the embodiment includes a generation unit and a calculation unit. The generation unit generates a two-dimensional cross-sectional image of the tubular structure reflecting the feature of a lesion site in the tubular structure included in the three-dimensional medical image data from the three-dimensional medical image data. The calculation means calculates fluid information in the tubular structure by applying computational fluid dynamics to the two-dimensional image generated by the generation means.

FIG. 1 is a diagram illustrating an example of a configuration of a medical image processing apparatus according to the first embodiment. FIG. 2 is a diagram illustrating an example of Valsalva Cave extracted by the 2D image generation unit according to the first embodiment. FIG. 3A is a diagram schematically illustrating an example of the core line extraction of the Valsalva sinus by the 2D image generation unit according to the first embodiment. FIG. 3B is a diagram schematically illustrating an example of Valsalva sin feature point extraction by the 2D image generation unit according to the first embodiment. FIG. 3C is a diagram schematically illustrating an example of display cross section determination of Valsalva cavities by the 2D image generation unit according to the first embodiment. FIG. 3D is a diagram schematically illustrating an example of stenosis site extraction by the 2D image generation unit according to the first embodiment. FIG. 3E is a diagram schematically illustrating an example of generation of a CPR image by the 2D image generation unit according to the first embodiment. FIG. 3F is a diagram schematically illustrating an example of CPR image generation by the 2D image generation unit according to the first embodiment. FIG. 4 is a diagram illustrating an example of the CPR image generated by the 2D image generation unit according to the first embodiment. FIG. 5A is a diagram illustrating an example of blood vessel wall extraction on the CPR image by the calculation unit according to the first embodiment. FIG. 5B is a diagram illustrating an example of calculation of blood flow information by the calculation unit according to the first embodiment. FIG. 6A is a diagram illustrating a first example of a display screen displayed by the control of the display control unit according to the first embodiment. FIG. 6B is a diagram illustrating a second example of the display screen displayed by the control of the display control unit according to the first embodiment. FIG. 6C is a diagram illustrating a third example of the display screen displayed by the control of the display control unit according to the first embodiment. FIG. 7 is a flowchart illustrating a processing procedure performed by the medical image processing apparatus according to the first embodiment.

(First embodiment)
FIG. 1 is a diagram illustrating an example of a configuration of a medical image processing apparatus 100 according to the first embodiment. As illustrated in FIG. 1, the medical image processing apparatus 100 includes an input unit 110, a display unit 120, a communication unit 130, a storage unit 140, and a control unit 150. For example, the medical image processing apparatus 100 is a workstation, an arbitrary personal computer, or the like, and is connected to a medical image diagnosis apparatus or an image storage apparatus (not shown) via a network. Examples of the medical image diagnostic apparatus include an X-ray diagnostic apparatus, an X-ray CT (Computed Tomography) apparatus, and an MRI (Magnetic Resonance Imaging) apparatus. The medical image diagnostic apparatus can generate three-dimensional medical image data (for example, three-dimensional image data of a contrasted heart). The image storage device is a database that stores medical images. Specifically, the image storage device stores the three-dimensional medical image data transmitted from the medical image diagnostic device in the storage unit and stores it.

  The medical image processing apparatus 100, the medical image diagnostic apparatus, and the image storage apparatus described above can communicate with each other directly or indirectly by, for example, a hospital LAN (Local Area Network) installed in the hospital. It is in a state. For example, when PACS (Picture Archiving and Communication System) is introduced, each device transmits and receives medical images and the like according to DICOM (Digital Imaging and Communications in Medicine) standards.

  The input unit 110 is a mouse, a keyboard, a trackball, or the like, and receives input of various operations on the medical image processing apparatus 100 from an operator. Specifically, the input unit 110 stores, as an image storage device, three-dimensional medical image data to be processed that is a target for analyzing a fluid flow (for example, blood flow) in a tubular structure (for example, a blood vessel). Accepts input of information to obtain from For example, the input unit 110 receives an input for acquiring a CT image obtained by photographing the heart of a patient with ischemic heart disease. The input unit 110 also accepts an input operation for editing the core wire of the tubular structure extracted by the control unit 150 described later.

