Medical image processing apparatus, medical image processing method, and medical image processing program

Abstract

To provide a medical image processing device capable of improving accuracy of non-rigid body positioning of a plurality of pieces of medical image data.SOLUTION: A medical image processing device includes a processing unit. The processing unit acquires first medical image data and second medical image data composed of two-dimensional or three-dimensional pixels indicating a subject, executes non-rigid body positioning processing for executing non-rigid body positioning between the first medical image data and the second medical image data by deforming the second medical image data for the fixed first medical image data, and displays the first medical image data and the second medical image data subjected to the non-rigid body positioning in a display unit. The non-rigid body positioning processing includes processing for generating deformation information on the deformation of the second medical image data in a region at least including a reachable region of the second medical image data, and processing for deforming the second medical image data on the basis of the deformation information. The deformation information includes moving information on a movement of at least one pixel included in the second medical image data in a region that is not included in a space region of the first medical image data.SELECTED DRAWING: Figure 3A

Description

Technical Field

[0001] The present disclosure relates to a medical image processing apparatus, a medical image processing method, and a medical image processing program.

Background Art

[0002] Conventionally, non-rigid registration has been performed for the registration of two medical image data. For example, it is known to perform 3D PET-CT registration of the chest (see Non-Patent Document 1). Also, it is known to perform non-rigid registration of medical volume data acquired at different times and different types of volume data (see Non-Patent Document 2).

Prior Art Documents

Non-Patent Documents

[0003]

Non-Patent Document 1

Non-Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional non-rigid body alignment methods have insufficient accuracy in non-rigid body alignment of multiple medical image data, and there is room for improvement.

[0005] This disclosure is made in view of the above circumstances and provides a medical image processing apparatus, a medical image processing method, and a medical image processing program that can improve the accuracy of non-rigid alignment of multiple medical image data. [Means for solving the problem]

[0006] One aspect of the present disclosure is a medical image processing apparatus comprising a processing unit, the processing unit acquires a first medical image data and a second medical image data, each composed of two-dimensional or three-dimensional pixels representing a subject, and performs a non-rigid alignment process to non-rigidly align the first medical image data and the second medical image data by deforming the second medical image data relative to the fixed first medical image data, and displays the non-rigidally aligned first medical image data and the second medical image data on a display unit, wherein the spatial region of the second medical image data includes a portion not included in the spatial region of the first medical image data, and the non-rigid alignment process is performed Includes multiple nodes, The process includes generating deformation information relating to the deformation of the second medical image data in a region that includes at least an reachable region which is a region that the second medical image data can reach by deformation, and a process of deforming the second medical image data based on the deformation information. The reachable region is a region that includes at least the spatial region of the second medical image data and the spatial region of the first medical image data. The deformation information includes movement information relating to the movement of at least one pixel included in the second medical image data in a region not included in the spatial region of the first medical image data, in a medical image processing apparatus.

[0007] One aspect of the present disclosure includes the steps of: acquiring a first medical image data and a second medical image data, each consisting of two-dimensional or three-dimensional pixels representing a subject; performing non-rigid alignment between the first medical image data and the second medical image data by deforming the second medical image data relative to the fixed first medical image data; and displaying the non-rigid alignment of the first medical image data and the second medical image data on a display unit, wherein the spatial region of the second medical image data includes a portion not included in the spatial region of the first medical image data, and the step of performing the non-rigid alignment is, Includes multiple nodes, The method includes the steps of generating deformation information relating to the deformation of the second medical image data in a region that includes at least an reachable region which is a region that the second medical image data can reach by deformation, and deforming the second medical image data based on the deformation information. The reachable region is a region that includes at least the spatial region of the second medical image data and the spatial region of the first medical image data. The deformation information includes movement information relating to the movement of at least one pixel included in the second medical image data in a region not included in the spatial region of the first medical image data, and is a medical image processing method.

[0008] One aspect of this disclosure is a medical image processing program for causing a computer to perform the above-described medical image processing method. [Effects of the Invention]

[0009] According to this disclosure, the accuracy of non-rigid alignment of multiple medical image data can be improved. [Brief explanation of the drawing]

[0010] [Figure 1] Block diagram showing an example of the hardware configuration of a medical image processing device in the first embodiment. [Figure 2] Block diagram showing an example of the functional configuration of a medical image processing device. [Figure 3A] This figure shows an example of the shape of the deformed mesh and volume data before and after deformation of the fixed volume and the deformed volume. [Figure 3B]Figure showing the movement examples of each point before and after the deformation of the deformable volume [Figure 4] Figure showing the first example of the deformed mesh in the first embodiment [Figure 5] Figure showing the second example of the deformed mesh in the first embodiment [Figure 6] Figure showing an example of the convex hull region including the deformable volume and the fixed volume [Figure 7A] Figure showing an arterial phase image [Figure 7B] Figure showing a delayed phase image [Figure 8] Flowchart showing the first operation example during alignment of a medical image processing device [Figure 9A] Flowchart showing the second operation example during alignment of a medical image processing device [Figure 9B] Flowchart showing the second operation example during alignment of a medical image processing device [Figure 9C] Figure for explaining the second operation example during alignment of a medical image processing device [Figure 10A] Figure showing an example of the alignment result of a wide - range region including the abdomen of a subject in a comparative example [Figure 10B] Figure showing an example of the alignment result of the abdominal region of a subject in a comparative example [Figure 11A] Figure showing an example of the alignment result of a wide - range region including the abdomen of a subject in the first embodiment [Figure 11B] Figure showing an example of the alignment result of the abdominal region of a subject in the first embodiment [Figure 12] Figure showing an example of the setting of the deformed mesh in a comparative example

Embodiments for Carrying Out the Invention

[0011] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings.

[0012] (The Process Leading to an Embodiment of the Present Disclosure)

[0013] Non-patent documents 1 and 2 describe the use of deformable mesh MSX in non-rigid body alignment. Figure 12 is a diagram illustrating conventional non-rigid body alignment using deformable mesh MSX.

[0014] The deformed mesh MSX is generated based on one of several medical image data sets. This medical image data is the one being aligned, and since it is not deformed and its shape is fixed, it is also called fixed image data VFX. The size of the deformed mesh MSX is, in principle, the same as the size of the fixed image data VFX, and depending on the interpolation formula used, one or two rows of fixed points are added to the outer edge. The medical image data to be deformed is also called deformed image data VMX. Each pixel of the deformed image data VMX corresponds to a movement vector represented by interpolating each node NDX of the deformed mesh MSX. Therefore, the medical image processing device can deform the deformed mesh MSX by moving the pixels of the deformed image data VMX corresponding to the node NDX as the node NDX of the deformed mesh MSX moves.

