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

Abstract

To make reconstruction processing accompanied by motion compensation efficient.SOLUTION: A medical image processing device includes: a projection data reception unit that receives projection data obtained by CT imaging of a subject; a first image reconstruction unit that generates a reconstructed image of the subject based on the projection data without performing motion compensation; a parameter determination unit that specifies a blood vessel in the reconstructed image and determines parameters based on a feature of the blood vessel; a motion estimation unit that estimates a range of motion of the blood vessel using the determined parameters; and a second image reconstruction unit that reconstructs a motion-compensated image of the subject based on the projection data and the range of motion of the blood vessel.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The embodiments disclosed in this specification and the drawings relate to a medical image processing device, a medical image processing method, and a program. [Background technology]

[0002] Cardiac X-ray computed tomography (CT) is one of the most challenging areas of medical imaging because the heart is constantly moving and exhibits both regular and irregular motion patterns. Therefore, modern imaging techniques are needed to capture clear cardiac images while the heart and blood vessels are in motion.

[0003] Various compensation methods for restoring image quality have been used to mitigate the effects of cardiac motion artifacts. Most of these methods have two main steps: (1) estimating cardiac motion, and (2) incorporating the estimated motion into the image reconstruction process to address the motion artifacts. The performance of these motion compensation methods is primarily determined by the accuracy of the motion estimation step.

[0004] It would be desirable to enhance current cardiac motion compensation approaches. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] U.S. Patent Application Serial No. 18 / 516,450 Summary of the Invention [Problem to be solved by the invention]

[0006] One of the problems to be solved by the embodiments disclosed in this specification and the drawings is to improve the efficiency of reconstruction processing involving motion compensation. However, the problems to be solved by the embodiments disclosed in this specification and the drawings are not limited to the above problem. Problems corresponding to the effects of each configuration shown in the embodiments described below can also be positioned as other problems. [Means for solving the problem]

[0007] The medical image processing device of the embodiment includes a projection data receiving unit that receives projection data obtained by CT imaging of a subject, a first image reconstruction unit that generates a reconstructed image of the subject based on the projection data without performing motion compensation, a parameter determination unit that identifies blood vessels in the reconstructed image and determines parameters based on characteristics of the blood vessels, a motion estimation unit that estimates the range of motion of the blood vessels using the determined parameters, and a second image reconstruction unit that reconstructs a motion-compensated image of the subject based on the projection data and the range of motion of the blood vessels. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram of an exemplary apparatus for performing motion estimation and compensation in a CT imaging system according to an embodiment. [Figure 2] FIG. 2 is a block diagram of an adaptive parameter adjustment circuit according to an embodiment. [Figure 3] FIG. 3 is a flowchart of an exemplary procedure for performing parameter adaptation according to an embodiment. [Figure 4] FIG. 4 is a block diagram of a mask size adaptation circuit according to an embodiment. [Figure 5] FIG. 5 is a diagram illustrating blood vessels identified in a reconstructed cardiac CT image according to an embodiment. [Figure 6A] FIG. 6A is a diagram illustrating an exemplary vascular slice identified in a reconstructed cardiac CT image according to an embodiment. [Figure 6B]FIG. 6B illustrates an exemplary vascular region cropped using a default vascular mask according to an embodiment. [Figure 6C] FIG. 6C illustrates an exemplary vascular motion artifact acquired within a vascular region according to an embodiment. [Figure 6D] FIG. 6D illustrates exemplary vascular motion artifacts obtained based on different CT number thresholds via a thresholding process according to an embodiment. [Figure 6E] FIG. 6E illustrates an exemplary circular vascular mask according to an embodiment. [Figure 7] FIG. 7 is a flowchart of an exemplary procedure for performing mask size adaptation according to an embodiment. [Figure 8] FIG. 8 is a block diagram of a control point number adaptation circuit according to the embodiment. [Figure 9] FIG. 9 is a flowchart of an exemplary procedure for implementing adaptation of the number of control points according to an embodiment. [Figure 10] FIG. 10 is a block diagram of a control point position adaptation circuit according to an embodiment. [Figure 11A] FIG. 11A is a graph illustrating the distribution of entropy values ​​calculated along identified blood vessels according to an embodiment. [Figure 11B] FIG. 11B is a diagram illustrating exemplary locations of control points assigned along an identified vessel according to an embodiment. [Figure 12] FIG. 12 is a flowchart of an exemplary procedure for implementing control point position adaptation according to an embodiment. [Figure 13] FIG. 13 is a schematic block diagram of an exemplary CT imaging system, according to an embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] The following disclosure presents embodiments or examples for implementing various features of the presented subject matter. To simplify the disclosure, specific examples of components and arrangements are described below. Of course, these are merely examples and are not intended to be limiting.

[0010] For example, as described in the embodiments, the order of description of different steps is presented for clarity. Generally, these steps can be performed in any suitable order. Also, although different features, techniques, and configurations in the embodiments may be described in various places in this disclosure, it is intended that each concept can be implemented independently of each other or in combination with each other. Thus, each embodiment can be embodied and viewed in many different ways.

[0011] Furthermore, as used herein, terms such as "a" and "an" generally mean "one or more" unless otherwise specified. That is, various terms in the embodiments can be interpreted as either singular or plural unless otherwise specified.

[0012] Typically, artifacts resulting from vascular motion result in various vascular deformation patterns. Examples of vascular deformation patterns include, but are not limited to, crescent shapes, elongated tails, and horn-like distortions. In the field of cardiac CT imaging, the coronary arteries are the primary region of interest for diagnosing cardiovascular diseases. Motion characteristics, including both magnitude and direction, can vary significantly along the coronary arteries, which creates challenges for motion estimation and compensation.

