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

Abstract

This invention provides a medical image processing device, method, and program that can reduce the difference in image quality between images obtained by visualizing interpolated tomographic data and original tomographic data, thereby reducing diagnostic discrepancies. [Solution] The processor in the medical image processing device acquires multiple time-series tomographic data, generates tissue motion analysis information between the first and second tomographic data at the first and second time points contained therein, deforms the first tomographic data based on the first tomographic data and motion analysis information to generate the first deformed tomographic data at a third time point between the first and second time points, deforms the second tomographic data based on the second tomographic data and motion analysis information to generate the second deformed tomographic data at the third time point, generates the first and second Laplacian pyramids based on the first and second deformed tomographic data, respectively, and generates a composite Laplacian pyramid by combining these, and generates interpolated tomographic data at the third time point based on this.

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, there is known a medical image processing apparatus that obtains volume data of a plurality of phases in chronological order and generates volume data of a phase between the plurality of phases by interpolation. This medical image processing apparatus is a medical image processing apparatus that visualizes each of a plurality of pieces of volume data having time, and includes a first generation unit that generates geometry information for one piece of volume data among the plurality of pieces of volume data, a second generation unit that generates motion analysis information between the one piece of volume data and at least one other piece of volume data among the plurality of pieces of volume data, a third generation unit that generates corresponding new geometry information for the at least one other piece of volume data among the plurality of pieces of volume data based on the motion analysis information and the geometry information, and a display processing unit that performs processing for displaying an image including the corresponding new geometry information for the at least one other piece of volume data. The medical image processing apparatus further includes a fourth generation unit that generates interpolated volume data for interpolating between the one piece of volume data and the at least one other piece of volume data, and a fifth generation unit that generates corresponding interpolated geometry information for the interpolated volume data based on the motion analysis information and the geometry information, and the display processing unit performs processing for displaying an image including the interpolated geometry information for the interpolated volume data (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the medical image processing apparatus described in Patent Document 1, the image quality of the image visualizing the interpolated volume data differs from the image visualizing the original volume data, which can lead to diagnostic inconsistencies. For example, the interpolated volume data is created by alpha blending the volume data of the preceding and succeeding phases. In this case, alpha blending attenuates high-frequency components present in the original volume data, removes noise, and causes the edges within the volume data to be lost. As a result, the image quality of the image visualizing the original volume data may differ from the image visualizing the interpolated volume data, which can lead to diagnostic inconsistencies.

[0005] This disclosure provides a medical image processing device, a medical image processing method, and a medical image processing program that can reduce the difference in image quality between an image with interpolated tomographic data visualized and an image with original tomographic data visualized, thereby reducing diagnostic inconsistencies. [Means for solving the problem]

[0006] One aspect of the present disclosure is a medical image processing apparatus comprising a processor, wherein the processor acquires a plurality of time-series tomographic data, generates motion analysis information relating to tissue movement in a first tomographic data at a first time point and a second tomographic data at a second time point included in the plurality of tomographic data, generates first deformed tomographic data at a third time point between the first and second time points by deforming the first tomographic data based on the first tomographic data and the motion analysis information, generates second deformed tomographic data at the third time point by deforming the second tomographic data based on the second tomographic data and the motion analysis information, generates a first Laplacian pyramid based on the first deformed tomographic data, generates a second Laplacian pyramid based on the second deformed tomographic data, generates a composite Laplacian pyramid by combining the first Laplacian pyramid and the second Laplacian pyramid, and generates interpolated tomographic data at the third time point based on the composite Laplacian pyramid.

[0007] One aspect of the present disclosure is a medical image processing method comprising: acquiring a plurality of time-series tomographic data; generating motion analysis information relating to tissue movement in a first tomographic data at a first time point and a second tomographic data at a second time point included in the plurality of tomographic data; generating a first deformed tomographic data at a third time point between the first and second time points by deforming the first tomographic data based on the first tomographic data and the motion analysis information; generating a second deformed tomographic data at a third time point by deforming the second tomographic data based on the second tomographic data and the motion analysis information; generating a first Laplacian pyramid based on the first deformed tomographic data; generating a second Laplacian pyramid based on the second deformed tomographic data; generating a composite Laplacian pyramid by combining the first and second Laplacian pyramids; and generating interpolated tomographic data at a third time point based on the composite Laplacian pyramid.

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

[0009] According to this disclosure, it is possible to reduce the difference in image quality between images with interpolated tomographic data visualized and images with original tomographic data visualized, maintain high-frequency components, and reduce diagnostic inconsistencies. [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 3] A diagram illustrating the generation of motion analysis information. [Figure 4] Diagram to explain phase interpolation [Figure 5] A diagram to explain the relationship between the original image, the transformed image, and the interpolated image. [Figure 6] A diagram showing an example of the Laplacian Pyramid. [Figure 7] A diagram illustrating the generation of interpolated volume data based on LPB processing. [Figure 8] Figure showing an example of two deformed images and an interpolated image. [Figure 9] Figure 8 shows an example of an enlarged image of the two deformed images and the interpolated image shown. [Figure 10] A flowchart illustrating the overall operation of a medical image processing system. [Figure 11] A diagram illustrating the relationship between volume data and interpolated volume data. [Figure 12] A flowchart illustrating an example of the operation involved in generating interpolated volume data. [Modes for carrying out the invention]

[0011] Hereinafter, embodiments will be described in detail with appropriate reference to the drawings. However, unnecessarily detailed descriptions may be omitted in some cases. For example, detailed descriptions of well-known matters and descriptions of substantially identical configurations may be omitted. This is to avoid unnecessary redundancy in the following description and to facilitate understanding by those skilled in the art. The accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure, and are not intended to limit the subject matter recited in the claims.