[0015] Here, if the deformed image data VMX is larger than the fixed image data VFX in 3D space (virtual 3D space), the deformed image data VMX may not fit within the range of the fixed image data VFX, extending beyond the range of the fixed image data VFX, and there may be no corresponding node NDX of the deformed mesh MSX for the pixels of the deformed image data VMX. In this case, the area of ​​the deformed image data VMX where the corresponding node NDX of the deformed mesh MSX is absent cannot be deformed. Therefore, at least a portion of the deformed image data VMX may not be deformable, and in this case, the accuracy of non-rigid alignment of multiple medical image data will be insufficient.

[0016] It should be noted that, conventionally, the region containing both fixed image data (VFX) and deformed image data (VMX) was the main area of ​​interest, and there was likely little concern for the accuracy of non-overlapping regions. Furthermore, in PET-CT registration, which is the primary application of non-rigid body registration, it is likely that the area outside the imaging range of the CT data was not of interest (in the example in Non-Patent Document 1, both arms of the patient imaged by the PET scanner were not imaged by the CT scanner, but this was not considered a problem). Also, in interphase registration of 4D (four-dimensional) data, which is the primary application of non-rigid body registration, it is likely that this was not a problem because the regions of all volumes were identical (in the example in Non-Patent Document 2).

[0017] The following embodiments describe a medical image processing apparatus, a medical image processing method, and a medical image processing program that can improve the accuracy of non-rigid alignment of multiple medical image data.

[0018] (First Embodiment) Figure 1 is a block diagram showing an example configuration of a medical image processing device 100 in the first embodiment. The medical image processing device 100 includes a port 110, a UI 120, a display 130, a processor 140, and memory 150.

[0019] A CT scanner 200 is connected to the medical image processing device 100. The medical image processing device 100 acquires volume data from the CT scanner 200 and processes the acquired volume data. The medical image processing device 100 may also consist of a PC and software installed on the PC.

[0020] The CT scanner 200 irradiates the subject with X-rays and acquires an image (CT image) by utilizing the differences in X-ray absorption by the tissues within the body. The subject may include living organisms, humans, or animals. The CT scanner 200 acquires a synogram from the X-ray detector and generates a tomographic image (also called a slice image or slice data) of the subject by image reconstruction based on the synogram. Based on the slice data, the CT scanner 200 generates volume data, for example, by stacking the slice data. The slice data and volume data include information about any location inside the subject. The CT scanner 200 transmits the volume data as a CT image to the medical image processing device 100 via a wired or wireless line. When acquiring a CT image, imaging conditions related to CT imaging and contrast conditions related to the administration of contrast agents may be considered. Contrast enhancement may be performed on blood vessels, digestive organs, bile ducts, etc. Contrast enhancement may be performed multiple times at different timings depending on the characteristics of the organ.

[0021] Port 110 within the medical image processing device 100 includes a communication port, an external device connection port, or a connection port to an embedded device, and acquires volume data obtained from CT images. The acquired volume data may be immediately sent to the processor 140 for various processing, or it may be stored in the memory 150 and then sent to the processor 140 for various processing when needed. The volume data may also be acquired via a recording medium or recording media. Furthermore, the volume data may be acquired in the form of intermediate data, compressed data, synograms, or slice data. In addition, the volume data may be acquired from information from a sensor device attached to the medical image processing device 100. Port 110 functions as an acquisition unit that acquires various data such as volume data.

[0022] The UI120 may include a touch panel, a pointing device, a keyboard, or a microphone. The UI120 accepts arbitrary input operations from the user of the medical image processing device 100. The user may include a physician, a radiologist, a student, or other medical professional (paramedic staff).

[0023] UI120 accepts various operations. For example, it accepts operations such as specifying a region of interest (ROI) and setting brightness conditions in volume data and images based on volume data (e.g., 3D images and 2D images described later). The region of interest may include regions of various tissues (e.g., blood vessels, bronchi, organs, tissues, bones, brain). Tissues may include diseased tissue, normal tissue, or tumor tissue.

[0024] The display 130 may include, for example, an LCD, and displays various information. The various information may include three-dimensional images and two-dimensional images obtained from volume data. The three-dimensional images may include volume rendering images, surface rendering images, virtual endoscopic images, virtual ultrasound images, or CPR images, etc. The volume rendering images may include RaySum images, MIP images, MinIP images, average value images, or raycast images, etc. The two-dimensional images may include axial images, sagittal images, coronal images, or MPR images, etc.

[0025] Memory 150 includes primary storage devices such as various ROMs and RAMs. Memory 150 may also include secondary storage devices such as HDDs and SSDs. Memory 150 may also include tertiary storage devices such as USB memory or SD cards. Memory 150 stores various information and programs. The various information may include volume data acquired by port 110, images generated by processor 140, configuration information set by processor 140, and various programs. Memory 150 is an example of a non-transient recording medium on which programs are recorded.

[0026] The processor 140 may include a CPU, DSP, or GPU. The processor 140 functions as a processing unit 160 that performs various processing and control by executing a medical image processing program stored in memory 150.

[0027] Figure 2 is a block diagram showing an example of the functional configuration of the processing unit 160.

[0028] The processing unit 160 comprises a region processing unit 161, an image generation unit 162, a positioning processing unit 163, and a display control unit 165. The processing unit 160 coordinates all parts of the medical image processing device 100. For example, the processing unit 160 performs processing related to the positioning of multiple medical image data (e.g., volume data). Note that each part included in the processing unit 160 may be implemented as different functions by a single piece of hardware, or as different functions by multiple pieces of hardware. Furthermore, each part included in the processing unit 160 may be implemented by dedicated hardware components.

[0029] The region processing unit 161 acquires volume data of the subject, for example, via port 110. The region processing unit 161 extracts any region included in the volume data. The region processing unit 161 may automatically specify a region of interest and extract it, for example, based on the voxel values ​​of the volume data. The region processing unit 161 may manually specify a region of interest and extract it, for example, via UI 120.

[0030] The image generation unit 162 generates various images. Based on at least a portion of the acquired volume data (for example, volume data of an extracted region), the image generation unit 162 generates 3D images, 2D images, and tomographic images. The image generation unit 162 may generate images by performing various renderings (for example, volume rendering or surface rendering).

[0031] The alignment processing unit 163 aligns multiple medical image data. The medical image data may include, for example, volume data, slice data, and 3D or 2D images based on volume data or slice data. The volume data may also consist of multiple volume data created in a time series. Therefore, the alignment may include alignment in a 3D space (virtual 3D space) or alignment in a 2D plane (virtual 2D plane). The alignment here includes at least non-rigid registration and may further include rigid registration.

[0032] Rigid body alignment is a type of alignment in which the shape of a region such as an organ in medical image data does not change, and may include, for example, alignment by linear transformation using translation or rotation. Rigid body alignment is applied to bones, brains, etc. The alignment processing unit 163 may perform rigid body alignment by known methods.

