X-ray CT apparatus and processor for image processing

Abstract

To prevent excessive motion correction or deformation, particularly maintain continuity of images in a body axis direction, and perform motion correction reconstruction under an optimal condition according to an image reconstruction condition, in motion correction reconstruction of an X-ray CT apparatus.SOLUTION: A processor for image processing of an X-ray CT apparatus according to the present invention includes, as a motion estimation model for acquiring motion information, a motion estimation model including regularization terms that are independently adjustable for continuity of a spatial domain and continuity of a time domain. The motion estimation model is configured to be able to adjust a weight of the regularization term and a control point position. A motion information acquisition unit is configured to automatically adjust the motion estimation model, or a calculation method of a control point parameter using the motion estimation model, according to a FOV or an image reconstruction interval, serving as an image reconstruction condition. As a result, it is possible to satisfactorily maintain image continuity in one cross section and inter-slice image continuity in a body axis direction.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present invention relates to an X-ray CT apparatus and a processor for processing transmission X-ray data obtained by the X-ray CT apparatus, and particularly relates to a motion correction reconstruction technique for CT images.

Background Art

[0002] An X-ray CT apparatus arranges a subject at the opening of a scanner equipped with an X-ray source and an X-ray detector, performs imaging while rotating the scanner, and reconstructs an image of a cross-section of the subject using X-ray projection data acquired at each angle of the scanner. At this time, by performing imaging while moving the position of the subject in the body axis direction with respect to the scanner, images of a plurality of cross-sections are obtained along the body axis direction.

[0003] In imaging of a moving part of a subject, such as chest imaging using a CT apparatus, in order to reduce artifacts due to movement, motion correction reconstruction that detects the motion information of the subject during imaging and reconstructs an image using the motion information is widely adopted.

[0004] For the detection of motion information, a pair of partial reconstruction (PAR: Partial Angle Reconstruction) images (hereinafter referred to as PAR images) reconstructed using less than 180 degrees of transmission X-ray data (hereinafter referred to as projection data) acquired at positions that are temporally opposite with the target image reconstruction position as the center is used (for example, Patent Document 1). Specifically, the two PAR images constituting a pair of PAR images are reconstructed from data projected from a direction in which the subject is inverted by 180 degrees. If there is no movement of the subject while the scanner moves 180 degrees, they will be the same image, but there are changes due to heartbeat and breathing movements during that time. In motion correction reconstruction, the amount of change in each pixel of such PAR images is calculated as motion information, and correction based on the motion information is performed on each projection data to reconstruct an image.

[0005] In cardiac imaging targeting the heart, to minimize the influence of heartbeats, electrocardiogram-synchronized imaging is performed to align the target image reconstruction position with a specific phase of the cardiac cycle. Therefore, the motion information obtained from the PAR image is also information corresponding to a specific phase. On the other hand, in chest imaging without electrocardiogram synchronization (asynchronous imaging), etc., since the reconstruction center phases are different in the images of each cross-section, there is a problem that the continuity of the images is significantly reduced, particularly in the body axis direction.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0007] In the technique described in Patent Document 1, as a motion estimation model for obtaining motion information using the PAR image, a free-form deformation model based on a 4D B-spline function that takes into account the time information at the time of PAR image acquisition is used, and by using a plurality of pairs of PAR images, deterioration of image continuity due to misalignment of the reconstruction center phases is prevented. However, in this technique, since a plurality of pairs of PAR images are used for the reconstruction of a single cross-section, there is a problem that the computational cost is large. Also, in this technique, the continuity of the images in the body axis direction is not considered.

[0008] Also, in asynchronous imaging, usually, the table speed is faster than that in electrocardiogram-synchronized imaging, resulting in a sparse scan, and the data available for creating the PAR image is reduced. Therefore, there is a problem that the motion correction accuracy is lowered.

[0009] Furthermore, when reconstructing images from data continuously acquired in the body axis direction, the image reconstruction conditions such as the slice thickness and FOV may be changed depending on the imaging position. However, in the conventional motion correction reconstruction technique, the motion correction accuracy greatly depends on the image reconstruction conditions.

[0010] The present invention aims to maintain the continuity of images in the body axis direction and enable optimal motion correction reconstruction regardless of image reconstruction conditions in imaging using an X-ray CT apparatus.

Means for Solving the Problems

[0011] The present invention introduces a motion estimation model for acquiring motion information, which is a motion estimation model that can be independently controlled for the continuity between the spatial domain and the temporal domain respectively. By independently controlling the continuity of the spatial domain and the continuity of the temporal domain, it is possible to maintain good image continuity within one cross-section and image continuity between slices in the body axis direction. Further, the present invention is configured such that the motion estimation model or the calculation method using the same can be dynamically adjusted in response to changes in image reconstruction conditions.