  The display unit 120 is a liquid crystal panel or the like as a stereoscopic display monitor, and displays various types of information. Specifically, the display unit 120 displays a GUI (Graphical User Interface) for receiving various operations from the operator, a 2D image generated by processing by the control unit 150 described later, and the like. The two-dimensional image generated by the control unit 150 will be described later. The communication unit 130 is a NIC (Network Interface Card) or the like, and performs communication with other devices.

  As illustrated in FIG. 1, the storage unit 140 includes an image data storage unit 141, a 2D image storage unit 142, and a determination condition storage unit 143. For example, the storage unit 140 is a hard disk, a semiconductor memory element, or the like, and stores various types of information. The image data storage unit 141 stores three-dimensional medical image data acquired from the image storage device via the communication unit 130. The 2D image storage unit 142 stores image data being processed by the control unit 150 to be described later, a 2D image group generated by the processing, and the like.

  The determination condition storage unit 143 stores conditions used for determination processing by the control unit 150 described later. Specifically, the determination condition storage unit 143 stores a condition for determining whether or not a lesion site in the tubular structure needs to be treated. For example, the determination condition storage unit 143 stores a condition for determining whether or not to perform PCI on the coronary artery. For example, the determination condition storage unit 143 stores a determination condition that “PCI” needs to be performed when “FFR: 0.80 or less”.

  The control unit 150 is, for example, an electronic circuit such as a CPU (Central Processing Unit) or MPU (Micro Processing Unit), an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array), and medical image processing Overall control of the apparatus 100 is performed.

  Further, as illustrated in FIG. 1, the control unit 150 includes, for example, an image acquisition unit 151, a 2D image generation unit 152, a calculation unit 153, and a display control unit 154. And the control part 150 calculates the various information regarding the flow of the fluid in the tubular structure contained in three-dimensional medical image data. Hereinafter, a case where the coronary artery is taken as an example of the tubular structure and blood flow information in the coronary artery is calculated will be described.

  The image acquisition unit 151 acquires three-dimensional medical image data from an image storage device (not shown) via the communication unit 130 and stores it in the image data storage unit 141. For example, the image acquisition unit 151 acquires three-dimensional medical image data corresponding to information input from the operator via the input unit 110 from the image storage device via the communication unit 130. For example, the image acquisition unit 151 acquires the three-dimensional image data of the heart collected by the X-ray CT apparatus and stores it in the image data storage unit 141.

  The 2D image generation unit 152 generates a two-dimensional cross-sectional image of the tubular structure reflecting the feature of a lesion site in the tubular structure included in the three-dimensional medical image data from the three-dimensional medical image data. . Specifically, the 2D image generation unit 152 extracts a core line of the tubular structure, and generates a CPR (Curved Multi Planer Reconstruction) image in which the tubular structure is developed along the extracted core line. More specifically, the 2D image generation unit 152 extracts the lumen of the tubular structure, and generates the CPR image so that the stenosis rate (for example, inner diameter) of the extracted lumen is minimized or maximized. To do. First, the 2D image generation unit 152 reads out the 3D medical image data designated by the operator via the input unit 110 from the image data storage unit 140, and the 2D image included in the read out 3D medical image data. Extract the objects to be generated.

  For example, the 2D image generation unit 152 reads the contrasted three-dimensional image data of the heart from the image data storage unit 141, and extracts a heart region from the read three-dimensional image data. For example, the 2D image generation unit 152 generates 3D image data based on a CT value for each voxel of 3D image data, an area expansion method using MRI signal intensity, a human body atlas, or the like. The heart region included in is extracted. The human atlas is a database in which three-dimensional structures and functions of organs are aggregated. Further, the method for extracting the heart region from the three-dimensional image data is not limited to the method described above, and other existing techniques can be applied.