[0033] Non-rigid body registration is a type of registration in which the shape of an organ region in medical image data changes. Non-rigid body registration may include, for example, registration by linear transformations using not only translation or rotation but also scaling or reduction, or registration by nonlinear transformations that are not linear. The registration processing unit 163 may, for example, represent nodes on a deformed mesh as finite elements and represent pixel movement by node movement. The registration processing unit 163 may also represent pixel movement by interpolating between nodes according to the B-spline method. The registration processing unit 163 may perform non-rigid body registration by performing registration by nonlinear transformation. The registration processing unit 163 may perform non-rigid body registration by large deformation simulation using the finite element method.

[0034] The image generation unit 162 may perform rendering and generate an image based on the volume data, which is a plurality of medical image data, that has been aligned by the alignment processing unit 163. The alignment processing unit 163 may also perform alignment, including rigid body alignment and non-rigid body alignment, on the multiple medical image data generated by the image generation unit 162.

[0035] The display control unit 165 displays various data, information, or images on the display 130. The images are images representing a part of the tissue within the subject, and may include, for example, images generated by the image generation unit 162, or tomographic images of a predetermined cross-section. The display control unit 165 displays, for example, an image based on a plurality of medical image data that have been aligned (e.g., non-rigid alignment).

[0036] Next, we will describe the process related to alignment in detail. This embodiment primarily illustrates that medical image data is volume data. It also primarily illustrates the alignment of volume data in three-dimensional space.

[0037] The alignment processing unit 163 takes measures to address mismatches that occur in non-rigid alignment when the moving volume is larger than the fixed volume. The moving volume VM is volume data whose shape can be deformed. The fixed volume VF is volume data whose shape does not deform (is fixed). The alignment processing unit 163 may manually or automatically set which volume data will be the moving volume VM and which will be the fixed volume VF. For example, it may determine which volume data will be the moving volume VM and which will be the fixed volume VF based on user operation via the UI 120. For example, it may determine which volume data will be the moving volume VM and which will be the fixed volume VF based on the characteristics of organs, tissues, etc., included in the volume data. For example, organs and blood vessels may be designated as the moving volume VM, and bones as the fixed volume VF.

[0038] In non-rigid alignment, the alignment processing unit 163 generates and sets a deformed mesh MS corresponding to the region in three-dimensional space that includes the fixed volume VF and the deformed volume VM. Even if there is some misalignment between the volume data, the alignment processing unit 163 can suppress the misalignment by deforming the deformed volume VM relative to the fixed volume VF and performing non-rigid alignment. Therefore, the displayed three-dimensional and two-dimensional images will be images in which the misalignment between the volume data is suppressed, resulting in a natural appearance for the user and increasing the options for treatment and diagnosis.

[0039] Next, we will explain non-rigid alignment using a deformable mesh MS.

[0040] Figure 3A shows an example of the shape of the deformed mesh MS and volume data before and after deformation of the fixed volume VF and the deformed volume VM. In the figure, the fixed volume VF is shown as "fixed" and the deformed volume VM is shown as "moving". Figure 3A also visualizes how the volume data is rendered and displayed as an image on a two-dimensional plane.

[0041] The deformable volume VM undergoes non-rigid deformation during non-rigid alignment. This non-rigid deformation is represented as deformation of the deformation mesh MS. The deformation mesh MS is positioned corresponding to the coordinates defined by the fixed volume VF. The deformation mesh MS contains multiple nodes ND. The deformation mesh MS is formed by arranging multiple nodes ND in the three-dimensional space where non-rigid alignment takes place. The multiple nodes ND are arranged, for example, in a grid pattern in three-dimensional space, but they may be arranged in other shapes.

[0042] The alignment processing unit 163 generates deformation information regarding the deformation of the deformable volume VM, which is deformed using the deformable mesh MS. The deformation information includes movement information regarding the movement of each voxel in the deformable volume VM corresponding to each node ND of the deformable mesh MS. In other words, the deformation information includes movement information indicating which voxel in the deformable volume VM before deformation (current state) moves to which voxel in the fixed volume (corresponds to). That is, the deformation information indicates the correspondence between each point in the deformable volume VM before and after deformation. The deformation information may also include information on the displacement of each voxel in the deformable volume VM from its initial state, with reference to the coordinates of the fixed volume VF.

[0043] The alignment processing unit 163 generates the deformed mesh MS by setting each node of the deformed mesh MS to correspond to the coordinates of the fixed-side volume VF, and generates deformation information. In Figure 3A, the deformed mesh MS is set so that each node ND forms a grid with respect to the fixed-side volume VF. Note that the fixed-side volume VF does not deform, so in Figure 3A, as an example, its shape remains triangular before and after deformation. On the other hand, the deformed-side volume VM has a different shape from the fixed-side volume VF before deformation, and in Figure 3A, it exhibits a distorted triangular shape. Therefore, the deformed mesh MS before deformation exhibits a distorted shape when compared to the reference shape (shape based on the coordinates of the fixed-side volume VF).

[0044] The alignment processing unit 163 moves each node ND of the deformed mesh MS based on the deformation information, thereby moving the corresponding voxels of the deformed volume VM, deforming the deformed volume VM to conform to the shape of the fixed volume VF, and performing non-rigid alignment. Thus, the alignment processing unit 163 obtains the deformed volume VM after deformation (deformed volume data VMA) from the deformed volume VM before deformation (pre-deformation volume data VMB) based on the deformation information. As a result, as shown in Figure 3A, the deformed volume VM is deformed into a triangular shape similar to the fixed volume VF.

[0045] Furthermore, it is not necessary to provide nodes in the deformed mesh MS that correspond to every voxel in the fixed-side volume VF. In this case, the deformation information may include voxels in the deformed-side volume VM for which no movement information exists. In this case, the alignment processing unit 163 may calculate the movement information for voxels for which movement information was not obtained by interpolation (for example, linear interpolation) based on the movement information of each voxel in the deformed-side volume VM corresponding to each node ND of the deformed mesh MS.

[0046] For example, the grid of the deformable mesh MS may be in units of 64 voxels (one node per 64 voxels). Also, the density of each node ND in the deformable mesh MS does not have to be uniform and may vary depending on the position (region) within the deformable mesh MS. For example, in regions of the deformable volume that can be deformed finely or regions that are to be deformed finely, the density of nodes ND in the deformable mesh MS may be increased to obtain more deformation information (information on the movement of voxels within the region) in these regions. For example, in regions of the deformable volume that are to be moved a large amount, the density of nodes ND in the deformable mesh MS may be decreased to stabilize the deformation information (information on the movement of voxels within the region) in these regions. In this way, the alignment processing unit 163 deforms the deformable volume VM based on the movement information of voxels in the deformable volume VM corresponding to the coordinate points defined in the fixed volume VF.