[0013] Patent document 1 (Attorney Docket No. 546525US) relates to a method and apparatus for integrating motion estimation and motion compensation in a cardiac CT imaging system, rather than performing the two steps independently. In this approach, motion estimation and motion compensation are performed iteratively until a predetermined termination criterion is met. During the iterations, the motion range, which is composed of motion vectors at several control points assigned along the vessel of interest, is constantly updated and optimized based on a cost function. By doing so, the accuracy of motion estimation can be improved during motion compensation, and the efficiency of motion compensation can be improved through the motion estimation process.

[0014] Using the entire reconstructed image in the above optimization process can impose an excessively high computational load. To reduce the computational load and accelerate the procedure, it is advantageous to use a vascular mask to narrow the focus to specific vascular regions and crop them from the reconstructed image.

[0015] However, there is no universally applicable set of parameters that can accommodate the wide range of conditions encountered in individual patients, including, for example, variations in vessel length, curvature, the presence of abnormalities, and the details of the image acquisition protocol.

[0016] Therefore, it is desirable to pre-adapt or tune these parameters to achieve accurate motion estimation and compensation. Adaptation can include customizing the size of the vessel region mask around different sections of the vessel of interest, as well as the location and number of control points used in the image non-rigid registration.

[0017] Embodiments are directed to methods and apparatus for adapting parameters involved in motion estimation and compensation, for example, within a CT imaging system. While the embodiments are described in the context of cardiac CT imaging, those skilled in the art will appreciate that the approach is applicable to imaging of other vascular structures without departing from the spirit and scope of the present disclosure.

[0018] 1 shows a block diagram of an exemplary apparatus 100 for performing motion estimation and compensation in a CT imaging system according to an embodiment. The apparatus 100 includes a projection data receiving circuit 110, an image reconstruction (without motion compensation) circuit 120, an adaptive parameter adjustment circuit 130, a motion optimization circuit 140, and an image reconstruction (with motion compensation) circuit 150. For clarity, the iterative motion range optimization described in U.S. Patent Application Publication No. 2010 / 0129998 is shown in FIG. 1 as being implemented by the motion optimization circuit 140. The apparatus 100 is an example of a medical image processing device.

[0019] The projection data receiving circuit 110 receives unprocessed projection data (raw data) obtained from imaging of an imaging target by a CT imaging system and transmits the data to the image reconstruction (without motion compensation) circuit 120 and the image reconstruction (with motion compensation) circuit 150. The projection data receiving circuit 110 is an example of a projection data receiving unit.

[0020] The image reconstruction (without motion compensation) circuit 120 reconstructs the projection data to generate an image of the imaging object and transmits the generated image to the adaptive parameter adjustment circuit 130. The image reconstruction process does not include any motion correction. That is, the image reconstruction (without motion compensation) circuit 120 generates a reconstructed image of the object based on the projection data without performing motion compensation. The image reconstruction (without motion compensation) circuit 120 is an example of a first image reconstruction unit.

[0021] The adaptive parameter adjustment circuit 130 identifies blood vessels in the reconstructed image generated by the image reconstruction (without motion compensation) circuit 120 and determines parameters based on the characteristics of the identified blood vessels. For example, the adaptive parameter adjustment circuit 130 can adjust adaptive parameters involved in motion range optimization and transmit the adjusted parameters to the motion optimization circuit 140. For example, these adaptive parameters include the size of the blood vessel region mask, the number of control points, and the specific positions of these control points. The adaptive parameter adjustment circuit 130 is an example of a parameter determination unit.

[0022] The motion optimization circuit 140 estimates the range of vascular motion using the parameters determined by the adaptive parameter adjustment circuit 130. For example, the motion optimization circuit 140 uses the adapted parameters to optimize the range of vascular motion and transmits it to the image reconstruction (with motion compensation) circuit 150. The motion optimization circuit 140 is an example of a motion estimation unit.

[0023] The image reconstruction (with motion compensation) circuit 150 reconstructs a motion-compensated image of the object based on the projection data received by the projection data receiving circuit 110 and the range of blood vessel motion estimated by the motion optimization circuit 140. For example, the image reconstruction (with motion compensation) circuit 150 reconstructs a motion-compensated image of the imaging object based on the optimized range of motion and outputs it as a final image.

[0024] 2 shows a block diagram of the adaptive parameter adjustment circuit 130 according to an embodiment of the present disclosure. The adaptive parameter adjustment circuit 130 includes a reconstructed image receiving circuit 210, a vessel identification circuit 220, a mask size adaptation circuit 230, a control point number adaptation circuit 240, a control point position adaptation circuit 250, and an adaptive parameter output circuit 260.

[0025] The reconstructed image receiving circuit 210 receives the image of the imaging object from the image reconstruction (without motion compensation) circuit 120 and transmits it to the blood vessel identification circuit 220 .

[0026] The vessel identification circuit 220 identifies specific vessels within the received image and sends it to the mask size adaptation circuit 230, the control point number adaptation circuit 240, and the control point position adaptation circuit 250. This vessel identification can be performed using a variety of methods, including, for example, a neural network-based approach or an image processing-based approach.

[0027] The mask size adaptation circuit 230, the control point number adaptation circuit 240, and the control point position adaptation circuit 250 determine the size of the vessel region mask and the number and position of the control points used during the subsequent motion estimation and compensation procedures. These decisions can be made taking into account the specific characteristics of the imaging subject, such as the length of the vessel of interest and the degree of motion artifact. Further details about the adaptation process are provided below with reference to Figures 4 through 12.