  Then, the 2D image generation unit 152 extracts an aortic sinus (valsalva sinus) from the extracted heart region. FIG. 2 is a diagram illustrating an example of Valsalva cavities extracted by the 2D image generation unit 152 according to the first embodiment. As shown in FIG. 2, the Valsalva sinus 10 including the coronary artery 20 is extracted from the heart region. Although only one coronary artery is shown in FIG. 2, in practice, the 2D image generation unit 152 extracts the left coronary artery including the left anterior descending branch and the left circumflex branch, and the right coronary artery, respectively. . The Valsalva sinus 10 including the coronary artery is extracted by a technique using a human body atlas.

  Then, the 2D image generation unit 152 extracts the core line of the Valsalva sinus by using a method of thinning the inner region with respect to the extracted Valsalva sinus. FIG. 3A is a diagram schematically illustrating an example of the core line extraction of Valsalva sin by the 2D image generation unit 152 according to the first embodiment. For example, as illustrated in FIG. 3A, the 2D image generation unit 152 extracts the extracted core wire 30 of the Valsalva sinus 10.

  Thereafter, the 2D image generation unit 152 extracts feature points of Valsalva Cave. FIG. 3B is a diagram schematically illustrating an example of Valsalva sin feature point extraction by the 2D image generation unit 152 according to the first embodiment. For example, as illustrated in FIG. 3B, the 2D image generation unit 152 includes the intersection 41 between the extracted core line and the upper end portion of the Valsalva sinus (or the upper end part of the image from which the Valsalva sinus is extracted), and the lower end of the core line and the Valsalva sinus. The intersection 42 with the part (or the lower end of the image from which the Valsalva sinus is extracted) and the starting part 43 of the coronary artery 20 are extracted. In FIG. 3B, a drawing of extracting one starting portion 43 is shown, but actually the starting portions of the left coronary artery and the left coronary artery are extracted.

  Then, the 2D image generation unit 152 determines a display cross section of the Valsalva sinus based on the extracted feature points of the Valsalva sinus. FIG. 3C is a diagram schematically illustrating an example of display cross section determination of Valsalva sin by the 2D image generation unit 152 according to the first embodiment. For example, as illustrated in FIG. 3C, the 2D image generation unit 152 determines the plane 50 defined by the intersection point 41, the intersection point 42, and the starting portion 43 as the display cross section of the Valsalva sinus 10. That is, as illustrated in FIG. 3C, the 2D image generation unit 152 determines a plane 50 that is orthogonal to the plane formed by the intersection 41, the intersection 42, and the starter 43 as the display cross section of the Valsalva sinus 10. Note that the above-described display cross section of the Valsalva sinus is determined for each of the left coronary artery and the right coronary artery.

  Then, the 2D image generation unit 152 extracts the core line of the coronary artery 20 based on the extracted start part 43 of the coronary artery 20, and extracts the contour lines of the lumen and the outer wall of the coronary artery based on the extracted core line. . For example, the 2D image generation unit 152 is configured to use a Bessel tracking method (eg, Onno Wink, Wiro J. Niessen, “Fast Delineation and Visualization of Vessel in 3-D Angiographic Images”, IEEE Trans. Med. Imag. Vol. 19, No. .4, 2000) or methods of thinning the internal area of a luminal organ (eg GD Rubin, DS Paik, PC Johnston, S. Napel, “Measurement of the Aorta and Its Branches with Helical CT,” Radiology , Vol.206, No.3, pp.823-9, Mar., 1998.) and the like are extracted.

  Thereafter, the 2D image generation unit 152 extracts a stenosis portion of the coronary artery using, for example, the extracted core line and the contour of the lumen and outer wall of the coronary artery. FIG. 3D is a diagram schematically illustrating an example of stenosis site extraction by the 2D image generation unit 152 according to the first embodiment. For example, as illustrated in FIG. 3D, the 2D image generation unit 152 extracts the core line 31 of the coronary artery and extracts contour lines of the lumen and the outer wall of the coronary artery 20 based on the extracted core line 31. Then, the 2D image generation unit 152 extracts the stenosis region 60 using the extracted core line and the contour lines of the lumen and the outer wall of the coronary artery 20.