[0012] (First Embodiment) Figure 1 is a block diagram showing a configuration example of a medical image processing apparatus 100 according to the first embodiment. The medical image processing apparatus 100 includes a port 110, a UI 120, a display 130, a processor 140, and a memory 150.

[0013] A CT apparatus 200 is connected to the medical image processing apparatus 100. The medical image processing apparatus 100 acquires volume data from the CT apparatus 200 and performs processing on the acquired volume data. The medical image processing apparatus 100 may be configured by a PC and software installed on the PC.

[0014] The CT apparatus 200 irradiates a subject with X-rays, and captures an image (CT image) using differences in X-ray absorption by tissues inside the body. The subject may include a living body, a human body, an animal, or the like. The CT apparatus 200 acquires sinograms from an X-ray detector, and generates tomographic data (for example, slice data) of the subject through image reconstruction based on the sinograms. The CT apparatus 200 generates volume data based on the slice data, for example, by stacking the slice data. The slice data and the volume data include information on any location inside the subject. The CT apparatus 200 transmits the volume data as the CT image to the medical image processing apparatus 100 via a wired line or a wireless line. For capturing the CT image, imaging conditions related to CT imaging and contrast conditions related to contrast medium administration may be considered. Note that contrast enhancement may be performed not only on blood vessels but also on digestive organs, bile ducts, and the like. Contrast enhancement may be performed multiple times at different timings according to the characteristics of the organ.

[0015] The port 110 in the medical image processing apparatus 100 includes a communication port, an external device connection port, or a connection port to an embedded device, etc., and acquires volume data obtained from CT images. The acquired volume data may be immediately sent to the processor 140 for various types of processing, or may be stored in the memory 150 and then sent to the processor 140 for various types of processing when necessary. In addition, the volume data may be acquired via a recording medium or a recording media. Further, the volume data may be acquired in the form of intermediate data, compressed data, sinogram, or slice data, etc. Moreover, the volume data may be acquired from information from a sensor device attached to the medical image processing apparatus 100.

[0016] The UI 120 may include a touch panel, a pointing device, a keyboard, or a microphone. The UI 120 accepts any input operation from the user of the medical image processing apparatus 100. The user may include a doctor, a radiologic technologist, a student, or other Paramedic Staff.

[0017] The UI 120 accepts various operations. For example, it accepts operations such as specifying a Region of Interest (ROI) and setting luminance conditions (e.g., window information) for volume data and images based on the volume data (e.g., 3D images and 2D images to be described later). The Region of Interest may include regions of various tissues (e.g., blood vessels, bronchi, organs, apparatuses, bones, brain). The tissues may include lesional tissue, normal tissue, or tumor tissue, etc. The window information includes at least one of Window Width (WW) and Window Level (WL), and is information for adjusting the luminance of the displayed image.

[0018] The display 130 may include, for example, an LCD or an organic EL display, and displays various information. The various information may include three-dimensional images and two-dimensional images obtained from volume data. Three-dimensional images may include volume rendering images, surface rendering images, virtual endoscopic images, virtual ultrasound images, etc. Volume rendering images may include RaySum images, MIP images, MinIP images, average value images, or raycast images, etc. Two-dimensional images may include MPR images or CPR images, etc. MPR images may include axial images, sagittal images, coronal images, and other MPR images.

[0019] 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 may be detachable from the medical image processing device 100. Memory 150 stores various information and programs. The various information may include volume data acquired by port 110, images generated by processor 140, setting information set by processor 140, and various programs. Memory 150 is an example of a non-transient recording medium on which programs are recorded.

[0020] The processor 140 may include a CPU, DSP, or GPU. The processor 140 may be composed of various integrated circuits (e.g., LSI or FPGA). The processor 140 functions as a processing unit 160 that performs various processing and control by executing a medical image processing program stored in the memory 150.

[0021] Figure 2 is a block diagram showing an example of the functional configuration of the processing unit 160.

[0022] The processing unit 160 comprises a region processing unit 161, a deformation processing unit 162, an interpolation processing unit 164, an image generation unit 165, and a display control unit 166.

[0023] The processing unit 160 oversees the processing and control of each part of the medical image processing device 100. Note that each part included in the processing unit 160 may be implemented as a different function 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.

[0024] 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. In this embodiment, the region of interest includes, for example, the heart.

[0025] The deformation processing unit 162 detects the movement of tissue contained in the volume data based on multiple volume data obtained in time series and generates motion analysis information related to the movement of the tissue. In this case, the deformation processing unit 162 performs motion analysis on the deformation (difference) of the volume data between multiple phases based on the volume data of multiple phases and obtains motion analysis information in the volume data. A phase is the time position in one cycle when the tissue performs periodic movement. Specific methods for motion analysis are described, for example, in Reference Patent Documents 1 and 2. These are examples of non-rigid registration, but rigid registration may also be used.