[0012] That is, the first aspect of the present invention is the following X-ray CT apparatus. A scanner equipped with an X-ray source and an X-ray detector that rotate around a subject, and a moving mechanism that relatively moves the position of the subject of the scanner in the body axis direction with respect to the subject, an imaging unit that acquires transmission X-ray data (hereinafter referred to as projection data) with different angles and positions in the body axis direction with respect to the subject, and an arithmetic unit that generates a tomographic image of the subject using the projection data acquired by the imaging unit.

[0013] The arithmetic unit includes a partial reconstruction image generation unit that generates a pair of partial reconstruction images at positions facing each other using the projection data, a motion information acquisition unit that applies a motion estimation model to the pair of partial reconstruction images to acquire motion information of the subject during scanning, and a motion correction reconstruction unit that reconstructs a tomographic image using the motion information and the projection data acquired in an angular range of 180 degrees or more. The motion estimation model includes a first regularization term that maintains the spatial continuity of the image and a second regularization term that maintains the temporal continuity, which can be independently adjusted. The motion information acquisition unit automatically changes at least one of the motion estimation model and the calculation method of motion estimation using the same according to the image reconstruction conditions.

[0014] A second aspect of the present invention is a processor for image processing, which generates a pair of partial reconstructed images at positions facing each other using projection data, and applies a motion estimation model including a first regularization term that maintains spatial continuity of the image and a second regularization term that maintains temporal continuity, which can be independently adjusted, to the pair of partial reconstructed images to obtain motion information of a subject during scanning, and reconstructs a tomographic image using the motion information and projection data acquired in an angular range of 180 degrees or more.

Advantages of the Invention

[0015] According to the present invention, by providing a motion estimation model including a first regularization term that maintains spatial continuity of the image and a second regularization term that maintains temporal continuity, which can be independently adjusted, it is possible to perform adjustments to maintain spatial and temporal continuity of the image in response to set image reconstruction conditions or changes thereto, thereby improving the accuracy of motion estimation. Further, according to the present invention, the motion estimation process using the motion estimation model can be dynamically changed, and even if there is a change in the image reconstruction conditions during image reconstruction, particularly regarding the continuity of the image in the body axis direction, deformation due to excessive motion correction can be prevented, and a diagnostic image can be provided.

Brief Description of the Drawings

[0016]

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Embodiments for Carrying Out the Invention

[0017] Hereinafter, embodiments of the X-ray CT apparatus of the present invention and a processor mainly responsible for image processing will be described. In the present embodiment, the processor means a general-purpose computer equipped with a general-purpose CPU (Central Processing Unit) or GPU (Graphic Processing Unit) and a memory, and hardware including programmable ICs such as ASIC (Application Specific Integrated Circuit), FAGA (Field Programable Gate Array), and CPLD (Complex Programmable Logic Device). Any one or a combination of these is collectively referred to as a processor.

[0018] First, the overall configuration of the X-ray CT apparatus to which the present invention is applied will be described.

[0019] As shown in FIG. 1, the X-ray CT apparatus 1 includes an imaging unit 10 having a gantry 100 and a couch device 101 for taking tomographic images and fluoroscopic images of a subject 3, and an operation unit 20 for operating and controlling the imaging unit 10.

[0020] As shown in FIG. 2, the gantry 100 is provided with an X-ray generating device 102 that generates X-rays to irradiate the subject 3, a collimator device 104 that narrows the X-ray beam generated from the X-ray generating device 102, an X-ray detecting device 103 that detects the X-rays transmitted through the subject, a scanner 108 on which they are mounted, a high-voltage generating device 105 that applies a high voltage to the X-ray generating device 102, a data collection device 106 that collects the transmitted X-ray data (referred to as projection data) obtained from the X-ray detecting device 103, and a driving device 107 that rotates the scanner around the subject 3. The X-ray generating device 102 includes an X-ray tube (not shown), and a predetermined tube current flows through the X-ray tube to irradiate the subject 3 with a predetermined amount of X-rays.

[0021] The operation unit 20 includes a processor 200 that functions as a central control device for controlling each device built in the gantry, and an input / output device 210 that functions as a user interface for communication between the user and the processor 200. An arithmetic unit 30 that performs various operations such as image reconstruction on the projection data collected by the data collection device 106 is mounted in the processor 200. However, it is also possible that a processor that is independent of the X-ray CT apparatus and processes data from the X-ray CT apparatus functions as the arithmetic unit 30, and such an independent processor is also included in the present invention. The functions of the processor 200 are realized by the processor 200 reading and executing a program that describes an arithmetic algorithm and a control processing procedure. However, some of the operations and processes performed by the arithmetic unit 30 can also be performed using a PLD (programmable logic device) such as an ASIC or an FPGA.

[0022] The input / output device 210 includes an input device 212 for the operator to input imaging conditions and the like, a display device 211 for displaying data such as imaging images and a GUI, and a storage device 213 for storing data necessary for imaging such as programs and device parameters.