  For example, the 2D image generation unit 152 calculates, for each arbitrary position along the core line, a first distance from the core line to the contour line of the lumen and a second distance from the core line to the outer wall. . Then, the 2D image generation unit 152 calculates a ratio of the first distance to the second distance calculated for each position. Then, the 2D image generation unit 152 determines a position where the calculated ratio is lower than a predetermined threshold as a stenosis site.

  Then, the 2D image generation unit 152 generates a CPR image that can be observed in a direction in which the extracted stenosis portion has a minimum diameter. Here, the 2D image generation unit 152 generates a CPR image that can be observed in a direction in which the region other than the stenosis region in the coronary artery has a minimum diameter. 3E and 3F are diagrams schematically illustrating an example of generation of a CPR image by the 2D image generation unit 152 according to the first embodiment. 3F shows a cross-sectional view of the coronary artery shown in FIG. 3E in the short direction.

  For example, the 2D image generation unit 152 generates a two-dimensional cross-sectional image obtained by cutting the coronary artery along the plane 51 along the core line 31 of the coronary artery 20 as illustrated in FIG. 3E. Here, as illustrated in FIG. 3F, the 2D image generation unit 152 cuts the coronary artery in a direction in which the stenosis rate of the lumen of the coronary artery is minimized. That is, as shown in FIG. 3F, the 2D image generation unit 152 generates a cross-sectional image by combining the planes 51 passing through the core wire 31 so that the stenosis rate 52 of the lumen of the coronary artery 20 is the shortest. Then, the 2D image generation unit 152 sequentially generates cross-sectional images in which the stenosis rate of the lumen is the shortest along the core line 31, and generates a CPR image obtained by connecting the generated cross-sectional images.

  FIG. 4 is a diagram illustrating an example of the CPR image generated by the 2D image generation unit 152 according to the first embodiment. For example, as illustrated in FIG. 4, the 2D image generation unit 152 connects the display cross section of the Valsalva sinus 10 and a plurality of cross section images in which the stenosis rate of the lumen is the shortest along the core line 31 in the coronary artery 20. Generate an image. In the above-described example, the case where a cross section with the smallest stenosis rate of the lumen is used has been described. However, the 2D image generation unit 152 uses the cross-sectional image of the cross section with the maximum stenosis rate of the lumen to obtain a CPR image. Can also be generated.

  The 2D image generation unit 152 can also generate a cross-cut image showing a cross-section in the short direction of the tubular structure and an SPR (Stretched CPR) image obtained by expanding the CPR image. That is, the 2D image generation unit 152 generates, for example, a cross-sectional image of the coronary artery as shown in FIG. 3F or an SPR image obtained by expanding the CPR image shown in FIG. Note that the above-described two-dimensional images (CPR image, crosscut image, SPR image, etc.) are generated for the left coronary artery and the right coronary artery, respectively.

  Returning to FIG. 1, the calculation unit 153 calculates fluid information in the tubular structure by applying computational fluid dynamics to the two-dimensional image generated by the 2D image generation unit 152. Specifically, the calculation unit 153 calculates, as the fluid information, parameters including at least the fluid pressure, the flow velocity, and the shear stress for each predetermined position in the tubular structure. For example, when the blood flow information of the coronary artery in the CPR image shown in FIG. 4 is calculated, the calculation unit 153 firstly based on the lumen and outer wall contour information of the coronary artery 20 extracted by the 2D image generation unit 152. The contours of the lumen and the outer wall of the coronary artery 20 on the CPR image are extracted.

  FIG. 5A is a diagram illustrating an example of blood vessel wall extraction on the CPR image by the calculation unit 153 according to the first embodiment. For example, as illustrated in FIG. 5A, the calculation unit 153 extracts the contour 71 of the outer wall of the coronary artery 20 and the contour 72 of the lumen on the CPR image. Then, the calculation unit 153 applies a computational fluid dynamics simulation using the extracted contour line 72 of the lumen as a boundary condition. As an example, the calculation unit 153 executes a computational fluid dynamics simulation according to the following equations (1) and (2). In Equation (1), “v” indicates a flow velocity, “t” indicates time, “ρ” indicates density, “P” indicates pressure, and “ν” indicates a kinematic viscosity coefficient. “F” indicates an external force term.