[0026] (Reference Patent Document 1: U.S. Patent No. 8311300) (Reference Patent Document 2: Japanese Patent No. 5408493)

[0027] The deformation processing unit 162 may acquire information relating to the amount of movement and velocity of movement of any point in the volume data as motion analysis information. If the deformation processing unit 162 applies the method of Reference Patent Document 1 to 3D data, it may divide the volume data into 3D grid nodes (k,l,m), and if the 3D coordinates at the grid node (k,l,m,t) in phase t of the 3D grid are (x,y), it may calculate information relating to the amount of movement of the grid points of node (k,l,m) based on the difference of multiple nodes (k,l,m,t) obtained by changing the value of phase t. Furthermore, the deformation processing unit 162 may calculate velocity information by differentiating the amount of movement information with respect to time. The information of the amount of movement and velocity may be represented as vectors. The 3D grid is also called a deformation grid.

[0028] When the deformation processing unit 162 interpolates the motion analysis information of this three-dimensional grid for each point in the entire volume data, motion analysis information for each point in the volume data is obtained. When this motion analysis information for a predetermined point is applied to each point in the region of interest, motion analysis information for each point in the region of interest is obtained.

[0029] Furthermore, when the deformation processing unit 162 applies the method described in Reference Patent Document 2, among the volume data arranged in time series, the volume data t k-1 and its time information t k-1 , and volume data t k and its time information t k Motion analysis information may be generated based on this. Motion analysis information may refer to information about the correspondence between corresponding positions or objects on multiple volume data, and information about the process of the position and object moving and changing.

[0030] Furthermore, the deformation processing unit 162 is not limited to the methods described in Reference Patent Documents 1 and 2, and may perform motion analysis using other known registration methods. The medical image processing device 100 can determine where any position within the subject has moved by performing motion analysis of each point or region of interest using motion analysis information.

[0031] Furthermore, the deformation processing unit 162 may generate deformed volume data in which the volume data for a predetermined phase has been deformed, based on the volume data for a predetermined phase and motion analysis information.

[0032] The interpolation processing unit 164 performs processing related to Laplacian Pyramid Blending (LPB). Hereinafter, Laplacian Pyramid Blending will also be simply referred to as "LPB". LPB processing achieves smooth blending at the boundaries of images by processing images while representing them at different resolutions using a pyramidal data structure (Laplacian pyramid). A Laplacian pyramid can be described as a representation format for two-dimensional or three-dimensional images. For general LPB, see, for example, the following reference patent document 1. As described later, the interpolation processing unit 164 creates a Laplacian pyramid of volume data (Laplacian volume data). Laplacian pyramid and Laplacian volume data have the same meaning; a Laplacian pyramid is two-dimensional or three-dimensional data, and Laplacian volume data is three-dimensional data.

[0033] (Reference Non-Patent Literature 1: PETER J. BURT and EDWARD H. ADELSON, RCA David Sarnoff Research Center, “A Multiresolution Spline With Application to Image Mosaics”, ACM Transactions on Graphics, Vol.2. No.4, October 1983, Pages 217-236)

[0034] The interpolation processing unit 164 interpolates the volume data at an arbitrary point in time between two adjacent phases, based on the volume data from those two phases, to generate interpolated volume data. In this case, the interpolation processing unit 164 may generate the interpolated volume data based on the processing result of the LPB (Laplacian Processing). Specifically, the interpolation processing unit 164 may synthesize the Laplacian volume data of the two original volume data to generate synthesized Laplacian volume data, and then generate the interpolated volume data based on the synthesized Laplacian volume data.

[0035] The image generation unit 165 generates various images. The image generation unit 165 generates 3D images and 2D images based on at least a portion of the acquired volume data (for example, volume data of an extracted region). The image generation unit 165 may generate images by performing various rendering methods (for example, volume rendering or surface rendering).

[0036] The display control unit 166 causes various data, information, or images to be displayed 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 165.

[0037] Next, I will provide some additional information regarding motion analysis. Figure 3 is a diagram illustrating the generation of motion analysis information.

[0038] The deformation processing unit 162 analyzes where each point has moved by aligning the volume data of each phase and determining the correspondence between pixels, thereby generating motion analysis information. For example, based on the volume data V[t] of phase t and the volume data V[t+1] of phase t+1, it generates motion analysis information D[t] regarding the deformation of the volume data from phase t to phase t1.

[0039] Next, we will explain phase interpolation. Figure 4 is a diagram illustrating phase interpolation. Figure 5 is a diagram illustrating the relationship between the original image, the transformed image, and the interpolated image.

[0040] As shown in Figure 4, the deformation processing unit 162 interpolates the volume data at any point in time between two adjacent phases (e.g., phase t+Δt) based on the volume data and motion analysis information (e.g., motion analysis information D[t]) of two adjacent phases (e.g., phase t and phase t+1).

[0041] As shown in Figure 5, in phase interpolation, volume data V[t+Δt] at an arbitrary time point t+Δt between phase t and phase t+1 is generated as interpolated volume data IV. In this case, the deformation processing unit 162 generates deformation volume data DV1 that occurs Δt time after phase t, based on the volume data V[t] of phase t and the motion analysis information D[t]. The deformation processing unit 162 also generates deformation volume data DV2 that occurs before phase t+1 and Δt time after phase t, based on the volume data V[t+1] of phase t+1 and the motion analysis information D[t]. In other words, although the two deformation volume data sets DV1 and DV2 are generated as volume data at the same time point (phase t+Δt), the original volume data is different, and the content of the deformation volume data may be slightly different. Note that the deformation grid GD in Figure 5 shows the movement of each point from the original volume data.