[0047] Figure 3B shows an example of the movement of each point in the deformed volume VM before and after deformation. In Figure 3B, as indicated by each arrow, the movement of each node ND (vertex) on the deformed mesh MS is represented by a vector. The movement information of each voxel in the deformed volume VM corresponding to each node ND of the deformed mesh MS is included in the deformation information. In Figure 3B, it can be seen that voxel VXB, which corresponds to node NDB before deformation, has moved to voxel VXA, which corresponds to node NDA after deformation.

[0048] Figure 4 shows a first example of the deformable mesh MS in this embodiment.

[0049] The alignment processing unit 163 generates a deformation mesh MS and generates deformation information, including at least the region of the deformed volume VM. Specifically, it generates a deformation mesh MS that includes an reachable region VMR, which is the region that the deformed volume VM can reach through deformation. The reachable region VMR may include, for example, the pre-deformation volume data VMB and the post-deformation volume data VMA, and may also include the region of the deformed volume VM during deformation. For example, when the deformed volume VM (pre-deformation volume data VMB) is deformed to match the fixed volume VF, the reachable region VMR is the region that includes at least the fixed volume VF and the deformed volume VM in Figure 4.

[0050] Furthermore, the alignment processing unit 163 may generate a deformed mesh MS that includes the regions of both the fixed-side volume VF and the reachable region VMR of the deformed-side volume VM. Information on the region of the fixed-side volume VF and the reachable region VMR of the deformed-side volume VM may be stored in the memory 150 in advance, or it may be acquired from an external device via the port 110. The deformation information also includes movement information relating to the movement of at least one pixel (voxel) included in the deformed-side volume VM in the region not included in the region of the fixed-side volume VF.

[0051] As a result, the node ND of the deformed mesh MS corresponds to one of the voxels in the pre-deformed volume data VMB, and further, it also corresponds to one of the voxels in the post-deformed volume data VMA. Therefore, the end of the deformed volume VM is never located outside the deformed mesh MS. Thus, the medical image processing device 100 can suitably deform the volume VM all the way to its end, and the possibility of it becoming impossible to deform can be suppressed.

[0052] Figure 5 shows a second example of the deformed mesh MS in this embodiment.

[0053] In the example in Figure 5, the deformation mesh MS is smaller than in Figure 4. Specifically, in areas where the deformed volume VM cannot move, i.e., outside the reachable area VMR, there are no nodes ND in the deformation mesh MS. Therefore, the alignment processing unit 163 generates a deformation mesh of the minimum size necessary for the deformation of the deformed volume VM. Thus, if nodes ND are arranged at the same density (fineness) in the deformation mesh MS in Figure 4 and Figure 5, the number of nodes ND in Figure 5 will be less than in Figure 4. As a result, the computational amount required to derive movement information for each voxel of the deformed volume VM corresponding to each node ND in the deformation mesh MS is reduced, the computational amount required to derive deformation information is reduced, and the computational amount required for the deformation of the deformed volume VM is reduced. In this way, the medical image processing device 100 can reduce the deformation mesh MS and reduce the computational amount required for non-rigid alignment using the deformation mesh MS.

[0054] In Figure 5, the deformed mesh MS may be one grid unit larger than the reachable area VMR, meaning the deformed mesh MS may be one node ND unit larger. The reason the deformed mesh MS is set slightly larger than the reachable area VMR is to serve as a boundary for spline interpolation (e.g., B-spline interpolation) and to enable smooth spline interpolation. In Figure 4 as well, the deformed mesh MS may be at least one grid unit larger than the reachable area VMR. Additionally, immovable fixed points may be placed outside the reachable area VMR. These fixed points can be used to enable smooth spline interpolation.

[0055] Furthermore, while the alignment processing unit 163 reduces the number of nodes ND by shrinking the deformed mesh MS, it may also set immovable fixed points in place of the reduced nodes ND. This allows the medical image processing device 100 to reduce the computational load related to non-rigid alignment. The alignment processing unit 163 may also interpolate the movement of each voxel in the deformed volume VM between the fixed points and the nodes ND. Note that the fixed points and the movement of each voxel obtained by interpolation are not included in the deformed mesh MS.

[0056] Figure 6 shows an example of a convex hull region VC that includes the deformable volume VM and the fixed volume VF. The convex hull region VC is an example of an inclusion region that includes the deformable volume VM and the fixed volume VF.

[0057] If the alignment processing unit 163 omits the deformation mesh MS outside the reachable region VMR as shown in Figure 5, it may generate a convex hull region VC that includes the deformation-side volume VM and the fixed-side volume VF (the sum of the two volumes). The alignment processing unit 163 may generate a deformation mesh MS corresponding to the size of the convex hull region VC. In this case, the deformation mesh MS may be made one cell larger than the convex hull region VC, as described above, and interpolation may be applied. Then, corresponding to this convex hull region VC, each voxel of the deformation-side volume VM is deformed based on the deformation information and non-rigidly aligned to each voxel of the fixed-side volume VF. In Figure 6, since alignment is performed from the deformation-side volume VM to the fixed-side volume VF, the convex hull region VC encompasses the reachable region VMR.

[0058] Furthermore, when considering a grid-like deformation mesh MS that includes the deformation-side volume VM and the fixed-side volume VF, a circumscribed rectangular parallelepiped region that circumscribes both the deformation-side volume VM and the fixed-side volume VF may be generated. The circumscribed rectangular parallelepiped region is an example of an inclusion region. Then, the deformation mesh MS may be generated corresponding to the size of this circumscribed rectangular parallelepiped region. Since the deformation-side volume VM is aligned with the fixed-side volume VF, this circumscribed rectangular parallelepiped region encloses the reachable region VMR.

[0059] Next, we will explain specific examples of the deformable volume VM and the fixed volume VF. Note that the two-dimensional data corresponding to the deformable volume VM and the fixed volume VF will be described as the deformable image and the fixed image, respectively.

[0060] Here, it is assumed that, for example, 3D data (e.g., volume data) and 2D data can be acquired via port 110 by various modality devices, not limited to the CT scanner 200. These various modality devices include, for example, MRI (Magnetic Resonance Imaging) scanners, PET (Positron Emission Tomography) scanners, angiography scanners, or other modality devices. Furthermore, it is assumed that the size of the deformable volume VM in 3D space is larger than the size of the fixed volume VF in 3D space.

[0061] The first specific example is as follows: The fixed-side volume VF is obtained during the arterial phase (e.g., early arterial phase) because the imaging timing (time) is short, resulting in imaging over a narrow area. The deformable-side volume VM is obtained during the delayed phase because the imaging time is longer than in the arterial phase, resulting in imaging over a wider area than in the arterial phase. Here, since the arterial phase shows finer blood vessels and is mainly used in diagnosis, users want to observe the images of the arterial phase as they are acquired. In other words, the deformable-side volume VM is larger in three-dimensional space than the fixed-side volume VF.