[0023] The calculation unit 30 includes an image reconstruction unit 310 that performs back-projection processing on the projection data obtained by the data acquisition device 106 to create a tomographic image, and a motion information acquisition unit 330 that detects the movement of the subject during scanning. Further, as shown in FIG. 3, in addition to the function of creating a tomographic image using transmission X-ray data in an angular range of 180 degrees or more, the image reconstruction unit 310 has a function (PAR image generation unit 311) of creating a partial reconstruction image (PAR image) using transmission X-ray data in an angular range of less than 180 degrees, and a function (motion correction reconstruction unit 312) of performing motion correction reconstruction using the motion information obtained by the motion information acquisition unit 303. The motion information acquisition unit 330 has a function (motion vector calculation unit 331) of calculating a motion vector (also referred to as a displacement vector) representing the movement of the subject using a pair of PAR images created by the PAR image generation unit 311, that is, PAR images with different projection data acquisition angular ranges, and a function (motion estimation model adjustment unit 332) of optimizing the motion estimation model according to the reconstruction conditions.

[0024] The processor 200 operates as a processor that realizes the functions of the above-described calculation unit 30, and controls the imaging unit 10 (X-ray generator 102, X-ray detector 103, high-voltage generator 105, collimator device 104, bed device 101, drive device 107, data acquisition device 106), the input / output device 210, and the calculation unit 30 according to an operation instruction from an operator via the input device 212. Under the control of the processor 200, these units operate, and reconstruction of CT images, display and storage of the reconstructed CT images, etc. are performed.

[0025] The outline of the process when performing imaging on a moving part and performing motion correction in the X-ray CT apparatus having the above configuration will be described with reference to FIG. 4.

[0026] <s1> Place the object 3 on the bed device 101, perform positioning photography, set the shooting range (the area in the body axis direction of the object), and then the imaging unit 10 starts scanning the shooting range as the scanner 108 rotates. The data collection device 106 collects the projection data obtained by the X-ray detection device 103 at each rotation angle and sends it to the processor 200.

[0027] <s2> The user sets the image reconstruction conditions for the projection data collected in the imaging step S1. The image reconstruction conditions include, for example, conditions such as the type and parameters of the reconstruction filter used for reconstruction, the image reconstruction interval, the field of view (FOV), and the application or non-application of the motion correction function.

[0028] <s3> The calculation unit 30 generates a tomographic image according to the image reconstruction conditions set by the user using the projection data collected by the data collection device 106. When the motion correction function is not applied, image reconstruction is performed by a method such as filtered backprojection according to the image reconstruction conditions set by the image reconstruction unit 310. On the other hand, when the application of motion correction is selected as the image reconstruction condition, the PAR image generation unit 311 generates PAR images at positions symmetric to each other with respect to the target reconstruction center.

[0029] <s4> The motion information acquisition unit 330 performs a process of estimating the motion of the subject by non-rigid registration using the PAR image, that is, acquires motion information. The motion estimation (non-rigid registration) by the motion information acquisition unit 330 is mainly an operation using a motion estimation model (B-spline function) performed by the motion vector calculation unit 331, and as motion information, a MVF (Motion Vector Field) in which the vectors of the motion of each pixel (control point) are mapped is obtained.

[0030] The motion information acquisition unit 330 dynamically adjusts the motion estimation model, parameters, etc. or the calculation method used in this operation so as to maintain the spatial and temporal image continuity between images in correspondence with the image reconstruction conditions at the time of applying it (processing of the motion estimation model adjustment unit 332). The adjustment of the motion estimation model will be described in detail in the embodiments described later.

[0031] <s5> The motion correction reconstruction unit 312 performs image reconstruction of the projection data obtained within a range of 180 degrees or more using the motion information (MVF) acquired by the motion information acquisition unit 330. The method of motion correction image reconstruction is the same as known methods. As shown in FIG. 5, the motion correction reconstruction unit 312 estimates the magnitude and direction of the movement of the subject at the time of acquisition of each projection data 500 used for image reconstruction from the motion information (MVF) 520 obtained from the PAR image pair 510, and while correcting the image at the target reconstruction position using the estimation result, performs filtered backprojection to reconstruct the tomographic image 530.

[0032] The reconstruction of the tomographic image (the above steps S3 to S5) is executed at the image reconstruction interval set as the image reconstruction condition, and a plurality of 2D tomographic image data or 3D tomographic image data are obtained along the body axis direction. The image reconstruction unit 310 causes the input / output device 210 (display device 211) to display the tomographic image data as an image in a predetermined display format.

[0033] Based on the configuration and outline of the processing of the X-ray CT apparatus described above, embodiments of the motion correction reconstruction performed by the processor 200, particularly the adjustment of the motion correction model, will be described below. In the following embodiments, as an example, the case of photographing a plurality of cross-sectional images along the body axis direction asynchronously for a moving part such as the chest will be described.