  That is, the calculation unit 153 predicts blood flow information by simultaneously combining the Navier-Stokes equation shown in Equation (1) and the continuous equation shown in Equation (2). Here, the calculation unit 153 can use a difference method, a finite element method, or the like as a method for solving the expressions (1) and (2) (Mitsuhiro Tanaka: basic numerical analysis—simulation technique of partial differential equation) See Introductory 2006).

  The calculation unit 153 is based on preset initial conditions (for example, the initial blood flow velocity and internal pressure on the aorta side (Val salva sinus side), the initial blood flow velocity and internal pressure on the peripheral side, the viscosity of blood, etc.) Using the above-described equations (1) and (2), blood flow information (parameters such as pressure, flow velocity and shear stress) within the contour line of the lumen on the CPR image is calculated. The initial condition may be a case of using a typical value that is generally used, or may be a case of using an actual measurement value measured in accordance with the patient.

  FIG. 5B is a diagram illustrating an example of calculation of blood flow information by the calculation unit 153 according to the first embodiment. FIG. 5B shows calculation of blood flow information for the CPR image shown in FIG. 5A. For example, as illustrated in FIG. 5B, the calculation unit 153 calculates parameters such as pressure, flow velocity, and shear stress at each position of the coronary artery 20 with the contour line 72 of the lumen of the coronary artery 20 as a boundary. For example, the calculation unit 153 calculates a parameter in the coronary artery on the aorta side (Valsalva sinus 10 side) as indicated by an arrow 73 in FIG. 5B. Then, the calculation unit 153 changes the position of the coronary artery 20 and calculates parameters in the peripheral coronary artery, for example, as indicated by an arrow 74 in FIG. 5B. FIG. 5B shows the case where parameters are calculated for two positions, but actually, the calculation unit 153 calculates the parameters for each position while gradually shifting the position of the coronary artery 20.

And the calculation part 153 calculates the information used for diagnoses, such as FFR, using the calculated parameter. When calculating FFR, the calculation part 153 calculates FFR in each position of the coronary artery 20 by the following formula (3). In Equation (3), “P _ao ” indicates the coronary artery internal pressure on the aorta side, and “P _dis ” indicates the coronary artery pressure on the peripheral side.

  That is, the calculation unit 153 calculates the ratio of the peripheral coronary artery internal pressure (for example, the internal pressure near the arrow 74 in FIG. 5B) to the coronary internal pressure on the aortic side (for example, the internal pressure near the arrow 73 in FIG. 5B). Here, when calculating the FFR, the calculation unit 153 calculates the FFR at each position where the internal pressure is calculated in the coronary artery.

  And the calculation part 153 determines the necessity of the treatment of a lesion site | part by comparing the calculated fluid information in the tubular structure, and the preset conditions. For example, the calculation unit 153 compares the calculated FFR of the coronary artery with the determination condition stored in the determination condition storage unit 143 to determine whether to perform PCI. For example, the calculation unit 153 determines that it is necessary to execute PCI on the coronary artery 20 when the FFR at the position indicated by the arrow 74 in FIG. 5B is “0.80”. The calculation unit 153 determines whether to perform PCI for each position of the coronary artery 20. In addition, the calculation unit 153 performs the above-described calculation of parameters, FFR, and the like and determination of necessity of treatment of a lesion site for each of the left coronary artery and the right coronary artery.

  Returning to FIG. 1, the display control unit 154 colors any one of the pressure, flow velocity, and shear stress of the fluid included in the parameter according to the value, and superimposes it on the two-dimensional cross-sectional image. It is displayed on the predetermined display unit. FIG. 6A is a diagram illustrating a first example of a display screen displayed by the control of the display control unit 154 according to the first embodiment. In FIG. 6A, the FFR is colored according to the value.