[0062] Figure 7A shows the arterial phase image G31. Figure 7B shows the delayed phase image G32. The arterial phase image G31 has higher resolution than image G32 and is based on a CT image acquired by the CT scanner 200 over a narrower imaging range than image G32. The delayed phase image G32 has lower resolution than image G31 and is based on a CT image acquired by the CT scanner 200 over a wider imaging range than image G31. In this case, since the arterial phase image G31 serves as the diagnostic criterion, it is preferable not to deform it. Therefore, the size of the image (data) in three-dimensional space is larger for image G32 than for image G31. When the alignment processing unit 163 performs non-rigid alignment of the arterial phase image G31 and the delayed phase image G32, in the first specific example, the arterial phase image G31 is used as the fixed side volume VF, and the delayed phase image G32 is used as the deformed side volume VM.

[0063] A second specific example is as follows: The fixed-side volume VF is a CT image acquired by the CT scanner 200. The deformable-side volume VM is volume data as an MRI image acquired by the MRI scanner. Since the MRI scanner may acquire images at an angle to the axial plane, the MRI image may extend beyond the 3D spatial region corresponding to the CT image. Here, since the CT image has a higher resolution and is mainly used, the user wants to observe the CT image as it was acquired. In this case, the deformable-side volume VM is larger in size in 3D space than the fixed-side volume VF. Also, MRI images are often obtained distorted due to the magnetic field. Even in such cases, the medical image processing device 100 can smoothly connect and render the fixed-side volume VF and the deformable-side volume VM by non-rigid alignment as in this embodiment, and obtain a highly reliable image.

[0064] The third specific example is as follows: The fixed side image is a single-section 2D video or a multi-section 2D video obtained by an MRI device. The deformed side volume VM is a CT image obtained by a CT device 200. For example, the single-section video or multi-section 2D video is an image for diagnosing cardiac function. The CT image is an auxiliary image for measuring positional relationships and ventricular volume. Therefore, the user wants to observe the single-section 2D video or multi-section 2D video as it was acquired. In this case, the alignment processing unit 163 uses the phase of the multi-section 2D video corresponding to the cardiac phase used to acquire the CT image as the fixed side image by electrocardiogram synchronization.

[0065] The fourth specific example is as follows: The fixed-side volume VF is a CT image acquired by CT scanner 200 with a narrowed FOV (Field of View). FOV corresponds to the imaging range on a slice (on the same cross-section). The deformed-side volume VM is volume data as an XA image acquired by an XA scanner (angiography X-ray diagnostic device, C-arm machine) with a wider imaging range than the fixed-side volume VF mentioned above. The XA scanner is small and lightweight and can be used during surgery. In addition, the XA scanner can acquire 2DT images by fixing the arm of the XA scanner and acquiring video. Furthermore, the XA scanner can acquire 4D (four-dimensional) data by rotating the arm and acquiring video. CT images acquired before surgery have higher resolution, and surgical planning is done using CT images. Therefore, users want to observe the CT images as they were acquired.

[0066] The fifth specific example is as follows: The fixed-side image is two-dimensional data (two-dimensional image) as an X-ray image acquired by a simple X-ray device. Alternatively, the fixed-side image may be an MIP image generated based on a CT image acquired by CT device 200. The deformed-side image is a 2D video acquired by an XA device. The X-ray images acquired before surgery have higher resolution, and surgical planning is done using the X-ray images. Therefore, the user wants to observe the X-ray images as they were acquired.

[0067] The sixth specific example is as follows: The fixed-side volume VF is 4D (four-dimensional) data of the heart obtained in electrocardiogram synchronization, and is data acquired within a narrow imaging range. The 4D data is data in which multiple 3D volume data and slice data are arranged in a time series, in other words, it is a 3D video. The deformable-side volume VM is chest data including the heart, and is 3D data with a wider imaging range than the fixed-side volume VF mentioned above. The 4D data of the heart is data for diagnosing cardiac function. The chest data is an auxiliary image for confirming the catheter route, etc. Therefore, the user wants to observe the 4D data as it was acquired.

[0068] Next, an example of the operation of the medical image processing device 100 will be described.

[0069] There are two types of operation examples for alignment using the medical image processing device 100: the first operation example and the second operation example.

[0070] In the first operational example, the alignment processing unit 163 generates a deformation mesh MS (large deformation mesh MS) corresponding to the inclusion region that includes the deformation-side volume VM and the fixed-side volume VF, and uses this deformation mesh MS to align the deformation-side volume VM and the fixed-side volume VF.

[0071] In the second operation example, the alignment processing unit 163 generates a first deformation mesh MS1 corresponding to the fixed-side volume VF, as in the conventional method, and uses the first deformation mesh MS1 to align the deformation-side volume VM and the fixed-side volume VF. Subsequently, the alignment processing unit 163 generates a second deformation mesh MS2 corresponding to at least a portion of the deformation-side volume VM that was not covered by the first deformation mesh MS1, and uses the second deformation mesh MS2 to align the deformation-side volume VM and the fixed-side volume VF. Then, the image generation unit 162 concatenates the image aligned with the first deformation mesh MS1 and the image aligned with the second deformation mesh MS2 to generate an image in which the entire image is aligned.

[0072] Figure 8 is a flowchart showing a first example of the operation of the medical image processing device 100 during alignment.

[0073] First, port 110 acquires volume data of a subject (e.g., a patient) with a small imaging range during the early arterial phase (arterial phase) as the fixed-side volume VF (S11). Also, during the venous phase (delayed phase), port 110 acquires volume data of a subject with a large imaging range as the deformation-side volume VM before deformation (i.e., pre-deformation volume data VMB) (S11).

[0074] The alignment processing unit 163 performs rigid alignment of the acquired fixed volume VF and the pre-deformation volume data VMB (S12). The alignment processing unit 163 generates a deformation mesh MS corresponding to the inclusion region encompassing the fixed volume VF and the pre-deformation volume data VMB (S13). The alignment processing unit 163 performs non-rigid alignment of the fixed volume VF and the pre-deformation volume data VMB and records movement information in the deformation mesh MS regarding the movement of each voxel from the pre-deformation volume data VMB to the post-deformation volume data VMA corresponding to each node ND of the deformation mesh MS (S14A). Then, the alignment processing unit 163 generates the post-deformation volume data VMA from the pre-deformation volume data VMB using the movement information recorded in the deformation mesh MS (S14B). Furthermore, in the deformation mesh MS, for deformation of a region that is not included in either the fixed volume VF or the pre-deformation volume data VMB, or is included in only one of them, the alignment processing unit 163 may set a node ND that is similar to a sponge-like tissue such as the lung, and make it follow the deformation of node ND included in the pre-deformation volume data VMB. Furthermore, in the deformation mesh MS, for deformation of a region that is not included in either the fixed volume VF or the pre-deformation volume data VMB, or is included in only one of them, the alignment processing unit 163 may set a finite element node with a Poisson's ratio of 0 and slight elasticity, and make it follow the deformation of node ND included in the pre-deformation volume data VMB.