[0028] The adaptive parameter output circuit 260 receives the adapted parameters and outputs them to the motion optimization circuit 140 .

[0029] FIG. 3 shows a flowchart of an exemplary procedure 300 for performing parameter adaptation according to an embodiment. In step S310, an image of an imaging object is received, which can be reconstructed without motion compensation. In step S320, a vessel of interest is identified within the received CT image, i.e., the vessel is identified from an image reconstructed without motion compensation. Steps S330 through S350 correspond to adapting the vessel mask size, the number of control points, and the control point positions, respectively. Finally, in step S360, the adapted or determined mask size and control point positions are output for use in a subsequent motion optimization stage.

[0030] 4 shows a block diagram of the mask size adaptation circuit 230 according to the embodiment. The mask size adaptation circuit 230 includes a vascular slice acquisition circuit 410, a vascular region extraction circuit 420, a maximum motion artifact acquisition circuit 430, a compactness calculation circuit 440, and a mask size determination circuit 450. The mask size adaptation circuit 230 is an example of a mask size adaptation unit.

[0031] The vascular slice acquisition circuit 410 acquires vascular slices contained in the vessels identified in the reconstructed image and sends them to the vascular region extraction circuit 420. As an example, Fig. 5 shows a reconstructed CT image in which a vessel of interest consisting of multiple vascular slices can be identified in the CT image.

[0032] The vascular region extraction circuit 420 trims the vascular region around each of the plurality of vascular slices based on a predetermined vascular mask. In other words, the vascular region extraction circuit 420 extracts the vascular region around each of the plurality of vascular slices. For example, the vascular region extraction circuit 420 uses a uniform default vascular mask to trim the corresponding vascular regions of the acquired vascular slices and transmits them to the maximum motion artifact acquisition circuit 430. This default vascular mask can maintain the same size across different vascular slices and does not require special adjustment for each individual vascular slice.

[0033] The maximum motion artifact acquisition circuit 430 acquires the maximum motion artifact within each of the vascular regions. For example, the maximum motion artifact acquisition circuit 430 acquires the maximum motion artifact within each of the trimmed vascular regions based on a set of predetermined CT value thresholds through a thresholding process. Then, the maximum motion artifact acquisition circuit 430 transmits these acquired maximum motion artifacts to the compactness calculation circuit 440.

[0034] Inherent variations in CT numbers may occur with different patients, varying amounts of motion, or various data acquisition sessions. To ensure that all relevant motion artifacts are included for accurate motion correction, the maximum motion artifact acquisition circuit 430 can perform a thresholding process on the vascular region using CT number thresholds in the range of, for example, 20%, 30%, 40%, 50%, etc. Small and remote segments are removed during the thresholding process to ensure that all and only the vascular slice and its motion artifacts are considered when the maximum motion artifact is acquired. This final maximum motion artifact is then used to calculate a mask size for the vascular slice, as described below.

[0035] Alternatively, the desired threshold can also be manually selected by the operator of the CT imaging system, for example, the operator can make this selection based on their visual assessment and judgment as to whether artifacts such as tails should be included or excluded from the maximum motion artifact.

[0036] Through the thresholding process, unintentional artifacts can be prevented from remaining during the subsequent motion estimation and compensation procedures, thereby ensuring the accuracy of the correction process.

[0037] The compactness calculation circuit 440 calculates a compactness measure for each of the maximum motion artifacts obtained for the extracted vessel region. For example, the compactness measure can be calculated using the following equation (1):

[0038]

number

[0039] In equation (1), P denotes the perimeter of the largest motion artifact, and A denotes the area of ​​the largest motion artifact. In relation to this compactness measure, a value of "1" denotes the highest level of compactness, which corresponds to a perfect circle, while higher values ​​denote less compactness and correspond to shapes that deviate from the circle.

[0040] Based on the compactness measurements calculated for each vascular slice, the mask size determination circuit 450 determines a mask size for the vascular slice. The determined mask size is transmitted to the control point position adaptation circuit 250 and the adaptive parameter output circuit 260. This determination can be made through various methods, one example of which is using a lookup table. In this approach, the lookup table can be constructed by compiling a data set that pairs compactness measurements with their corresponding mask sizes. The appropriate mask size can then be determined by referencing the lookup table using the compactness measurements as a key. Those skilled in the art will also appreciate that the determination can alternatively be achieved, for example, using a trained neural network.

[0041] It should be noted that the compactness measurements provided in this embodiment are merely exemplary. Various other morphological metrics can be used without departing from the spirit and scope of the present disclosure. That is, the compactness calculation circuit 440 may calculate other metrics instead of compactness. Examples of other metrics include, but are not limited to, entropy, circularity, Euclidean spacing, stretchiness, and convexity.

[0042] A representative vessel slice in a reconstructed cardiac CT image is shown in Figure 6A. The location of this vessel slice can be represented by its center of mass. Figure 6B shows an example vessel region cropped from the reconstructed image using a default vessel mask. The size of the default vessel mask can be predetermined to be large enough to cover not only the entire vessel slice but also potential motion artifacts that may occur in various patient cases.

[0043] An exemplary vascular motion artifact obtained after removing small features or objects around the vascular slice is shown in Figure 6C. Through a thresholding process shown in Figure 6D, the largest vascular motion artifacts can be identified and captured. As mentioned above, this process is performed to avoid missing artifacts that should be corrected. Figure 6E shows an exemplary vascular mask with a circular shape and a radius of "r." The center of this circular mask can be aligned with the vascular slice location (represented by the center of mass of the slice).