[0034] <Embodiment 1> This embodiment is characterized in that, as a motion estimation model, a free-form deformation model (FFD) based on a 3D B-spline function is used, and a change considering image continuity is added. The image continuity independently introduces information in the spatial domain and information in the time domain, and aims at the continuity of the images not only within the tomographic image but also along the body axis direction.

[0035] Hereinafter, the processing of this embodiment, particularly the processing of motion estimation (S3 and S4 in FIG. 4), will be mainly described. In the following description, refer to FIG. 4 as necessary.

[0036] <S1, S2> The imaging unit 10 scans the imaging range to collect transmission X-ray data at each angle of the scanner. It receives the setting of image reconstruction conditions by the user via the input / output device 210.

[0037] <Processing of the PAR image generation unit: S3> When the application of the motion correction function is selected as the image reconstruction condition, the PAR image generation unit 311 generates two partial reconstruction images (PAR images) from the projection data collected by the data collection device 106 by the filtered back-projection method. The two PAR images are partial images generated using projection data within an angle range of less than 180 degrees obtained at positions facing each other with the target reconstruction image position as the center. The target reconstruction image position is the position where the scanner is at a predetermined rotation angle (for example, the 0-degree position where the X-ray source is directly above the subject), which can be set by the user when setting the image reconstruction conditions or the default value can be changed.

[0038] The relationship among the rotation angle of the scanner, the projection data, and the PAR image pair is shown in FIG. 6. The projection data 500 in the center of the drawing is the projection data collected in an angle range 500A of 180 degrees or more of the scanner. Among this projection data 500, by back-projecting the projection data 501 and 502 collected in the same angle ranges 501A and 502 facing the image reconstruction center position 500C, partial images 511 and 512 as shown on the right side of FIG. 5 are obtained. Since these use projection data within an angle range of less than 180 degrees, they are not complete cross-sectional images, but each pixel has position information that can estimate motion information.

[0039] Note that the PAR image may be a 3D image instead of a 2D image. When the PAR image generation unit 311 takes a plurality of cross-sections along the body axis direction, it generates a PAR image for each cross-section.

[0040] <Processing of the motion vector calculation unit: S4> Using the pair of PAR images generated by the PAR image generation unit 311, the motion information acquisition unit 330 estimates the motion of the subject while the scanner moves from the position 501A to the position 502A. Prior to motion estimation, filtering for noise reduction may be performed on the two PAR images as necessary. By performing the noise reduction process, the accuracy of the subsequent motion estimation process can be improved. As the filter, a smoothing filter such as a bilateral filter can be used, and by adjusting its parameters, the degree of noise reduction can be adjusted. When the projection data (511, 512 in FIG. 6) of the two PAR images are data at symmetric positions centered on the rotation angle 0 degrees of the scanner, usually, the tube current conditions are the same and the degree of noise is also the same. However, when the tube current conditions are different, the parameters of the filters applied to each may be made different according to the magnitude of the tube current at the time of acquiring the projection data.

[0041] In motion estimation, non-rigid registration is performed between the pixel of interest in the partial image 511 and the pixel of the partial image 512 corresponding to the pixel of interest, and a motion vector field (MVF) between the images is calculated. Non-rigid registration is a process of calculating the MVF using a motion estimation model (function). In this embodiment, as the motion estimation model, a free-form deformation (FFD) model based on a 3D B-spline function is used. The FFD model based on the 3D B-spline function is represented by, for example, the following equation (1).

[0042]

Equation

[0043] The motion estimation process results in calculating the control point parameter Θ (control point parameter) of the motion estimation model. The control point parameter Θ can be calculated by minimizing the SSD (root mean square error) dissimilarity (equation (2)) between the PAR image at the reference time and the PAR image at the angle opposite to the angle at the reference time.

[0044]

number

[0045] Since the convergence calculation for this minimization is an ill-posed problem with many transformation parameters, a regularization term is introduced to efficiently and robustly solve the problem. The regularization term is introduced as a term that penalizes differences in control point parameters. In this embodiment, a spatial domain regularization term (first regularization term) and a time domain regularization term (second regularization term) are introduced as independent regularization terms, as shown in Equation (3) and Equation (4). Equation (3) is the regularization term R1(Θ) that penalizes differences in spatially adjacent control point parameters and is a regularization term for suppressing excessive deformation within the image of interest. Equation (4) is the regularization term R2(Θ) that penalizes differences in temporally adjacent control point parameters and is a term for suppressing excessive deformation from the image of interest and adjacent images. Temporally adjacent images refer to the image of interest and its preceding and succeeding images (forward and backward in time, i.e., forward and backward in the body axis direction).

[0046]

number

[0047]

number

[0048] Using these regularization terms, the cost function of the control point parameters is finally expressed by the following equation (5).