[0075] The display control unit 165 displays an image on the display 130 based on the aligned fixed volume VF and the deformed volume data VMA (for example, by superimposing them) (S15). The display control unit 165 may also blend the fixed volume data and the deformed volume data at a predetermined blend ratio (mixing ratio) and display an image based on the blended data. This makes the boundary between the fixed volume and the deformed volume data less noticeable, further suppressing discontinuity.

[0076] As a result, the medical image processing device 100 can perform non-rigid alignment using a deformation mesh MS that corresponds to the size of the convex hull region VC encompassing both the fixed-side volume VF and the deformable-side volume VM. Therefore, the medical image processing device 100 can accommodate the deformation of the deformable-side volume VM, taking into account the reachable region VMR, with a single non-rigid alignment, and can perform suitable non-rigid alignment.

[0077] Figures 9A and 9B are flowcharts illustrating a second operation example during alignment of the medical image processing device 100. In Figures 9A and 9B, the explanation of the same process as in Figure 8 is omitted or simplified. Figure 9C is a diagram illustrating the second operation example.

[0078] First, port 110 acquires volume data of a subject with a small imaging range obtained by imaging with the CT scanner 200 as the fixed-side volume VF (S21). Also, port 110 acquires volume data of a subject with a large imaging range obtained by imaging with the PET scanner as the pre-deformation volume data VMB (S21).

[0079] The alignment processing unit 163 performs rigid alignment of the acquired fixed volume VF and the pre-deformation volume data VMB (S22). The alignment processing unit 163 generates a first deformation mesh MS1 that encompasses the fixed volume VF (S23). The alignment processing unit 163 performs non-rigid alignment of the fixed volume VF and the pre-deformation volume data VMB and records movement information in the first deformation mesh MS1 regarding the movement of each voxel from the pre-deformation volume data VMB to the post-deformation volume data VMA corresponding to each node ND of the first deformation mesh MS1 (S24). This movement information also includes position information of the pre-deformation volume data relative to the fixed volume VF (information of the initial position before deformation).

[0080] Furthermore, since the first deformable mesh MS1 is a conventional deformable mesh MSX, it may not be able to fully cover the reachable region VMR of the deformable volume VM, and the reachable region VMR may be located outside the region of the first deformable mesh MS1. Therefore, at steps S24A and S24B, the non-rigid alignment is insufficient.

[0081] Proceed to Figure 9B. The alignment processing unit 163 calculates the difference region VS by subtracting the spatial region of the fixed-side volume VF from the region of the pre-deformation volume data VMB (the region in three-dimensional space where the pre-deformation volume data VMB exists, also called the spatial region). Then, it generates a second deformation mesh MS2 that corresponds to (and encompasses) the difference region VS (S25). As a non-rigid alignment, the alignment processing unit 163 uses the first deformation mesh MS1 as a reference and records movement information in the second deformation mesh MS2 regarding the movement of each voxel from the pre-deformation volume data VMB to the deformed volume data VMA corresponding to each node ND of the second deformation mesh MS2, so that the second deformation mesh MS2 is smoothly connected to the first deformation mesh MS1 (S26). For example, the alignment processing unit 163 uses the movement of node ND of the first deformable mesh MS1 at the boundary portion BP of the first deformable mesh MS2 as a boundary condition, moves node ND of the second deformable mesh MS2 at the boundary portion BP, and sets node ND in other parts of the second deformable mesh MS2 that are similar to sponge-like tissue such as lungs, thereby deforming the second deformable mesh MS2. This results in a smooth connection between the first deformable mesh MS1 and the second deformable mesh MS2. Alternatively, the movement of node ND of the first deformable mesh MS1 at the boundary portion BP of the first deformable mesh MS2 as a boundary condition, moves node ND of the second deformable mesh MS2 at the boundary portion BP, and sets finite element nodes with a Poisson's ratio of 0 and slight elasticity in other parts of the second deformable mesh MS2, thereby deforming the second deformable mesh MS2. This results in a smooth connection between the first deformable mesh MS1 and the second deformable mesh MS2. This allows us to obtain output results equivalent to the first example while suppressing the computational complexity.

[0082] Therefore, the deformation information using the first deformation mesh MS1 corresponds to the deformation information regarding the deformation of the deformation-side volume VM in the spatial domain of the fixed-side volume VF. Similarly, the deformation information using the second deformation mesh MS2 corresponds to the deformation information regarding the deformation of the deformation-side volume VM in the spatial domain of the difference domain VS.

[0083] The alignment processing unit 163 connects the first deformation mesh MS1 and the second deformation mesh MS2 to create a third deformation mesh MS3, thereby performing non-rigid alignment of the combined spatial region of the pre-deformation volume data VMB and the fixed-side volume VF (S27A). In other words, the alignment processing unit 163 performs non-rigid alignment of the fixed-side volume VF and the pre-deformation volume data VMB in the combined region, and uses the first deformation mesh MS1 and the second deformation mesh MS2 to record movement information in the third deformation mesh MS3 regarding the movement of each voxel from the pre-deformation volume data VMB to the post-deformation volume data VMA corresponding to each node ND of the third deformation mesh MS3. The alignment processing unit 163 uses the movement information recorded in the third deformation mesh MS3 to generate the post-deformation volume data VMA from the pre-deformation volume data VMB (S27B).

[0084] The display control unit 165 displays an image based on the fixed volume VF and the deformed volume data VMA on the display 130 (for example, by superimposing them (S28)).

[0085] As a result, the medical image processing device 100 can perform non-rigid body alignment using a first deformation mesh MS1 based on the size of the fixed-side volume VF, and then perform non-rigid body alignment on a portion of the deformed-side volume VM that could not be covered by the first deformation mesh MS1 using a second deformation mesh MS2. Therefore, the medical image processing device 100 can compensate for the shortfall with the same amount of computation as conventional non-rigid body alignment.

[0086] (Comparison of images obtained in the comparative example and this embodiment) Figure 10A shows an example of the alignment results for a wide area including the abdomen of the subject in the comparative example. Figure 10B shows an example of the alignment results for the abdominal area of ​​the subject in the comparative example. In Figure 10A, the body surface of the subject is not depicted. The images shown in Figures 10A and 10B are images obtained when non-rigid alignment is performed using, for example, the conventional method of Non-Patent Document 1 or Non-Patent Document 2.

[0087] In Figure 10A, a misalignment between volume data occurs in the pelvis, which appears as a discontinuity line L11 crossing the pelvis in image G11, based on volume data that was non-rigid-aligned in the comparative example. In Figure 10B, a misalignment between volume data occurs in the abdomen, which appears as a discontinuity line L12 crossing the abdomen in image G12, based on volume data that was non-rigid-aligned in the comparative example.