[0044] FIG. 7 shows a flowchart of an exemplary procedure 700 for performing mask size adaptation according to an embodiment of the present disclosure. In step S710, an identified vessel including multiple vessel slices is received. In step S720, multiple vessel slices included in the identified vessel are obtained. In step S730, a vessel region is extracted in each vessel slice by applying a default vessel mask. In step S740, a maximum motion artifact is obtained for each vessel slice based on a set of predetermined CT number thresholds via a thresholding process. In step S750, a compactness measure is calculated for each vessel slice. In step S760, a mask size is determined for each vessel slice based on the compactness calculated for the slice. In step S770, the determined mask size is output.

[0045] 8 shows a block diagram of a number of control points adaptation circuit 240 according to an embodiment of the present disclosure. The number of control points adaptation circuit 240 includes a vessel length estimation circuit 810, a control point interval acquisition circuit 820, and a number of control points determination circuit 830. The number of control points adaptation circuit 240 is an example of a number of control points adaptation unit.

[0046] The vessel length estimation circuit 810 receives the identified vessels in the reconstructed image, estimates the lengths of the identified vessels, and sends them to the control point number determination circuit 830. For example, the vessel length estimation circuit 810 can trace the vessel from its start point to its end point, and then accumulates the lengths of multiple vessel slices to estimate the entire vessel length.

[0047] The control point spacing acquisition circuit 820 acquires the desired spacing between adjacent control points and sends it to the control point number determination circuit 830. This spacing can be determined empirically and is typically in the range of 15 mm to 20 mm. Alternatively, it can be manually set by the operator of the CT imaging system.

[0048] Based on the received spacing between adjacent control points and the estimated vessel length, the control point number determination circuit 840 determines the number of control points to be used during the motion estimation and compensation process. For example, the number of control points can be easily calculated as the vessel length divided by the spacing between adjacent control points.

[0049] 9 shows a flowchart of an exemplary procedure 900 for performing adaptation of the number of control points according to an embodiment of the present disclosure. In step S910, an identified vessel is received. In step S920, the identified vessel is tracked to estimate its length. In step S930, a desired spacing between adjacent control points is obtained. In step S940, the number of control points is determined based on the vessel length and the control point spacing. In step S950, the determined number of control points is output.

[0050] 10 shows a block diagram of a control point position adaptation circuit 250 according to an embodiment of the present disclosure. The control point position adaptation circuit 250 includes a vascular slice acquisition circuit 1010, a vascular region extraction circuit 1020, an entropy calculation circuit 1030, and a control point assignment circuit 1040. The control point position adaptation circuit 250 is an example of a control point position adaptation unit.

[0051] The vessel slice acquisition circuit 1010 acquires a plurality of vessel slices contained in the identified vessel and transmits them to the vessel region extraction circuit 1020 .

[0052] The vascular region extraction circuit 1020 extracts corresponding vascular regions for the acquired vascular slices using the vascular mask size determined by the mask size adaptation circuit 230. More specifically, the vascular region extraction circuit 1020 extracts corresponding vascular regions around each vascular slice by applying a vascular mask of a size corresponding to the vascular slice. The vascular mask of a size corresponding to the vascular slice is, for example, a vascular mask of a specific mask size corresponding to the vascular slice.

[0053] The entropy calculation circuit 1030 receives the extracted vascular regions for the plurality of vascular slices. The entropy calculation circuit 1030 calculates an entropy value for each of the extracted vascular regions. For example, the entropy calculation circuit 1030 can identify vascular motion artifacts within each of the extracted vascular regions and calculate an entropy value associated with the vascular motion artifacts.

[0054] The entropy calculated for the motion artifact is one example of a motion artifact level. That is, the entropy calculation circuit 1030 calculates the motion artifact level for each vascular region as a motion artifact metric. Those skilled in the art will appreciate that other forms of motion artifact metric can be calculated to characterize or quantify the motion artifact level. Examples of other forms of motion artifact level include, but are not limited to, compactness, circularity score, etc.

[0055] The control point assignment circuit 1040 receives the number of control points determined by the control point number adaptation circuit 240 and the entropy value from the entropy calculation circuit 1030. The control point assignment circuit 1040 utilizes the calculated entropy value to position the determined number of control points along the identified vessel.

[0056] For example, the control point assignment circuit 1040 can determine the locations of the control points based on the distribution of entropy values ​​along the identified blood vessel. That is, the control point assignment circuit 1040 can determine the locations corresponding to each of the control points based on the distribution of the magnitude of the motion artifact metric along the blood vessel. Specifically, the control points can be assigned along the blood vessel, typically at or near peaks in the entropy value curve that indicate high levels of motion.

[0057] For example, the control point assignment circuit 1040 can determine the location of the control points by assigning more control points to identified vessel sections that contain more vessel slices whose entropy values ​​exceed a predetermined threshold compared to sections that contain fewer slices whose entropy values ​​exceed a predetermined threshold.

[0058] For example, control points may not be assigned to vessel slices whose entropy values ​​are below a predetermined threshold.

[0059] FIG. 11A shows a graph depicting the distribution of calculated entropy values ​​along an identified vessel, according to an embodiment of the present disclosure. Typically, higher entropy values ​​are observed in vessel slices that have undergone greater irregular deformation due to motion, while slices that maintain a circular and regular shape exhibit lower calculated entropy values. In FIG. 11A, triangular markers serve as a reference for comparison and indicate the locations of control points that are uniformly distributed along the vessel. Meanwhile, circular markers indicate the assignment of control points based on the distribution of calculated entropy values, e.g., more control points are assigned to vessel sections with higher entropy values. The resulting locations of control points along the vessel are shown in FIG. 11B.