[0049]

Number

[0050] By introducing such two regularization terms, the motion vector calculation unit 331 can stably solve the optimal solution that minimizes Equation (2), and the motion vector Θ is obtained.

[0051] The concept of the motion estimation model with regularization terms introduced is shown in FIG. 7. As shown in the figure, when reconstructing a plurality of tomographic images, the (t - 1)-th image 701, the t-th image 702, and the (t + 1)-th image 703 at a predetermined image reconstruction interval in the body axis direction, when taking the central point of the t-th image 702 as the control point of interest, in this motion estimation model, the parameters of the control point of interest are determined by taking into account the information of the control points adjacent within the image 702 (spatial adjacency information) and the information of the control points of the images 701 and 703 corresponding to the control point of interest of the image 702 (temporal adjacency information).

[0052] Therefore, the continuity of the image depends on the interval between control points within the image and the interval between control points between adjacent images. It also depends on the magnitudes of the weights of the two regularization terms described above. The motion estimation model of the present embodiment is characterized in that the control points and the weights of the regularization terms are provided so as to be adjustable according to image reconstruction conditions such as the FOV and the image reconstruction interval, whereby the continuity of the image can be optimized.

[0053] Specific examples of the adjustment will be described in the embodiments described later. For example, in the adjustment of control points, the number of pixels determining the intervals between control points and the positions (coordinates) on the image are changed according to the image reconstruction conditions (e.g., FOV). The weights of the respective regularization terms are, for example, set to predetermined values by default in advance for standard image reconstruction conditions (FOV and image reconstruction interval), and when the FOV and image reconstruction interval set by the user are different from the standard FOV or image reconstruction, the weight λ1 of the regularization term R1(Θ) in the spatial domain or the weight λ2 of the regularization term R2(Θ) in the temporal domain is adjusted. The adjustment of the weights according to the image reconstruction conditions can be performed not only at the time of user setting but also dynamically in response to changes in the image reconstruction conditions during imaging.

[0054] <Motion correction reconstruction process: S5> Using the motion estimation result obtained by using the above-described motion model, the motion correction reconstruction unit 312 performs image reconstruction. As shown in FIG. 5, in the motion correction reconstruction, the motion information acquisition unit 330 estimates the magnitude and direction of the motion of the subject at the time of acquisition of each projection data used for image reconstruction from the MVF 520 calculated using the PAR image 510, and performs filtered backprojection while correcting the image at the target reconstruction position using the estimation result to reconstruct the tomographic image 530.

[0055] As described above, according to the X-ray CT apparatus of the present embodiment, by introducing regularization terms in the spatial domain and the temporal domain that can be independently adjusted as the motion estimation model when the arithmetic unit that performs motion correction reconstruction performs motion estimation processing, the frequency of occurrence of unnatural deformations can be reduced, and in particular, a reconstructed image in which the continuity of the image is maintained in the body axis direction can be provided.

[0056] Further, according to the present embodiment, since the motion is estimated using only a pair of PAR images for one cross section, the amount of calculation is reduced, and the processing time and the memory usage amount can be reduced.

[0057] Next, a specific embodiment of adjusting a motion estimation model according to image reconstruction conditions will be described based on the above-mentioned embodiment 1. The adjustment of the motion estimation model includes adjusting the formula for calculating the control parameters (displacement vectors) between images described above, specifically adjusting the weights of the two regularization terms (spatial domain regularization term and time domain regularization term) in the calculation formula, adjusting the control points, adjusting the calculation method, etc., and the adjustment is performed in a manner according to the image reconstruction conditions.

[0058] <Embodiment 2> This embodiment describes adjustment of a motion estimation model according to the FOV, which is one of the image reconstruction conditions. The processing flow is the same as the flow shown in Fig. 4, but when the image reconstruction conditions are set in S2 of the flow in Fig. 4, the motion estimation model adjustment unit 332 automatically adjusts the number of pixels between control points (the number of pixels between a control point of interest and the next control point) based on the set FOV.

[0059] Specifically, the motion estimation model adjustment unit 332 adjusts the FOV (FOV adj ) is the reference FOV (FOV base ), if different from FOV base Number of pixels between control points (INT base ) as the reference point, and the control point interval (mm) is base The number of pixels between control points (INT) is set to be equal to the control point interval (mm) of adj ) is adjusted by setting the number of pixels between the control points calculated using the following formula. adj Number of pixels between control points (INT adj )

[0060]

number

[0061] As an example, the standard FOV base The set FOV is 300mm. adj The case where the FOV is 1 / 3 of 300 mm (100 mm) is shown in Figure 8. base For an image with a 300mm control point interval of 25mm (INT base : If it is set to 42 pixels, the FOV in the right figure adj In an image with = 100 mm, the number of pixels at the same control point interval becomes three times (126). By the adjustment according to the above-described formula (6), it is possible to prevent excessive deformation caused by the control point interval on the actual image becoming too small. Also, without unnecessarily increasing the number of control points, motion estimation equivalent to that in the reference FOV base becomes possible. In the example of FIG. 8, it is set to a size that covers the subject range scanned in the reference FOV base However, the reference FOV base may be set to different values for each imaging site. For example, for the chest, set FOV = 300 mm, for the heart, set FOV = 150 mm, etc. as reference values. In such a case, the FOV adj set later may be larger than the reference FOV base but according to formula (6), the number of pixels between control points can be adjusted to be smaller so as to keep the control point interval (mm) the same.