[0088] Figure 11A shows an example of the alignment result of a wide area including the abdomen of the subject in this embodiment. Figure 11B shows an example of the alignment result of the abdominal area of ​​the subject in this embodiment. In Figure 11A, the body surface of the subject is not drawn. The area of ​​the subject shown in Figure 11A is the same area as the area of ​​the subject shown in Figure 10A. The area of ​​the subject shown in Figure 11B is the same area as the area of ​​the subject shown in Figure 10B.

[0089] In Figures 11A and 11B, the non-rigid alignment described above is performed, or rigid alignment may be performed. In Figure 11A, the misalignment between volume data is suppressed, and the misalignment shown in Figure 10A is eliminated. Therefore, in image G21 based on volume data that has been non-rigidly aligned in this embodiment, the discontinuity line L11 shown in Figure 10A does not exist. In Figure 11B, the misalignment between volume data is suppressed, and the misalignment shown in Figure 10B is eliminated. Therefore, in image G22 based on volume data that has been non-rigidly aligned in this embodiment, the discontinuity line L12 shown in Figure 10B does not exist.

[0090] Next, variations of this embodiment will be described.

[0091] The alignment processing unit 163 may, in the deformation of the deformation-side volume VM, directly deform the pre-deformation volume data VMB to derive the post-deformation volume data VMA. In this case, the pre-deformation volume data VMB may not be retained in memory 150, and only the post-deformation volume data VMA may be retained in memory 150. Alternatively, the alignment processing unit 163 may create a new post-deformation volume data VMA based on the pre-deformation volume data VMB. In this case, both the pre-deformation volume data VMB and the new post-deformation volume data VMA may be retained in memory 150. Furthermore, the alignment processing unit 163 may, without immediately creating the post-deformation volume data VMA, refer to the voxel values ​​of the pre-deformation volume data VMB when calculating the voxel values ​​of the post-deformation volume data VMA.

[0092] The alignment processing unit 163 may perform non-rigid alignment on at least a portion (region of interest) of the volume data. In other words, it may perform non-rigid alignment on a portion of the fixed volume VF and a portion of the deformable volume VM. For example, if it is sufficient to align only the left ventricle in a chest image (the chest region in the volume data), it may perform non-rigid alignment on the chest region of the fixed volume VF and the chest region of the deformable volume VM. In this case, the alignment processing unit 163 may deform the area outside the region of interest using the method of the second example of operation.

[0093] Furthermore, multiple deformable mesh MSs may be prepared and stored in memory 150. Multiple types of grid fineness (multi-resolution) of deformable mesh MSs may be prepared. Also, the size of each type of deformable mesh MS may differ, and the area covered by each deformable mesh MS may differ. In addition, if the medical image data is 3D data, the deformable mesh MS may be constructed in 3D, and if the medical image data is 2D data, it may be constructed in 3D.

[0094] Furthermore, the deformable mesh MS may be composed of either three-dimensional or two-dimensional elements when the medical image data is a combination of three-dimensional and two-dimensional data. Also, in the case of a combination of a fixed-side two-dimensional image and a deformable-side volume VM, the alignment processing unit 163 may deform the deformable-side volume VM by first performing non-rigid alignment on the cross-section of the deformable-side volume VM of the fixed-side two-dimensional image, and then extrapolating the same deformation in the depth direction of that cross-section. Also, in the case of a combination of a fixed-side two-dimensional image of multiple cross-sections and a deformable-side volume VM, the alignment processing unit 163 may deform the deformable-side volume VM by first performing non-rigid alignment on the corresponding cross-section of the deformable-side volume VM for each cross-section of the fixed-side multiple-cross-section two-dimensional image, and then extrapolating the deformation between the multiple cross-sections in the depth direction of that cross-section.

[0095] Furthermore, the alignment processing unit 163 may repeatedly perform non-rigid alignment. For example, non-rigid alignment may be performed using a deformable mesh MS with a coarse grid, followed by non-rigid alignment using a deformable mesh MS with a finer grid. Alternatively, the deformable mesh MS may be available in multiple stages of fineness, and the alignment processing unit 163 may start with non-rigid alignment using a deformable mesh MS with a coarse grid and then sequentially perform non-rigid alignment using progressively finer deformable mesh MS.

[0096] Thus, the medical image processing device 100 of this embodiment generates a deformation mesh MS in a range that includes the reachable region VMR that the deformation-side volume VM can reach through deformation, so there is no region in which the deformation-side volume VM to be deformed cannot be deformed. Therefore, the deformation-side volume VM can be suitably deformed and non-rigid alignment can be performed with respect to the fixed-side volume VF, thereby improving the accuracy of non-rigid alignment.

[0097] Although various embodiments have been described above with reference to the drawings, it goes without saying that this disclosure is not limited to such examples. It is clear to those skilled in the art that various modifications or alterations can be conceived within the scope of the claims, and these will naturally also fall within the technical scope of this disclosure.

[0098] Furthermore, the medical image processing device 100 may include at least a processor 140 and memory 150. The port 110, UI 120, and display 130 may be external to the medical image processing device 100.

[0099] Furthermore, it was illustrated that the volume data, as captured CT images, is transmitted from the CT scanner 200 to the medical image processing device 100. Alternatively, the volume data may be transmitted to a server on the network (e.g., an image data server (PACS) (not shown)) for storage. In this case, the port 110 of the medical image processing device 100 may acquire the volume data from the server via a wired or wireless line when necessary, or via any storage medium (not shown).

[0100] Furthermore, it was illustrated that the volume data of the captured CT image is transmitted from the CT scanner 200 to the medical image processing device 100 via port 110. This includes cases where the CT scanner 200 and the medical image processing device 100 are essentially combined into a single product. It also includes cases where the medical image processing device 100 is treated as the console for the CT scanner 200.

[0101] Furthermore, while the example illustrates the acquisition of images using the CT scanner 200 and the generation of volume data containing information about the inside of the subject, images may be acquired and volume data generated using other devices. These other devices include various modality devices. Additionally, the PET scanner may be used in combination with other modality devices.

[0102] Furthermore, the operation of the medical image processing device 100 can be expressed as a defined medical image processing method. It can also be expressed as a program that causes a computer to execute each step of the medical image processing method.

[0103] (Summary of the above embodiment) As described above, the medical image processing apparatus 100 of the above embodiment includes a processing unit 160. The processing unit 160 acquires a first medical image data (e.g., fixed-side volume VF) and a second medical image data (e.g., deformable-side volume VM) composed of two-dimensional or three-dimensional pixels representing a subject. The processing unit 160 performs a non-rigid alignment process to non-rigidly align the first medical image data and the second medical image data by deforming the second medical image data relative to the fixed first medical image data. The processing unit 160 displays the non-rigidally aligned first medical image data and the second medical image data on a display 130 (an example of a display unit). The spatial region of the second medical image data includes portions not included in the spatial region of the first medical image data. The non-rigid alignment process includes generating deformation information relating to the deformation of the second medical image data in a region that includes at least an reachable region VMR, which is a region that the second medical image data can reach through deformation, and deforming the second medical image data based on the deformation information. The deformation information includes movement information relating to the movement of at least one pixel included in the second medical image data in a region not included in the spatial region of the first medical image data.