[0042] Furthermore, when these two deformed volume data are alpha-blended, if the blended (combined) volume data is visualized, the sharpness of the original image (the image visualizing the volume data V[t] of phase t and the volume data V[t+1] of phase t+1) may be lost due to subtle texture discrepancies between the two deformed volume data. In contrast, in this embodiment, the interpolation processing unit 164 can suppress the reduction in sharpness by combining the two deformed volume data based on the processing results of the LPB processing.

[0043] Next, we will explain LPB processing.

[0044] Figure 6 shows an example of a Laplacian pyramid.

[0045] The interpolation processing unit 164 hierarchically generates multiple volume data sets for different frequencies based on the original volume data. Specifically, the interpolation processing unit 164 generates volume data (blurred data BD) by reducing the resolution of the original volume data by a predetermined percentage (e.g., half). In this case, the interpolation processing unit 164 repeatedly generates blurred data as the volume data for the lower layer, using the volume data of the upper layer as the original volume data in each hierarchical level. This generates a Gaussian pyramid GP containing multiple volume data sets for different frequency levels (also simply referred to as "levels"), and hierarchically generates differential volume data showing the difference between two adjacent levels of volume data in the Gaussian pyramid GP. The interpolation processing unit 164 generates a Laplacian pyramid LP containing multiple differential volume data sets hierarchically for each frequency level. In other words, in the Laplacian pyramid LP, the interpolation processing unit 164 extracts high-frequency component information by taking the difference between layers (levels) of the image with different resolutions. The differential volume data is also called Laplacian level-specific volume data LLV. The original volume data of the top layer is, for example, the deformed volume data DV1 and DV2. Furthermore, since there is no volume data below the lowest layer of the volume data, the interpolation processing unit 164 uses the volume data of the lowest layer of the Gaussian pyramid GP as is.

[0046] The interpolation processing unit 164 expands the Laplacian level-specific volume data LLV at each level of the Laplacian pyramid LP to the size of the original volume data, then adds them together to generate a single volume data (pyramid up). In this case, the interpolation processing unit 164 may also generate a single volume data by adding the pixel values ​​of each pixel to match the volume sizes of the Laplacian level-specific volume data LLV at each level. Alternatively, if the Laplacian pyramid LP is pyramid up without any processing, the original volume data is restored.

[0047] Furthermore, the interpolation processing unit 164 may perform a pyramid-up operation as follows. Specifically, when combining two Laplacian level-specific volume data LLVs of adjacent frequency levels, the interpolation processing unit 164 may add the pixel values ​​of each pixel to match the volume size of the upper layer's Laplacian level-specific volume data LLV, and create a new upper layer's Laplacian level-specific volume data LLV (one-step pyramid-up). Then, by sequentially performing this one-step pyramid-up from the lower layer to the upper layer, the entire system is pyramid-up, and finally a single volume data is generated.

[0048] In this embodiment, as shown in Figure 7, the Laplacian pyramid that is pyramidized is a composite Laplacian pyramid SLP (composite Laplacian volume data SLV) formed by combining the respective Laplacian pyramids LP1 and LP2 (Laplacian volume data LV1 and LV2) for multiple deformed volume data DV1 and DV2. In this case, the interpolation processing unit 164 may generate the composite Laplacian pyramid SLP by combining the Laplacian pyramids LP1 and LP2 level by level. In this case, the interpolation processing unit 164 may generate composite Laplacian level-specific volume data SLLV by combining the Laplacian level-specific volume data LLV1 and LLV2 contained in each Laplacian pyramid LP1 and LP2 level by level. The level-specific composite Laplacian level-specific volume data SLLV constitute the composite Laplacian pyramid SLP.

[0049] Next, we will explain the generation of interpolated volume data based on LPB processing. Figure 7 illustrates the generation of interpolated volume data based on LPB processing.

[0050] The interpolation processing unit 164 uses the deformed volume data DV1 of phase t+Δt as the original volume data of the top layer of the Gaussian pyramid GP to generate Laplacian volume data LV1 of the deformed volume data DV1. The interpolation processing unit 164 uses the deformed volume data DV2 of phase t+Δt as the original volume data of the top layer of the Gaussian pyramid GP to generate Laplacian volume data LV2 of the deformed volume data DV2.

[0051] The interpolation processing unit 164 weights the Laplacian volume data LV1 based on the absolute value of the pixel value of each pixel in the Laplacian volume data LV1. The interpolation processing unit 164 weights the Laplacian volume data LV2 based on the absolute value of the pixel value of each pixel in the Laplacian volume data LV2. In this case, the interpolation processing unit 164 may weight each level-specific Laplacian level volume data LLV1 contained in the Laplacian volume data LV1 based on the absolute value of the pixel value of each pixel in that Laplacian level volume data LLV1. Furthermore, the interpolation processing unit 164 may weight each level-specific Laplacian level volume data LLV2 contained in the Laplacian volume data LV2 based on the absolute value of the pixel value of each pixel in that Laplacian level volume data LLV2.

[0052] The interpolation processing unit 164 calculates a weighted map based on the absolute value of the pixel value of each pixel in the Laplacian volume data LV. In this case, the interpolation processing unit 164 may generate a weighted map for each level of the Laplacian level-specific volume data LLV. In other words, the interpolation processing unit 164 can be said to generate a weighted map for each level in the Laplacian pyramid LP.