[0062] According to the present embodiment, even when a FOV different from the reference FOV is set by the user, by adjusting the number of pixels between control points according to the FOV, it is possible to prevent the degree of deformation from changing depending on the FOV, and to eliminate excessive deformation and insufficient deformation. That is, the accuracy of motion estimation can be improved and the effectiveness of motion correction reconstruction can be improved.

[0063] In the above description, the case where the image reconstruction conditions set by the user in S1 are different from the reference FOV set as the default conditions of the image reconstruction conditions has been described. However, for example, when viewing images of both lungs with a large FOV (300 mm) and a lesion such as a nodule is found, and the image is enlarged to FOV 150 mm for enlarged image reconstruction, it is also applicable when there is a change in user settings during image reconstruction. In such a case, by dynamically changing the content of the motion estimation process along with the change in the image reconstruction conditions, the continuity with the previous motion estimation process can be maintained, and excessive deformation and the like associated with the condition change can be prevented.

[0064] <Modification Example 1 of Embodiment 2> In the above Embodiment 2, the number of pixels between control points during motion estimation was adjusted according to the image reconstruction condition (FOV) set by the user. However, in this modification example, an adjustment is made to fix the position of the control points on the image without depending on the FOV. That is, even when the FOV is changed, the coordinates of the control points are controlled so that the position of the control points with respect to the center of the image does not change. In this example, a case where the FOV is changed to be smaller during image reconstruction with respect to the initially set FOV will be described as an example.

[0065] As shown in FIG. 9, when the coordinates of the control point of interest at a certain point in the motion estimation process are (pxbf, pybf) and the reconstruction center coordinates are (cxbf, cybf), and assuming that the FOV (FOVbf) at that time is changed to FOVaf as shown in the right diagram of FIG. 9, the motion estimation model adjustment unit 332 receives this change and determines the coordinates of the control point of interest for the next reconstructed image according to the following equation.

Equation

[0066] By this process, the coordinates of each control point are always fixed, that is, the coordinates on the image are the same even when the FOV of the reconstruction is changed. Therefore, fluctuations in the motion estimation accuracy due to changes in the FOV are prevented, and motion estimation with a certain accuracy can be realized.

[0067] <Modification Example 2 of Embodiment 2> In the above Embodiment 2 and Modification Example 1, the number of pixels between control points or the coordinates of the control points during motion estimation were adjusted according to the FOV. However, instead of adjusting the control points, the weight λ1 of the regularization term R1(Θ) in the spatial domain in the above equation (5) for calculating the displacement vector may be changed (adjustment of the calculation formula for motion estimation). For example, for λ1 set by default for the reference FOV base when a FOV smaller than the reference is set, λ1 > λ1 base Set the weight λ1 that results in [a certain value]. By increasing the weight of the regularization term in the spatial domain, the penalty for excessive changes (motion estimation) in the spatial region caused by reducing the FOV can be increased, and the accuracy of motion estimation can be improved. Conversely, when a FOV larger than the reference FOV is set, λ1 < λ1 base Set the weight λ1 that results in [a certain value]. Also in this case, overlooking fine motions caused by an increase in the FOV can be prevented, and the accuracy of motion estimation can be improved.

[0068] Note that the method of this Modification Example 2 can also be used in combination with the method of Embodiment 2, i.e., adjusting the number of pixels between control points, or the method of Modification Example 1, i.e., fixing the control point coordinates.

[0069] According to this modification example, similar to Embodiment 2 and its Modification Example 1, excessive motion estimation and a decrease in the accuracy of motion estimation associated with changes in the FOV can be prevented, and the accuracy of motion estimation can be improved.

[0070] <Embodiment 3> In this embodiment as well, it is the same to use the same motion estimation model as in Embodiments 1 and 2. However, in Embodiment 2 and its modification examples, the control points of the motion estimation model or the weights of the regularization terms were adjusted along with the change in the FOV. In this embodiment, the control parameter calculation method between images is dynamically changed so as not to be affected by the change in the image reconstruction interval.

[0071] When reconstructing tomographic images at a predetermined image reconstruction interval in the body axis direction, the motion estimation process is to determine the control point parameters of the image adjacent to the reference image from the reference image. The image reconstruction intervals of images 701 to 703 shown in FIG. 7 are set as image reconstruction conditions. However, when the image reconstruction interval is changed, the control point interval in the body axis direction will be different.