[0060] FIG. 12 shows a flowchart of an exemplary procedure 1200 for performing control point position adaptation according to an embodiment of the present disclosure. In step S1210, an identified blood vessel is received. In step S1220, a number of slices included in the identified blood vessel are derived. In step S1230, a determined mask size is received. In step S1240, a blood vessel region is extracted in each blood vessel slice by applying a blood vessel mask of a specific size corresponding to that slice. In step S1250, an entropy value is calculated for each blood vessel region. In step S1260, a determined number of control points are received. In step S1270, the determined number of control points are assigned along the identified blood vessel based on the calculated entropy value. In step S1280, the control points at the determined positions are output.

[0061] The incorporation of the aforementioned parameter adaptation eliminates the need for manual adjustments when addressing different target vessels (e.g., Right Coronary Artery (RCA) or Left Coronary Artery (LCA)), patients, or different conditions. The use of adaptive parameters during the optimization process not only improves the quality of the motion-compensated images, but also reduces the failure rate of motion correction.

[0062] 13 is a schematic block diagram of a CT apparatus or scanner according to one embodiment of the present disclosure. As shown in FIG. 13 , a radiography gantry 1350 is depicted as viewed from the side and further includes an X-ray tube 1351, an annular frame 1352, and a multi-row or two-dimensional array X-ray detector 1353. The X-ray tube 1351 and the X-ray detector 1353 are mounted on the annular frame 1352 diametrically opposite each other across an object OBJ, and the annular frame 1352 is rotatably supported about a rotation axis RA. A rotation device 1357 rotates the annular frame 1352 at a high speed, such as 0.4 seconds per rotation, while the object OBJ is moved along the axis RA toward or away from the illustrated surface.

[0063] Embodiments of an X-ray CT apparatus according to the present disclosure will be described below with reference to the accompanying drawings. It should be noted that X-ray CT apparatuses include various types of apparatuses, such as a rotating / rotating type apparatus in which both an X-ray tube and an X-ray detector rotate around an object to be examined, and a fixed / rotating type apparatus in which multiple detector elements are arranged in a circular or horizontal pattern and only the X-ray tube rotates around the object to be examined. The present disclosure is applicable to either type. Here, the rotating / rotating type, which is currently the mainstream, will be exemplified.

[0064] The multi-slice X-ray CT apparatus further includes a high-voltage generator 1359, which generates a tube voltage applied to the X-ray tube 1351 through a slip ring 1358 so that the X-ray tube 1351 generates X-rays. The X-rays are emitted toward the object OBJ, and their cross-sectional area is represented by a circle. For example, the X-ray tube 1351 has an average X-ray energy during a first scan that is less than the average X-ray energy during a second scan. Therefore, two or more scans corresponding to different X-ray energies can be acquired. An X-ray detector 1353 is disposed on the opposite side of the object OBJ from the X-ray tube 1351 to detect emitted X-rays that have propagated through the object OBJ. The X-ray detector 1353 further includes individual detector elements or units.

[0065] The CT apparatus further includes other devices that process detection signals from the X-ray detector 1353. A data acquisition circuit or data acquisition system (DAS) 1354 converts the signal output from the X-ray detector 1353 for each channel into a voltage signal, amplifies the signal, and further converts the signal into a digital signal. The X-ray detector 1353 and DAS 1354 are configured to process a predetermined total number of projections per rotation (TPPR).

[0066] The above-mentioned data is transmitted via a non-contact data transmitter 1355 to a pre-processing device 1356 housed in a console outside the radiography gantry 1350. The pre-processing device 1356 performs specific corrections, such as sensitivity corrections, on the raw data. Regardless of whether or not the pre-processing device 1356 corrects the data, the data before reconstruction is collectively referred to as projection data. A memory 1362 stores the resulting data, also called projection data, immediately before reconstruction. The memory 1362, along with a reconstruction device 1364, an input device 1365, and a display 1366, are connected to a system controller 1360 via a data / control bus 1361. The system controller 1360 controls a current regulator 1363, which limits the current to a level sufficient to drive the CT system.

[0067] In any generation CT scanner system, the detectors are rotated and / or fixed relative to the patient. In one implementation, the CT system described above may be a combination of third-generation and fourth-generation geometric systems. In a third-generation system, the X-ray tube 1351 and the X-ray detector 1353 are mounted diametrically on an annular frame 1352 and rotate around the object OBJ as the annular frame 1352 rotates around the rotation axis RA. In a fourth-generation geometric system, the detectors are fixedly mounted around the patient and the X-ray tube rotates around the patient. In an alternative embodiment, the radiography gantry 1350 has multiple detectors arranged on an annular frame 1352 supported by a C-arm and a stand.

[0068] The memory 1362 can store measurements indicative of X-ray exposure at the X-ray detector 1353. Additionally, the memory 1362 can store dedicated programs for performing CT image reconstruction, material decomposition, and motion estimation and motion compensation methods, including those described in the embodiments.

[0069] The reconstruction device 1364 can execute the above-mentioned reference method described in the embodiment. That is, the reconstruction device 1364 is an example of the apparatus 100 shown in Fig. 1. Of course, the apparatus 100 may be an apparatus other than the X-ray CT apparatus shown in Fig. 13. Furthermore, the reconstruction device 1364 can execute pre-reconstruction image processing such as volume rendering processing and image subtraction processing, as necessary.

[0070] Reconstruction pre-processing of the projection data performed by pre-processing device 1356 can include, for example, detector calibration, correcting for detector non-linearities, and polar effects.