[0053] In commonly used LPB (Laplacian Printing), weighting is performed using region maps that represent the respective display areas contained in the two images. However, in this embodiment, since a weighting map is generated directly based on the Laplacian volume data, it is not necessary to use a region map. The interpolation processing unit 164 may also use a combination of weighting maps and region maps to weight the Laplacian volume data LV1 and Laplacian volume data LV2.

[0054] The interpolation processing unit 164 combines the weighted Laplacian volume data LV1 and the weighted Laplacian volume data LV2 to generate the composite Laplacian volume data SLV. Specifically, for each level, the interpolation processing unit 164 combines the weighted Laplacian level-specific volume data LLV1 and the weighted Laplacian level-specific volume data LLV2 to generate the composite Laplacian level-specific volume data SLLV. In this case, the interpolation processing unit 164 mixes them in proportion to the absolute value of the pixel values ​​of each image in the Laplacian volume data LV. Furthermore, the combined Laplacian volume data LV1 and LV2 are data that have been adjusted to match the size of the original volume data (the deformed volume data DV1 and DV2 to be combined). As a result, the interpolation processing unit 164 can pyramid up the composite Laplacian volume data SLV to more easily reflect areas with strong high-frequency components in the original volume data, thereby generating an interpolated volume data IV with sharp edges.

[0055] Here is an example of pseudocode for generating synthetic Laplacian volume data (SLV).

[0056] w=(1-ratio)*abs(LV1[x,y,z])+ratio*abs(LV2[x,y,z]); w1=(1-ratio)*abs(LV1[x,y,z]) / w; w2 = ratio * abs(LV2[x, y, z]) / w; SLV[i2][x,y,z]=w1*LV1[x,y,z]+w2*LV2[x,y,z];

[0057] Here, "Ratio" indicates the bias in the time position of the interpolation target between adjacent phases. For example, if the target interpolation position is the middle time position between phase t and phase t+1, then Ratio = 1 / 2. For example, if the time between phase t and phase t+1 is divided in a 1:2 ratio, and the target interpolation position is closer to phase t than to phase t+1, then Ratio = 1 / 3. "w" represents the total weight. "w1" represents the weight for the deformed volume data DV1 and indicates the contribution of the deformed volume data DV1 to the interpolation. "w2" represents the weight for the deformed volume data DV2 and indicates the contribution of the deformed volume data DV2 to the interpolation.

[0058] LV1[x,y,z] (position [x,y,z] in Laplacian volume data LV1) and LV2[x,y,z] (position [x,y,z] in Laplacian volume data LV2) represent each pixel of the Laplacian volume data LV (Laplacian pyramid LP), and indicate the respective voxel contained in the hierarchical Laplacian level-specific volume data LLV contained within the Laplacian volume data LV. The values ​​indicated by LV1[x,y,z] and LV2[x,y,z] represent the voxel values ​​of the respective voxels contained in the hierarchical Laplacian level-specific volume data LLV1 and LLV2 in the Laplacian volume data LV1 and LV2, respectively.

[0059] In the pseudocode example above, the weight map exists abstractly, and each pixel (w1, w2) of the weight map is generated. However, once the calculation for that pixel is complete, it is deleted, and the entire weight map does not necessarily need to be generated concretely.

[0060] Next, we will explain examples of deformed and interpolated images.

[0061] Figure 8 shows an example image of two deformed images DG1 and DG2 and an interpolated image IG. Figure 9 shows an enlarged example image of the two deformed images DG1 and DG2 and the interpolated image IG shown in Figure 8. The deformed and interpolated images in Figures 8 and 9 are images from phase t+Δt.

[0062] The image generation unit 165 generates a deformed image DG1 as a 3D or 2D image based on the deformed volume data DV1. The image generation unit 165 generates a deformed image DG2 as a 3D or 2D image based on the deformed volume data DV2. The image generation unit 165 generates an interpolated image IG as a 3D or 2D image based on the interpolated volume data IV.

[0063] Figures 8 and 9 show three interpolated images IGX, IGA, and IGB as interpolated image IG. Interpolated image IGX is an image in which interpolated volume data obtained by alpha-blending deformed volume data DV1 and deformed volume data DV2 is visualized. Interpolated image IGA is an image in which interpolated volume data obtained by pyramiding up composite Laplacian volume data SLV, which is simply alpha-blended with uniform weights for each pixel in Laplacian volume data LV1 and Laplacian volume data LV2. This corresponds to an interpolated image obtained by LPB processing without using a weighting map. Interpolated image IGB is an image in which interpolated volume data obtained by pyramiding up composite Laplacian volume data SLV, which is synthesized by applying weights based on the absolute value of each pixel's pixel value to each pixel in Laplacian volume data LV1 and Laplacian volume data LV2.

[0064] Next, the operation of the medical image processing device 100 will be described.

[0065] Figure 10 is a flowchart showing an example of the overall operation of the medical image processing device 100. Figure 11 illustrates the relationship between volume data V and interpolated volume data IV. Figure 11 shows an example where n=4 and n²=4. n is the number of phases. n² is the number of interpolations between adjacent phases, and therefore the number of interpolated volume data points. In other words, the number of interpolations between phases is not limited to one, but can be any number.