  For example, as shown in FIG. 6A, the display control unit 154 assigns a color to each FFR value at each position of the coronary artery and causes the display unit 120 to display an image superimposed on the CPR image. The correspondence between the FFR value and the color is set in advance by the operator or the designer.

  Further, when the operator designates the position of the blood vessel via the input unit 110, the display control unit 154 further superimposes and displays the FFR value at the designated position, the necessity of PCI, and the like. FIG. 6B is a diagram illustrating a second example of a display screen displayed by the control of the display control unit 154 according to the first embodiment. For example, as shown in FIG. 6B, when the cursor 80 is placed on the coronary artery by the operator, the display control unit 154 determines whether the PCI is necessary and the FFR value “0.80” at the adjusted position. The result “required” is displayed superimposed.

  Further, the display control unit 154 causes the display unit 120 to display the determination result determined by the calculation unit 153. FIG. 6C is a diagram illustrating a third example of the display screen displayed by the control of the display control unit 154 according to the first embodiment. For example, as shown in FIG. 6C, the display control unit 154 causes the display unit 120 to display a list of PCI determination necessity results for each vascular branch of the coronary artery. That is, the display control unit 154 displays on the display unit 120 a list of determination results for determining whether the right coronary artery, the left anterior descending branch, and the left circumflex branch are necessary by the calculation unit 153.

  Then, the display control unit 154 causes the display unit 120 to sequentially display a two-dimensional cross-sectional image of the tubular structure that requires treatment of the lesion site. For example, the display control unit 154 displays the CPR image, the SPR image, the crosscut image, and the like of the right coronary artery in which the necessity determination of PCI is “necessary” in the list illustrated in FIG. . Here, when treatment is necessary in a plurality of blood vessels, the display control unit 154 causes the display unit 120 to display the blood vessels in descending order of risk. Note that the risk level may be determined based on, for example, the FFR value.

  In addition, when the parameter is calculated for each predetermined position in the blood vessel by the calculation unit 153, the display control unit 154 sequentially performs coloring according to the calculated value each time the parameter is calculated. A moving image superimposed on the cross-sectional image is displayed on the display unit 120. For example, the image acquisition unit 151 acquires three-dimensional image data generated by the medical image diagnostic apparatus in real time, and the 2D image generation unit 152 and the calculation unit 153 obtain blood flow information of blood vessels included in the image data. Calculate immediately. The display control unit 154 can convert the calculated blood flow information into color information, sequentially superimpose it on the 2D cross-sectional image, and display it.

  FIG. 7 is a flowchart illustrating a processing procedure performed by the medical image processing apparatus 100 according to the first embodiment. FIG. 7 shows a procedure for processing the coronary artery. As shown in FIG. 7, in the medical image processing apparatus 100 according to the first embodiment, the 2D image generation unit 152 reads a heart CT image from the image data storage unit 141 (step S101), and the read CT image A heart region is extracted from (step S102).

  Then, the 2D image generation unit 152 extracts the Valsalva sinus (aortic sinus) from the extracted heart region (Step S103), and extracts the core line of the Valsalva sinus (Step S104). Thereafter, the 2D image generation unit 152 extracts an intersection between the core line of Valsalva sinus and the upper end (step S105). In addition, the 2D image generation unit 152 extracts an intersection between the core line and the lower end of the Valsalva sin (step S106). Then, the 2D image generation unit 152 extracts the origin of the coronary artery (step S107) and determines the display cross section of the Valsalva sinus (step S108).

  Thereafter, the 2D image generation unit 152 extracts the core line of the coronary artery (step S109), and detects the lumen of the coronary artery (step S110). Subsequently, the 2D image generation unit 152 detects the stenosis of the coronary artery (step S111), and extracts the minimum diameter of the coronary artery including the stenosis (step S112). Thereafter, the 2D image generation unit 152 determines a cross section of the coronary artery that passes through the extracted minimum diameter (step S113). Then, the 2D image generation unit 152 generates a CPR image in which the determined cross section of the coronary artery and the display cross section of the Valsalva sinus are smoothly connected (step S114).