[0072] In this embodiment, the control parameter calculation method is adjusted so that even if the image reconstruction interval is changed, it is equivalent to performing motion estimation at a constant control point interval. For this reason, in this embodiment, it is assumed that there are virtual tomographic images between adjacent images, and the motion estimation between the virtual tomographic images is calculated. That is, for the virtual tomographic images, the temporary control point parameters are sequentially calculated, and when the position of the virtual tomographic image reaches the position of the adjacent tomographic image, the temporary control point parameters calculated for the virtual tomographic image are used as the control point parameters of the adjacent tomographic image.

[0073] Hereinafter, the processing of this embodiment will be specifically described with reference to FIGS. 10 and 11. FIG. 10 is a diagram showing the processing of this embodiment. As shown in the figure, in this embodiment, first, the control point positions are automatically set (S21), and then a PAR image is generated for the virtual tomographic image (S22).

[0074] For the automatic setting of the control point positions, for the control points in the tomographic image, according to the method of Embodiment 2 or its Modification Example 1, the control point interval (interval in terms of the number of pixels) or the coordinates of the control points are set in accordance with the FOV set as the image reconstruction condition. That is, when the FOV is smaller than the preset FOV, the number of pixels between the control points is changed so that the distance between the control points in the real space (mm) is constant (the method of Embodiment 2). Alternatively, control is performed so that the position of the control point with respect to the image center in the image after the FOV change is the same as that before the FOV change (the method of Modification Example 1).

[0075] Regarding the body axis direction, as shown in FIG. 11, a virtual tomographic image 7011 is assumed at a position with a predetermined interval d with respect to the tomographic image 701 of interest. This interval d is a value smaller than the set image reconstruction interval D, and this interval d becomes the control point interval in the body axis direction in the present embodiment. For example, when the standard image reconstruction interval D is 5 mm, the interval d is set to a fraction (e.g., 1 / 10) of it, and it is also set to a value that can cope even when a small image reconstruction interval such as 0.625 mm is set for more detailed diagnosis. This value d may be set to a predetermined value in advance, or may be set as a ratio to the set image reconstruction interval. It may also be a configuration set by the user.

[0076] When the position of the virtual tomographic image 7011 is determined, the PAR image generation unit 311 reconstructs a PAR image from a pair of projection data obtained within a predetermined angular range with respect to the reconstruction center position of the virtual tomographic image.

[0077] Next, the motion information acquisition unit 330 calculates control point parameters for the virtual tomographic image using the motion estimation model (Equation (5)) using the PAR image, and acquires motion information (S23). The calculation unit 30 sequentially calculates control point parameters for the virtual tomographic images 7012... while changing the virtual tomographic image position in the body axis direction until reaching the image reconstruction position of the adjacent tomographic image 702, for the PAR image generation (S22) and motion information acquisition (S23) described above. When the position of the virtual tomographic image reaches the image reconstruction position (S4), the control point parameters calculated for that virtual tomographic image are used as the control point parameters of the adjacent image.

[0078] The motion correction reconstruction unit 312 reconstructs the adjacent tomographic image 702 using the MVF composed of these control point parameters.

[0079] Thereafter, similarly, the processes of S22 to S24 are performed until the image reconstruction condition (image reconstruction interval) is changed.

[0080] According to this embodiment, virtual tomographic images are assumed at regular intervals between adjacent images, and temporary control point parameters are calculated sequentially, so that results equivalent to those obtained when control point parameters are calculated at a constant image reconstruction interval can be obtained regardless of the image reconstruction conditions, enabling stable motion estimation. Furthermore, even when the image reconstruction interval is large, motion estimation can be performed while fully considering the continuity in the body axis direction.

[0081] According to the method of this embodiment, a PAR image is generated for each virtual tomographic image, which increases the amount of calculation, but since image reconstruction of the virtual tomographic image itself is not performed and only the PAR image is generated, excessive computational load can be avoided.

[0082] In this embodiment, it is also possible to use the method of embodiment 1 in combination, that is, the method of adjusting the weight λ2 of the time domain regularization term of the motion estimation model (Equation (5)) according to changes in the image reconstruction interval.

[0083] The above describes an embodiment of the processing of the calculation unit 30 (processor) of the X-ray CT apparatus of the present invention, but the processing described as each embodiment and modification can be appropriately combined as long as there is no technical contradiction, and such combinations are also included in the present invention. Furthermore, the present invention is effectively applied to asynchronous imaging, but can also be applied to synchronous imaging in the same way.