[0066] First, the region processing unit 161 acquires volume data V[i] of multiple subjects in a time series of n phases (step S11). i is a variable that identifies the volume data and can take values ​​of 0, 1, ..., n-1. The region processing unit 161 extracts a region of interest from the volume data V[i], which is the region of a predetermined organ (e.g., the heart).

[0067] The deformation processing unit 162 generates motion analysis information D[0], D[1], ..., D[n-2] that shows how the organ changed between each phase (step S12). For example, the motion analysis information between phase 0 and phase 1 is motion analysis information D[0].

[0068] The interpolation processing unit 164 generates interpolated volume data IV[i2] (step S13). i2 is a variable that identifies the interpolated volume data and can take values ​​of 0, 1, ..., n2-1. In other words, there are n2 values ​​of i2. The interpolation processing unit 164 associates the interpolated volume data IV[0] with volume data[i] and the interpolated volume data IV[n2-1] with volume data[i+1], and interpolates the space between volume data V[i] and volume data V[i+1] with (n2-2) interpolated volume data. As a result, the interpolation processing unit 164 generates interpolated volume data IV[1], interpolated volume data IV[2], ..., and interpolated volume data IV[n2-2] along with the interpolated volume data IV[0] and interpolated volume data IV[n2-1]. In other words, each phase in which volume data is acquired from the CT device 200 is also included in the phase in which the interpolated volume data is derived. The interpolated volume data in this phase is the same as the original volume data.

[0069] The interpolation processing unit 164 arranges the generated interpolated volume data IV in a time series and generates volume data V2[0], volume data V2[1], ..., volume data V2[(n-1)*(n2-1)] in a time series for {(n-1)*(n2-1)+1} phases (including the time position to be interpolated as a phase) (step S14).

[0070] The display control unit 166 displays the generated volume data V2 on the display 130 (step S15).

[0071] Figure 12 is a flowchart illustrating an example of the operation involved in generating interpolated volume data in step S13. Here, interpolation between phase 1 and phase 2 is illustrated, but the same applies to interpolation between other adjacent phases.

[0072] The deformation processing unit 162 applies motion analysis information D[i] to volume data V[i] by +i2 / (n2-1) based on volume data V[i] and motion analysis information D[i] to generate deformed volume data DV1 (step S21). Applying motion analysis information D[i] by "+i2 / (n2-1)" means applying it to the amount of movement of each point in volume data V[t] derived from D[i] by "i2 / (n2-1)" in the later time direction. For example, in Figure 11, when i2=1, the time position corresponding to i2=1 (It[1 / 3]) indicates that each point in volume data V[1] is deformed by moving it in the later time direction by 1 / 3 of the amount of movement defined by motion analysis information D[1].

[0073] The deformation processing unit 162 applies motion analysis information D[i] to volume data V[i+1] by -(n2-1-i2) / (n2-1) based on volume data V[i] and motion analysis information D[i] to generate deformed volume data DV2 (step S22). Applying motion analysis information D[i] by "-(n2-1-i2 / (n2-1)" means applying it to the amount of movement of each point in volume data V[t] derived from D[i] by "(n2-1-i2) / (n2-1)" in the direction of the previous time. For example, in Figure 11, when i2=1, the time position (It[1 / 3]) corresponding to i2=1 indicates that each point in volume data V[2] is deformed by moving it in the direction of the previous time by 2 / 3 of the amount of movement defined by motion analysis information D[1].

[0074] The interpolation processing unit 164 generates a Laplacian pyramid LP1 of the deformed volume data DV1 (step S23). The Laplacian pyramid LP1 consists of multiple Laplacian level-specific volume data LLV1 for different frequency levels.

[0075] The interpolation processing unit 164 generates a Laplacian pyramid LP2 of the deformed volume data DV2 (step S23). The Laplacian pyramid LP2 consists of multiple Laplacian level-specific volume data LLV2 for different frequency levels.

[0076] The interpolation processing unit 164 creates a weighted map 1 for each level of the Laplacian pyramid LP1, consisting of the absolute values ​​of each pixel in the Laplacian pyramid LP1 (step S24). In this case, the interpolation processing unit 164 may generate a weighted map 1 for each Laplacian level volume data LLV1, consisting of the absolute values ​​of each pixel. The interpolation processing unit 164 creates a weighted map 2 for each level of the Laplacian pyramid LP2, consisting of the absolute values ​​of the pixels in the Laplacian pyramid LP2. In this case, the interpolation processing unit 164 may generate a weighted map 2 for each Laplacian level volume data LLV2, consisting of the absolute values ​​of each pixel.

[0077] The interpolation processing unit 164 applies weighting map 1 and weighting map 2 to each level of the Laplacian pyramid LP1 and Laplacian pyramid LP2, respectively, and combines them to create a combined Laplacian pyramid SLP (step S24). In this case, the interpolation processing unit 164 may apply weighting map 1 and weighting map 2 to the Laplacian level-specific volume data LLV1 and Laplacian level-specific volume data LLV2 for each level, and combine them to generate a combined Laplacian level-specific volume data SLLV. Applying a weighting map here means that when blending (combining) the Laplacian level-specific volume data LLV1 and Laplacian level-specific volume data LLV2, the pixels originating from Laplacian level-specific volume data LLV1 and the Laplacian level-specific volume data LLV2 are multiplied by the corresponding pixel values ​​of weighting map 1 and weighting map 2, respectively, as weights, and then blend (combine) them.