[0071] Post-reconstruction processing performed by the reconstruction device 1364 may include image filtering and smoothing, volume rendering, and image subtraction, as needed. The image reconstruction process may be performed using filtered backprojection, an iterative image reconstruction method, or a stochastic image reconstruction method. The reconstruction device 1364 may use memory to store, for example, projection data, reconstructed images, calibration data and parameters, and computer programs.

[0072] The reconfiguration device 1364 may include a CPU (processing circuitry) that may be implemented as discrete logic gates, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other complex programmable logic device (CPLD). FPGA or CPLD implementations may be coded in VDHL, Verilog, or any other hardware description language, and the code may be stored directly in electronic memory within the FPGA or CPLD or in separate electronic memory. Furthermore, the memory 1362 may be a non-volatile memory such as a ROM, EPROM, EEPROM, or FLASH memory. The memory 1362 may be a volatile memory such as static or dynamic RAM, and a processor such as a microcontroller or microprocessor may be provided to manage the electronic memory and the interaction between the FPGA or CPLD and the memory.

[0073] Alternatively, the CPU in the reconstruction device 1364 may execute a computer program including a set of computer-readable instructions that implement the functions described herein, the program being stored in any of the non-transitory electronic memory and / or hard disk drives, CDs, DVDs, flash drives, or any other known storage mediums described above. Furthermore, the computer-readable instructions may be provided as a utility application, a background daemon, or an operating system component, or a combination thereof, and executed in conjunction with a processor such as an Intel Xeon processor or an AMD Opteron processor, and an operating system such as Microsoft 10, UNIX, Solaris, LINUX, Apple's MAC-OS, and other operating systems known to those skilled in the art. Furthermore, the CPU may be implemented as multiple processors working in parallel to execute instructions.

[0074] In one implementation, the reconstructed image can be displayed on a display 1366. The display 1366 can be an LCD display, a CRT display, a plasma display, an OLED, an LED, or any other display known in the art.

[0075] The memory 1362 may be a hard disk drive, a CD-ROM drive, a DVD drive, a FLASH drive, RAM, ROM, or any other electronic storage device known in the art.

[0076] Numerous modifications and variations of the embodiments presented herein are possible in light of the above teachings. It is therefore to be understood that, within the scope of the appended claims, the application may be practiced other than as specifically described herein. The present invention is not limited to the examples described hereinabove. In particular, it is possible to combine features of illustrated examples with those of others in variations not illustrated.

[0077] The components of each device according to the above-described embodiments are conceptual and functionally independent, and are not necessarily physically configured as shown in the drawings. In other words, the specific form of distribution and integration of each device is not limited to that shown in the drawings, and all or part of each device can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc. Furthermore, all or any part of the processing functions performed by each device can be realized by a CPU and a program analyzed and executed by the CPU, or can be realized as hardware using wired logic.

[0078] The medical image processing method described in the above-described embodiment can be realized by executing a prepared program on a computer such as a personal computer or a workstation. This program can be distributed via a network such as the Internet. This program can also be recorded on a non-transitory computer-readable recording medium such as a hard disk, flexible disk (FD), CD-ROM, MO, or DVD, and executed by being read from the recording medium by a computer.