[0084] Furthermore, the present invention also encompasses modifications to or additions to known configurations included in the X-ray CT apparatus and processor of the above-described embodiments, provided that the gist of the present invention is not changed. [Explanation of symbols]

[0085] 1: X-ray CT device, 10: imaging unit, 30: calculation unit, 200: processor, 310: image reconstruction unit, 311: PAR image generation unit, 312: motion correction reconstruction unit, 330: motion information acquisition unit, 331: motion vector calculation unit, 332: motion estimation model adjustment unit

Claims

1. A scanner equipped with an X-ray source and an X-ray detector that rotate around the subject, and a moving mechanism that relatively moves the position of the scanner in the body axis direction of the subject with respect to the subject, the imaging unit acquires transmission X-ray data with different angles and positions in the body axis direction with respect to the subject, and an arithmetic unit that generates a tomographic image of the subject using the transmission X-ray data acquired by the imaging unit, The arithmetic unit is, A partial reconstruction image generation unit that generates a pair of partial reconstruction images at positions facing each other using the transmission X-ray data, a motion information acquisition unit that applies a motion estimation model to the pair of partial reconstruction images to acquire motion information of the subject during scanning, and a motion correction reconstruction unit that reconstructs a tomographic image using the motion information and transmission X-ray data acquired in an angular range of 180 degrees or more, The motion estimation model includes a first regularization term that can be independently adjusted to maintain spatial continuity of the image and a second regularization term that maintains temporal continuity, The motion information acquisition unit automatically changes at least one of the motion estimation model and the calculation method of motion estimation using the same according to the image reconstruction conditions. An X-ray CT apparatus characterized by this.

2. The X-ray CT apparatus according to Claim 1, The motion information acquisition unit includes a motion estimation model adjustment unit that adjusts the motion estimation model according to the image reconstruction conditions. An X-ray CT apparatus characterized by this.

3. The X-ray CT apparatus according to Claim 2, The motion estimation model adjustment unit adjusts at least one of the weight of the first regularization term and the weight of the second regularization term according to the image reconstruction conditions. An X-ray CT apparatus characterized by this.

4. The X-ray CT apparatus according to Claim 3, The motion estimation model adjustment unit adjusts the weight of the first regularization term according to the FOV set as the image reconstruction condition. An X-ray CT apparatus characterized by this.

5. The X-ray CT apparatus according to Claim 3, The motion estimation model adjustment unit adjusts the weight of the second regularization term according to the image reconstruction interval set as the image reconstruction condition. An X-ray CT apparatus characterized by this.

6. The X-ray CT apparatus according to Claim 3, The motion estimation model adjustment unit adjusts the control points of the motion estimation model together with the adjustment of the weight of the regularization term according to the image reconstruction conditions. An X-ray CT apparatus characterized by this.

7. The X-ray CT apparatus according to Claim 2, The movement estimation model adjustment unit adjusts control points of the movement estimation model according to the FOV set as the image reconstruction condition. An X-ray CT apparatus characterized by this.

8. The X-ray CT apparatus according to claim 7, wherein the movement estimation model adjustment unit adjusts the number of pixels between adjacent control points of the movement estimation model according to the FOV. An X-ray CT apparatus characterized by this.

9. The X-ray CT apparatus according to claim 7, wherein the movement estimation model adjustment unit adjusts the coordinates of the control points so that the position of the control points with respect to the image center in the image space becomes constant according to the FOV.An X-ray CT apparatus characterized by this.

10. The X-ray CT apparatus according to claim 1, wherein the movement information acquisition unit dynamically adjusts the calculation method according to the image reconstruction interval set as the image reconstruction condition in the calculation of the control point parameters using the movement estimation model. An X-ray CT apparatus characterized by this.

11. The X-ray CT apparatus according to claim 10, wherein the movement information acquisition unit sets one or more virtual cross-sections at regular intervals between the cross-section of interest and the cross-section adjacent to the subject's body axis direction, and calculates the control point parameters using the partial reconstruction images in the virtual cross-sections until the virtual cross-section position reaches the adjacent cross-section position, and the control point parameters calculated in the virtual cross-section when the virtual cross-section position reaches the adjacent cross-section position are used as the control point parameters of the adjacent cross-section. An X-ray CT apparatus characterized by this.

12. A processor for processing the transmission X-ray data acquired by an X-ray CT apparatus, generating a pair of partial reconstruction images at positions facing each other using the transmission X-ray data, applying a movement estimation model including a first regularization term that maintains the spatial continuity of the image and a second regularization term that maintains the temporal continuity, which can be independently adjusted, to the pair of partial reconstruction images to obtain the movement information of the subject during scanning, and reconstructing a tomographic image using the movement information and the transmission X-ray data acquired in an angular range of 180 degrees or more. A processor for image processing, characterized by this.

13. The processor according to claim 12, wherein the acquisition of the movement information dynamically changes at least one of the movement estimation model used for the acquisition of the movement information and the calculation method using the movement estimation model according to the image reconstruction conditions set at the time of motion correction reconstruction. A processor characterized by this.

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