[0104] As a result, the medical image processing device 100 deforms the deformable volume VM based on deformation information regarding the deformation of the deformable volume VM within the range including the reachable region VMR. Therefore, there is no region where the deformable volume VM to be deformed cannot be deformed. Thus, the medical image processing device 100 can suitably deform the deformable volume VM and perform non-rigid alignment with respect to the fixed volume VF, thereby improving the accuracy of non-rigid alignment.

[0105] Furthermore, the deformation information may include deformation information relating to the deformation of the second medical image data in the convex hull region VC of the sum region of the spatial region of the first medical image data and the spatial region of the second medical image data.

[0106] As a result, the medical image processing device 100 can obtain deformation information regarding the deformation of the entire convex hull region VC in a single step, and deform the deformation-side volume VM across the entire convex hull region VC, enabling non-rigid alignment. Therefore, the time required for non-rigid alignment can be reduced.

[0107] Furthermore, the deformation information may include first deformation information relating to the deformation of the second medical image data in the spatial domain of the first medical image data, and second deformation information relating to the deformation of the second medical image data in the difference domain VS obtained by subtracting the spatial domain of the first medical image data from the spatial domain of the second medical image data before deformation.

[0108] As a result, the medical image processing device 100 first deforms the deformable volume VM in the region covering the fixed volume VF, as in the conventional method, and then deforms the deformable volume VM in the remaining region to be deformed. Therefore, the medical image processing device 100 can perform deformation and non-rigid alignment in areas that were insufficient with the conventional method, after performing non-rigid alignment using the conventional method.

[0109] Furthermore, the first medical image data and the second medical image data may be at least a portion of the volume data acquired by the CT scanner. This allows the medical image processing device 100 to improve the accuracy of non-rigid alignment even when non-rigid alignment is performed on CT images acquired with the same modality device.

[0110] Furthermore, the first medical image data may be at least a portion of the first volume data acquired by the CT scanner. The second medical image data may be at least a portion of the second volume data acquired by the MRI scanner. This allows the medical image processing device 100 to improve the accuracy of non-rigid alignment even when non-rigid alignment is performed on CT images and MRI images acquired by different modality devices.

[0111] Furthermore, the first medical image data may consist of one or more two-dimensional medical image data. The second medical image data may consist of three-dimensional medical image data. This allows the medical image processing device 100 to improve the accuracy of non-rigid alignment even when non-rigid alignment is performed on two-dimensional and three-dimensional medical image data. [Industrial applicability]

[0112] This disclosure is useful for medical image processing equipment, medical image processing methods, and medical image processing programs that can improve the accuracy of non-rigid alignment of multiple medical image data. [Explanation of symbols]

[0113] 100 Medical Image Processing Equipment 110 ports 120 User Interface (UI) 130 displays 140 processors 150 memory 160 Processing Unit 161 Area Processing Unit 162 Image generation unit 163 Alignment Processing Unit 165 Display Control Unit 200 CT equipment MS Deformed Mesh MS1 First Deformed Mesh MS2 Second Deformed Mesh ND node VC convex hull region VF Fixed side volume VS difference area VM Deformed Volume VMA Deformed Volume Data VMB pre-deformation volume data

Claims

1. A medical image processing device, Equipped with a processing unit, The aforementioned processing unit, First medical image data and second medical image data, each consisting of two-dimensional or three-dimensional pixels representing the subject, are acquired. A non-rigid alignment process is performed to align the first medical image data and the second medical image data in a non-rigid position by deforming the second medical image data relative to the fixed first medical image data. An image based on the first medical image data and the second medical image data, which have been non-rigid alignment, is displayed on the display unit. The spatial region of the second medical image data includes the portion not included in the spatial region of the first medical image data. The aforementioned non-rigid alignment process is performed by: A process for generating deformation information relating to the deformation of the second medical image data in a region that includes at least an reachable region which is a region that the second medical image data can reach through deformation, and which includes multiple nodes; The process includes, based on the deformation information, a process for deforming the second medical image data, The reachable region is a region that includes at least the spatial region of the second medical image data and the spatial region of the first medical image data. The deformation information includes movement information relating to the movement of at least one pixel included in the second medical image data in a region not included in the spatial region of the first medical image data. Medical image processing equipment.

2. The plurality of nodes constitute a deformable mesh, The medical image processing apparatus according to claim 1.

3. The deformation information includes deformation information relating to the deformation of the second medical image data in the convex hull region of the sum region of the spatial region of the first medical image data and the spatial region of the second medical image data. The medical image processing apparatus according to claim 1.

4. The aforementioned deformation information is, The first deformation information relating to the deformation of the second medical image data in the spatial domain of the first medical image data, This includes a second deformation information relating to the deformation of the second medical image data in the difference region obtained by subtracting the spatial region of the first medical image data from the spatial region of the second medical image data before deformation, The medical image processing apparatus according to claim 1.

5. The first medical image data and the second medical image data are at least a portion of the volume data acquired by the CT scanner. The medical image processing apparatus according to claim 3 or 4.

6. The first medical image data is at least a portion of the first volume data acquired by the CT scanner. The aforementioned second medical image data is at least a portion of the second volume data acquired by the MRI device. The medical image processing apparatus according to claim 3 or 4.

7. The first medical image data is one or more two-dimensional medical image data, The second medical image data mentioned above is a three-dimensional medical image data. The medical image processing apparatus according to claim 2 or 3.

8. A step of acquiring first medical image data and second medical image data, which consist of two-dimensional or three-dimensional pixels representing a subject, The steps include: performing non-rigid alignment between the first medical image data and the second medical image data by deforming the second medical image data relative to the fixed first medical image data; The steps include displaying an image on a display unit based on the first medical image data and the second medical image data that have been non-rigid alignment, It has, The spatial region of the second medical image data includes the portion not included in the spatial region of the first medical image data. The step of performing the aforementioned non-rigid body alignment is: A step of generating deformation information relating to the deformation of the second medical image data in a region that includes at least an reachable region which is a region that the second medical image data can reach by deformation, and which includes multiple nodes, The step of deforming the second medical image data based on the deformation information is included, The reachable region is a region that includes at least the spatial region of the second medical image data and the spatial region of the first medical image data. The deformation information includes movement information relating to the movement of at least one pixel included in the second medical image data in a region not included in the spatial region of the first medical image data. Medical image processing methods.

9. A medical image processing program for causing a computer to execute the medical image processing method described in claim 8.

Images