  Thereafter, the calculation unit 153 extracts the contour line of the blood vessel wall of the CPR image generated by the 2D image generation unit 152 (step S115), and applies the computational fluid dynamics simulation (step S116). Then, the display control unit 154 displays the simulation result on the display unit 120 (step S117), and ends the process.

  As described above, according to the first embodiment, the 2D image generation unit 152 generates a two-dimensional cross-sectional image of a blood vessel that reflects the characteristics of a lesion site in the blood vessel included in the three-dimensional medical image data. Generated from three-dimensional medical image data. Then, the calculation unit 153 calculates fluid information in the blood vessel by applying computational fluid dynamics to the two-dimensional image generated by the 2D image generation unit 152. Therefore, the medical image processing apparatus 100 according to the first embodiment makes it possible to calculate intravascular blood flow information simply and at high speed.

  Further, according to the first embodiment, the 2D image generation unit 152 extracts a blood vessel core line, and generates a CPR image in which the blood vessel is expanded along the extracted core line. Therefore, the medical image processing apparatus 100 according to the first embodiment can calculate an accurate calculation result when applying computational fluid dynamics.

  Further, according to the first embodiment, the 2D image generation unit 152 extracts the lumen of the blood vessel, and generates a CPR image so that the stenosis rate of the extracted lumen is minimized or maximized. Therefore, the medical image processing apparatus 100 according to the first embodiment can generate a cross-sectional image in which the state of stenosis is reflected, and can calculate a more accurate calculation result.

  Further, according to the first embodiment, the 2D image generation unit 152 generates a cross cut image indicating a cross section in the short direction of the blood vessel and an SPR image obtained by expanding the CPR image as a two-dimensional cross section image. Therefore, the medical image processing apparatus 100 according to the first embodiment can flexibly cope with various states of the lesion site of the blood vessel.

  Further, according to the first embodiment, the calculation unit 153 calculates, as the fluid information, parameters including at least blood pressure, flow velocity, and shear stress for each predetermined position in the blood vessel. Then, the display control unit 154 colors any one of the blood pressure, the flow velocity, and the shear stress included in the parameter according to the value, and superimposes the two-dimensional cross-sectional image on the display unit 120. Display. Therefore, the medical image processing apparatus 100 according to the first embodiment makes it possible to provide an image that allows the observer to grasp the blood flow state sensuously.

  Further, according to the first embodiment, the display control unit 154 increases the calculated value every time the parameter is calculated when the calculation unit 153 calculates the parameter for each predetermined position in the blood vessel. The corresponding coloring is sequentially performed, and the moving image superimposed on the two-dimensional cross-sectional image is displayed on the display unit 120. Therefore, the medical image processing apparatus 100 according to the first embodiment can display the calculated blood flow information in real time.

  Further, according to the first embodiment, the input unit 110 accepts an editing operation from the operator with respect to the core line extracted by the 2D image generation unit 152. Therefore, the medical image processing apparatus 100 according to the first embodiment can generate a CPR image and an SPR image along a finely adjusted core line, and can more accurately diagnose a lesion site. To do.

  Further, according to the first embodiment, the calculation unit 153 determines whether or not a lesion site needs to be treated by comparing the calculated blood flow information in the blood vessel with a preset condition. Then, the display control unit 154 causes the display unit 120 to display the determination result determined by the calculation unit 153. Therefore, the medical image processing apparatus 100 according to the first embodiment makes it possible to provide a diagnosis result.

  Further, according to the first embodiment, the calculation unit 153 determines whether it is necessary to treat a lesion site for each blood vessel. Then, the display control unit 154 causes the display unit 120 to sequentially display a two-dimensional cross-sectional image of a blood vessel that requires treatment of a lesion site based on the determination result determined by the calculation unit 153. Therefore, the medical image processing apparatus 100 according to the first embodiment can automatically display an image desired by a doctor.

(Second Embodiment)
Although the first embodiment has been described so far, the present invention may be implemented in various different forms other than the first embodiment described above.