[0079] With respect to the above embodiment, the following supplementary notes are disclosed as one aspect and optional features of the invention. (Appendix 1) An apparatus for performing parameter adaptation for motion compensation in a CT imaging system, the apparatus comprising: a processing circuit that receives projection data obtained from imaging of a subject using the CT imaging system; reconstructs an image of the subject without performing motion compensation based on the received projection data; identifies a blood vessel in the reconstructed image, the blood vessel including a plurality of blood vessel slices; determines parameters to be used during motion estimation of the identified blood vessel based on characteristics of the identified blood vessel; estimates a range of blood vessel motion using the determined parameters; and reconstructs a motion-compensated image of the subject based on the received projection data and the estimated range of blood vessel motion. (Appendix 2) The parameters include the size of a vascular mask to be used during estimation of the range of vascular motion, and the processing circuit may determine a plurality of vascular slices included in the identified vessel, trim the vascular region surrounding each of the plurality of vascular slices based on a default vascular mask of a predetermined default size, obtain a maximum motion artifact within each of the trimmed vascular regions based on a set of predetermined CT number thresholds through a thresholding process, calculate morphological metrics for each vascular slice of the plurality of vascular slices based on the obtained maximum motion artifact within the vascular region surrounding the vascular slice, and determine a mask size for each vascular slice of the plurality of vascular slices based on the morphological metrics calculated for the vascular slice. (Appendix 3) The morphological metric may be represented by the compactness of the largest acquired motion artifact within the vascular region surrounding the vascular slice, and the processing circuitry may calculate the compactness using equation (1), where P means the perimeter of the largest acquired motion artifact and A means the area of ​​the largest acquired motion artifact. (Appendix 4) The vascular mask to be used during estimation of the range of vascular motion may be circular in shape, the size of the vascular mask being characterized by its radius, and the processing circuitry may determine the size of the vascular mask by retrieving the radius of the vascular mask from a look-up table using the calculated compactness as a key. (Appendix 5) The vascular mask to be used during estimation of the range of vascular motion may be circular in shape, the size of the vascular mask being characterized by its radius, and the processing circuitry may determine the size of the vascular mask by applying the calculated compactness measure to a trained neural network and obtaining the radius of the vascular mask from the output of the neural network. (Appendix 6) The parameters may further include the number of control points to be used during estimation of the range of movement of the vessel, and the processing circuitry may estimate the length of the identified vessel, obtain the spacing between adjacent control points, and determine the number of control points based on the estimated length and the obtained spacing. (Appendix 7) The processing circuitry may track the identified vessel, estimate a corresponding length of each of a plurality of vessel slices included in the identified vessel, and obtain an estimated length of the identified vessel based on the estimated corresponding lengths of the plurality of vessel slices. (Appendix 8) The processing circuitry may receive an interval entered by an operator of the CT imaging system as the acquired interval, or may derive an interval within a predetermined range as the acquired interval. (Appendix 9) The predetermined range may be 15 millimeters to 20 millimeters. (Appendix 10) The parameters may further include corresponding positions of each of the control points to be used during estimation of the range of vascular motion, and the processing circuitry may calculate a motion artifact metric for each of the plurality of vascular slices included in the identified vessel, and determine corresponding positions of each of the determined number of control points based on the calculated motion artifact metric. (Appendix 11) The processing circuit may extract a vascular region around each vascular slice of the plurality of vascular slices based on a vascular mask of a particular determined mask size corresponding to the vascular slice, and calculate a motion artifact level for each vascular region of the extracted vascular region as a calculated motion artifact metric. (Appendix 12) The processing circuitry may identify motion artifacts within each of the extracted vessel regions and calculate entropy, compactness, and circularity scores for each of the identified motion artifacts as a calculated motion artifact level. (Appendix 13) The processing circuitry may determine corresponding positions of a determined number of control points along the identified vessel based on the distribution of the magnitude of the calculated motion artifact metric. (Appendix 14) The processing circuitry may determine corresponding positions of the determined number of control points by assigning more control points to portions of the identified vessel that include more vessel slices for which the calculated motion artifact metric exceeds a predetermined threshold compared to other portions of the identified vessel that include fewer vessel slices for which the calculated motion artifact metric exceeds a predetermined threshold. (Appendix 15) The processing circuitry may not assign control points to vascular slices for which the calculated motion artifact metric is below a predetermined threshold. (Appendix 16) 1. A method for performing parameter adaptation for motion compensation in a CT imaging system, the method comprising: receiving projection data obtained from imaging of a subject using a CT imaging system; reconstructing an image of the subject without performing motion compensation based on the received projection data; identifying a blood vessel in the reconstructed image, the blood vessel including a plurality of blood vessel slices; determining parameters to be used during motion estimation of the identified blood vessel based on characteristics of the identified blood vessel; estimating a range of blood vessel motion using the determined parameters; and reconstructing a motion-compensated image of the subject based on the received projection data and the estimated range of blood vessel motion. (Appendix 17) The parameters include the size of the vascular mask to be used during estimation of the range of vascular motion, and the determining step may include determining a plurality of vascular slices included in the identified vessel, trimming the vascular region surrounding each of the plurality of vascular slices based on a default vascular mask of a predetermined default size, obtaining the maximum motion artifact within each of the trimmed vascular regions based on a set of predetermined CT value thresholds via a thresholding process, calculating morphological metrics for each vascular slice of the plurality of vascular slices based on the obtained maximum motion artifact within the vascular region surrounding the vascular slice, and determining a mask size for each vascular slice of the plurality of vascular slices based on the morphological metrics calculated for the vascular slice. (Appendix 18) The parameters may further include the number of control points to be used during estimation of the range of movement of the vessel, and the determining step may include estimating the length of the identified vessel, obtaining a spacing between adjacent control points, and determining the number of control points based on the estimated length and the obtained spacing. (Appendix 19) The parameters may further include corresponding positions of each of the control points to be used during estimation of the range of vascular motion, and the determining step may include calculating a motion artifact metric for each of a plurality of vascular slices included in the identified vessel, and determining the corresponding positions of each of the determined number of control points based on the calculated motion artifact metric. (Appendix 20) 1. A non-transitory computer-readable medium having instructions stored therein that, when executed by one or more processors, cause the one or more processors to perform a method for performing parameter adaptation for motion compensation in a CT imaging system, the method including: receiving projection data obtained from imaging of a subject using the CT imaging system; reconstructing an image of the subject without performing motion compensation based on the received projection data; identifying a blood vessel in the reconstructed image without motion compensation, the blood vessel including a plurality of blood vessel slices; determining parameters to be used during motion estimation of the identified blood vessel based on characteristics of the identified blood vessel; estimating a range of blood vessel motion using the determined parameters; and reconstructing a motion-compensated image of the subject based on the received projection data and the estimated range of blood vessel motion.

[0080] According to at least one of the embodiments described above, it is possible to improve the efficiency of reconstruction processing involving motion compensation.

[0081] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0082] 110: Projection data receiving circuit 120: Image reconstruction (without motion compensation) circuit 130: Adaptive parameter adjustment circuit 140: Motion optimization circuit 150: Image reconstruction (with motion compensation) circuit 210: Reconstruction image receiving circuit 220: Blood vessel identification circuit 230: Mask size adaptation circuit 240: Control point number adaptation circuit 250: Control point position adaptation circuit 260: Adaptive parameter output circuit 410: Blood vessel slice acquisition circuit 420: Blood vessel region extraction circuit 430: Maximum motion artifact acquisition circuit 440:Compactness calculation circuit 450: Mask size determination circuit 810: Blood vessel length estimation circuit 820: Control point interval acquisition circuit :830 Control point number determination circuit 1010: Blood vessel slice acquisition circuit 1020: Blood vessel region extraction circuit 1030: Entropy calculation circuit 1040: Control point assignment circuit 1350: Radiography Gantry 1351:X-ray tube 1352: Annular frame 1353: X-ray detector 1354:DAS 1355: Contactless data transmitter 1356: Pretreatment device 1357: Rotating device 1358: Slip ring 1359: High voltage generator 1360: System Controller 1361: Data / Control Bus 1362:Memory 1363: Current regulator 1364: Reconfiguration Device 1365: Input device 1366: Display