[0078] The interpolation processing unit 164 pyramidizes the composite Laplacian pyramid SLP to generate interpolated volume data IV (step S25).

[0079] Next, variations of this embodiment will be described.

[0080] The medical image processing device 100 may generate interpolated volume data for volume data that changes periodically over time. For example, the medical image processing device 100 may generate interpolated volume data IV at a point in time corresponding to the timing of electrocardiogram synchronization (i.e., the timing of the heartbeat in the electrocardiogram). In this case, the medical image processing device 100 can obtain, as interpolated volume data, volume data for important timings in the heartbeat that is not available from the volume data obtained from the CT device 200 at predetermined time intervals.

[0081] Furthermore, while the above description primarily illustrates the interpolation of volume data in three-dimensional space (an example of tomographic data) over time, the medical image processing device 100 is not limited to this. The medical image processing device 100 may also interpolate images on a two-dimensional plane (e.g., three-dimensional images or two-dimensional images) (an example of tomographic data) over time. For example, at least a portion of the motion analysis information, deformation volume data, Laplacian pyramid, Laplacian level-specific volume data, Laplacian volume data, and interpolated volume data described above may be obtained as data on a two-dimensional plane rather than data on three-dimensional space.

[0082] Furthermore, the time interval (interpolation interval) at which 3D interpolated volume data or 2D interpolated images are generated may be equal or unequal.

[0083] Thus, the medical image processing device 100 of this embodiment can suppress the difference between the image quality of the image visualized from the interpolated volume data and the image quality of the image visualized from the original volume data, thereby suppressing the possibility of diagnostic inconsistencies. In this process, the medical image processing device 100 generates two deformed volume data sets for the same point in time of the interpolation target based on two adjacent volume data sets and motion analysis information. Then, the medical image processing device 100 synthesizes the two deformed volume data sets. In this process, the medical image processing device 100 performs LPB processing. In LPB processing, a Laplacian pyramid LP is constructed for each deformed volume data set to generate Laplacian volume data LV1 and LV2. Then, by synthesizing the Laplacian volume data LV1 and LV2, the medical image processing device 100 can maintain the high-frequency components that existed in the deformed volume data and maintain the edges and textures in the original volume data. Therefore, when visualizing multiple volume data sets in a time series, the medical image processing device 100 can smoothly reproduce the movement of tissues and organs within the volume data and provide the user with an image that does not feel unnatural.

[0084] 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. Furthermore, the components of the above embodiments may be combined in any way without departing from the spirit of the invention.

[0085] 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.

[0086] 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).

[0087] 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.

[0088] Furthermore, while the example illustrates the acquisition of images using a 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. Other devices include MRI (Magnetic Resonance Imaging) scanners, PET (Positron Emission Tomography) scanners, angiography scanners, or other modality devices. In addition, PET scanners may be used in combination with other modality devices.

[0089] 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 for causing a computer to execute the medical image processing method.

[0090] (Summary of the above embodiment) Based on the above, this disclosure contains at least the following information. The components and other elements in parentheses are examples of those corresponding to the embodiments described above, but are not limited to these.

[0091] [Item 1] A medical image processing device (medical image processing device 100) equipped with a processor (processor 140), The aforementioned processor, By acquiring multiple fault data in a time series, Motion analysis information regarding tissue movement in the first tomographic data at a first time point (phase t) and the second tomographic data at a second time point (phase t+1), which are included in the aforementioned plurality of tomographic data, is generated. Based on the first fault data and the motion analysis information, the first deformed fault data (deformed volume data DV1) is deformed to generate a first deformed fault data at a third time point (phase t+Δt) between the first time point and the second time point. By deforming the second fault data based on the second fault data and the motion analysis information, the second deformed fault data (deformed volume data DV2) at the third time point is generated. Based on the first deformation fault data, a first Laplacian pyramid (Laplacian pyramid LP1, Laplacian volume data LV1) is generated. Based on the second deformation fault data, a second Laplacian pyramid (Laplacian pyramid LP2, Laplacian volume data LV2) is generated. By combining the first Laplacian pyramid and the second Laplacian pyramid, a composite Laplacian pyramid (composite Laplacian pyramid SLP, composite Laplacian volume data SLV) is generated. Based on the aforementioned composite Laplacian pyramid, interpolated tomographic data (interpolated volume data IV) at the third time point is generated. Medical image processing equipment.

[0092] This allows the medical image processing device to reduce the difference in image quality between images visualized from interpolated tomographic data and images visualized from original tomographic data, and to generate smooth videos from multiple tomographic data points in a time series. Therefore, the medical image processing device can reduce the likelihood of users experiencing diagnostic discomfort.

[0093] [Item 2] The aforementioned processor, The first Laplacian pyramid is weighted based on the absolute value of the pixel value of each pixel in the first Laplacian pyramid. The second Laplacian pyramid is weighted based on the absolute value of the pixel value of each pixel in the second Laplacian pyramid. The combined Laplacian pyramid is generated by combining the weighted first Laplacian pyramid and the second Laplacian pyramid. The medical image processing equipment described in item 1.

[0094] As a result, medical image processing devices, for example, weight each pixel in a Laplacian pyramid more heavily the larger its absolute value. Therefore, medical image processing devices can generate clearer interpolated tomographic data with higher frequency components emphasized.