  In the first embodiment described above, the case where a CPR image is generated as a two-dimensional cross-sectional image has been described. However, the embodiment is not limited to this. For example, an SPR image or a crosscut image may be generated. In such a case, the display control unit 154 can display the analysis result of the blood flow information superimposed on the SPR image or the crosscut image.

  In the first embodiment described above, the case where the image acquisition unit 151 acquires three-dimensional image data from the image storage device or the medical image diagnostic device has been described. However, the embodiment is not limited to this. For example, a doctor carries three-dimensional image data on a portable storage medium such as a flash memory or an external hard disk, and the image data storage unit of the medical image processing apparatus 100 is used. 141 may be stored. In such a case, acquisition of three-dimensional image data by the image acquisition unit 151 may not be executed.

  Further, in the first embodiment described above, the medical image processing apparatus 100 has been described. However, the embodiment is not limited to this, and for example, the storage unit 140 and the control of the medical image processing apparatus 100 illustrated in FIG. The unit 150 may be incorporated in the medical image diagnostic apparatus, and the blood flow state analysis described above may be executed by the medical image diagnostic apparatus.

  According to the medical image processing apparatus of at least one embodiment described above, intravascular blood flow information can be calculated easily and at high speed.

  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.

DESCRIPTION OF SYMBOLS 100 Medical image processing apparatus 110 Input part 150 Control part 151 Image acquisition part 152 2D image generation part 153 Calculation part 154 Display control part

Claims (10)

Generating means for generating, from the three-dimensional medical image data, a two-dimensional cross-sectional image of the tubular structure in which a feature of a lesion site in the tubular structure included in the three-dimensional medical image data is reflected;
Calculating means for calculating fluid information in the tubular structure by applying computational fluid dynamics to the two-dimensional image generated by the generating means;
A medical image processing apparatus comprising:   The medical image processing apparatus according to claim 1, wherein the generation unit extracts a core line of the tubular structure and generates a CPR image in which the tubular structure is developed along the extracted core line.   The medical device according to claim 2, wherein the generation unit extracts the lumen of the tubular structure, and generates the CPR image so that the stenosis rate of the extracted lumen is minimized or maximized. Image processing device.   The generation unit generates, as the two-dimensional cross-sectional image, a cross-cut image showing a cross-section in a short direction of the tubular structure and an SPR image obtained by extending the CPR image. The medical image processing apparatus according to 3. A display control unit for displaying the two-dimensional cross-sectional image generated by the generation unit on a predetermined display unit;
The calculation means calculates, as the fluid information, parameters including at least a fluid pressure, a flow velocity, and a shear stress for each predetermined position in the tubular structure,
The display control unit performs coloring according to a value for any one of the pressure, flow velocity, and shear stress of the fluid included in the parameter, and superimposes the predetermined display on the two-dimensional cross-sectional image. The medical image processing apparatus according to any one of claims 1 to 4, wherein the medical image processing apparatus is displayed by a unit.   When the parameter is calculated for each of the predetermined positions in the tubular structure by the calculation unit, the display control unit sequentially applies coloring according to the calculated value each time the parameter is calculated. 6. The medical image processing apparatus according to claim 5, wherein a moving image superimposed on the two-dimensional cross-sectional image is displayed on the predetermined display unit.   The medical image processing apparatus according to claim 2, further comprising an input unit that receives an editing operation from an operator with respect to the core line extracted by the generation unit. The calculation means determines whether or not the lesion site needs to be treated by comparing the calculated fluid information in the tubular structure with a preset condition,
The medical image processing apparatus according to claim 1, wherein the display control unit displays the determination result determined by the calculation unit on the predetermined display unit. The calculation means determines the necessity of treatment of the lesion site for each tubular structure,
The display control means causes the predetermined display unit to sequentially display the two-dimensional cross-sectional images of the tubular structure requiring treatment of the lesion site based on the determination result determined by the calculation means. The medical image processing apparatus according to claim 8.   The medical image processing apparatus according to claim 1, wherein the fluid information includes FFR.