Claims

1. a projection data receiving unit that receives projection data obtained by computed tomography (CT) imaging of a subject; a first image reconstruction unit that generates a reconstructed image of the subject based on the projection data without performing motion compensation; a parameter determination unit that identifies blood vessels in the reconstructed image and determines parameters based on characteristics of the blood vessels; a motion estimation unit that estimates a range of motion of the blood vessel using the determined parameters; a second image reconstruction unit that reconstructs a motion-compensated image of the subject based on the projection data and the range of motion of the blood vessel; A medical image processing device comprising:

2. the parameter determination unit includes a mask size adaptation unit that determines, as the parameter, a size of a vascular mask used to estimate a range of movement of the blood vessel; The mask size adaptor obtaining a plurality of vascular slices contained in the identified vessel; trimming a vascular region around each of the plurality of vascular slices based on a default vascular mask of a predetermined default size; obtaining, via a thresholding process, a maximum motion artifact within each of the trimmed vessel regions based on a set of predetermined CT number thresholds; calculating a morphological metric for the vascular slice based on the acquired maximum motion artifact within the vascular region surrounding the vascular slice; The medical imaging system of claim 1 , further comprising: determining a mask size for the vessel slice based on the morphological metric.

3. the morphological metric is represented by the compactness of the largest motion artifact; The mask size adaptor calculates the compactness using the following formula (1): The medical image processing apparatus according to claim 2 , wherein in formula (1), P denotes the perimeter of the maximum motion artifact, and A denotes the area of ​​the maximum motion artifact. [Equation 1]

4. the vascular mask is circular in shape, and the size of the vascular mask is characterized by its radius; The medical image processing apparatus according to claim 3 , wherein the mask size adaptor determines the size of the vascular mask by obtaining the radius of the vascular mask from a lookup table using the compactness measure as a key.

5. the vascular mask is circular in shape, and the size of the vascular mask is characterized by its radius; The medical image processing device according to claim 3 , wherein the mask size adaptation unit determines the size of the vascular mask by applying the compactness measure to a trained neural network and obtaining a radius of the vascular mask from an output of the neural network.

6. the parameter determination unit includes a control point number adaptation unit that determines, as the parameter, the number of control points used to estimate the range of movement of the blood vessel; The control point number adaptation unit Estimating the length of the identified blood vessel; Get the spacing between adjacent control points, The medical image processing apparatus according to claim 2 , wherein the number of control points is determined based on the estimated length and the acquired interval.

7. The control point number adaptation unit tracing the identified vessel to estimate a corresponding length of each vessel slice of the plurality of vessel slices contained in the identified vessel; The medical image processing apparatus of claim 6 , further comprising: obtaining the length based on the corresponding lengths of the plurality of vascular slices.

8. The medical image processing apparatus according to claim 6 , wherein the control point number adaptation unit receives, as the interval, an interval input by an operator, or derives an interval within a predetermined range.

9. The medical image processing apparatus according to claim 8 , wherein the predetermined range is from 15 mm to 20 mm.

10. the parameter determination unit includes a control point position adaptation unit that determines, as the parameters, positions of control points used to estimate the range of movement of the blood vessel; the control point position adaptor calculates a motion artifact metric for each vessel slice of the plurality of vessel slices included in the identified vessel; The medical image processing apparatus according to claim 6 , further comprising: determining a position corresponding to each of the number of control points determined by the control point number adaptation unit based on the motion artifact metric.

11. the control point position adaptation unit extracts a vascular region around each vascular slice of the plurality of vascular slices based on a vascular mask having a mask size determined by the mask size adaptation unit and corresponding to the vascular slice; The medical image processing apparatus according to claim 10 , wherein a motion artifact level for each of the extracted vascular regions is calculated as the motion artifact metric.

12. the control point position adaptation unit identifies motion artifacts within each of the extracted vascular regions; The medical image processing apparatus of claim 11 , further configured to calculate, as the calculated motion artifact level, an entropy, a compactness, and a circularity score for each of the identified motion artifacts.

13. 11. The medical image processing device of claim 10, wherein the control point position adaptation unit determines positions corresponding to each of the number of control points determined by the control point number adaptation unit based on a distribution of the magnitudes of the calculated motion artifact metric along the identified blood vessel.

14. 14. The medical image processing device according to claim 13, wherein the control point position adaptation unit determines positions corresponding to each of the number of control points determined by the control point number adaptation unit by assigning more control points to portions of the identified blood vessel that include more blood vessel slices whose motion artifact metric exceeds a predetermined threshold compared to portions that include fewer blood vessel slices whose motion artifact metric exceeds a predetermined threshold.

15. The medical imaging apparatus of claim 14 , wherein the control point position adaptation unit does not assign control points to vascular slices where the motion artifact metric is below the predetermined threshold.

16. receiving projection data obtained by CT imaging of a subject; generating a reconstructed image of the object based on the projection data without performing motion compensation; Identifying a blood vessel in the reconstructed image and determining parameters based on characteristics of the blood vessel; estimating a range of motion of the blood vessel using the determined parameters; Reconstructing a motion-compensated image of the subject based on the projection data and the extent of the blood vessel motion. A medical image processing method comprising:

17. receiving projection data obtained by CT imaging of a subject; generating a reconstructed image of the object based on the projection data without performing motion compensation; Identifying a blood vessel in the reconstructed image and determining parameters based on characteristics of the blood vessel; estimating a range of motion of the blood vessel using the determined parameters; Reconstructing a motion-compensated image of the subject based on the projection data and the extent of the blood vessel motion. A program that causes a computer to perform each process.

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