[0095] [Item 3] The aforementioned processor, The first Laplacian level-specific volume data (Laplacian level-specific volume data LLV1) for each of the multiple frequency levels constituting the first Laplacian pyramid and the second Laplacian level-specific volume data (Laplacian level-specific volume data LLV2) for each of the multiple frequency levels constituting the second Laplacian pyramid are combined for each frequency level to generate combined Laplacian level-specific volume data (combined Laplacian level-specific volume data SLLV) for each frequency level constituting the combined Laplacian pyramid. A medical image processing device as described in item 1 or 2.

[0096] This allows medical image processing equipment to easily generate Laplacian volume data using multiple Laplacian level volume data sets for each frequency level.

[0097] [Item 4] Fault data is volume data. A medical image processing device as described in any one of items 1 to 3.

[0098] This allows the medical image processing device to represent smooth state transitions in three-dimensional space by interpolating volume data corresponding to states in three-dimensional space.

[0099] [Item 5] Acquiring multiple fault data in time series, To generate motion analysis information regarding tissue movement in the first fault data at a first time point and the second fault data at a second time point, which are included in the aforementioned plurality of fault data, By deforming the first fault data based on the first fault data and the motion analysis information, the first deformed fault data at a third time point between the first time point and the second time point is generated. The second deformation fault data at the third time point is generated by deforming the second fault data based on the second fault data and the motion analysis information, Based on the first deformation fault data, a first Laplacian pyramid is generated, Based on the second deformation fault data, a second Laplacian pyramid is generated, By combining the first Laplacian pyramid and the second Laplacian pyramid, a composite Laplacian pyramid is generated. Based on the aforementioned synthetic Laplacian pyramid, interpolated tomographic data at the third time point is generated, A medical image processing method having [a specific feature].

[0100] As a result, the medical image processing method achieves the same effect as item 1.

[0101] [Item 6] A medical image processing program that causes a computer to execute the medical image processing methods described in item 5.

[0102] As a result, the medical image processing program can achieve the same effect as item 1. [Industrial applicability]

[0103] This disclosure is useful for medical image processing equipment, medical image processing methods, and medical image processing programs that can reduce the difference in image quality between images visualized from interpolated tomographic data and images visualized from original tomographic data, thereby reducing diagnostic inconsistencies. [Explanation of Symbols]

[0104] 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 Deformation Processing Unit 164 Interpolation Processing Unit 165 Image generation unit 166 Display Control Unit SLP Synthetic Laplacian Pyramid SLV Synthetic Laplacian Volume Data SLLV Composite Laplacian Volume Data by Level DV, DV1, DV2 Deformed Volume Data LLV, LLV1, LLV2 Laplacian Level Volume Data LP, LP1, LP2 Laplacian Pyramid LV, LV1, LV2 Laplacian Volume Data

Claims

1. A medical image processing device equipped with a processor, The aforementioned processor, By acquiring multiple fault data in a time series, Motion analysis information regarding tissue movement in the first fault data at a first time point and the second fault data at a second time point, which are included in the plurality of fault data, is generated. By deforming the first fault data based on the first fault data and the motion analysis information, the first deformed fault data is generated at a third time point between the first time point and the second time point. By deforming the second fault data based on the second fault data and the motion analysis information, the second deformed fault data at the third time point is generated. Based on the first deformation fault data, a first Laplacian pyramid is generated. Based on the second deformation fault data, a second Laplacian pyramid is generated. By combining the first Laplacian pyramid and the second Laplacian pyramid, a composite Laplacian pyramid is generated. Based on the aforementioned synthetic Laplacian pyramid, interpolated tomographic data at the third time point is generated. Medical image processing equipment.

2. The aforementioned processor, The first Laplacian pyramid is weighted based on the absolute value of the pixel value of each pixel in the first Laplacian pyramid. The second Laplacian pyramid is weighted based on the absolute value of the pixel value of each pixel in the second Laplacian pyramid. The combined Laplacian pyramid is generated by combining the weighted first Laplacian pyramid and the second Laplacian pyramid. The medical image processing apparatus according to claim 1.

3. The aforementioned processor, The first Laplacian level volume data for each frequency level constituting the first Laplacian pyramid and the second Laplacian level volume data for each frequency level constituting the second Laplacian pyramid are combined for each frequency level to generate the combined Laplacian level volume data for each frequency level constituting the combined Laplacian pyramid. The medical image processing apparatus according to claim 1 or 2.

4. Fault data is volume data. The medical image processing apparatus according to claim 1 or 2.

5. Acquiring multiple fault data in time series, To generate motion analysis information regarding tissue movement in the first fault data at a first time point and the second fault data at a second time point, which are included in the plurality of fault data, By deforming the first fault data based on the first fault data and the motion analysis information, the first deformed fault data is generated at a third time point between the first time point and the second time point. The second fault data is deformed based on the second fault data and the motion analysis information to generate the second deformed fault data at the third time point, Based on the first deformation fault data, a first Laplacian pyramid is generated, Based on the second deformation fault data, a second Laplacian pyramid is generated, By combining the first Laplacian pyramid and the second Laplacian pyramid, a composite Laplacian pyramid is generated. Based on the aforementioned synthetic Laplacian pyramid, interpolated tomographic data at the third time point is generated, A medical image processing method having [a specific feature].

6. A medical image processing program for causing a computer to execute the medical image processing method described in claim 5.

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