Mu-map alignment using cad data
Patent Information
- Application Number
- CN202610530204.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2025-04-21
- Filing Date
- 2026-04-21
- Publication Date
- 2026-10-23
Smart Images
Figure CN122954459A_ABST
Abstract
Description
Background Technology
[0001] Positron emission tomography (PET) scans can generate quantitative images representing biological processes within a patient, such as glucose metabolism and receptor affinity. PET images can help doctors diagnose diseases, design treatment plans, and assess treatment effectiveness. PET images are particularly useful in the assessment and staging of cancer, cardiovascular disease, and brain disorders.
[0002] Typically, in PET imaging, radiotracers are administered to the patient via intravenous injection, inhalation, oral administration, or direct organ injection. The tracer accumulates at the region of interest (e.g., tumor, heart) based on its composition and affinity. The radioactive decay of the tracer results in the emission of positrons that encounter and are annihilated by electrons. Annihilation produces two 511 keV photons traveling in substantially opposite directions.
[0003] The detector ring surrounds the patient, and overlap is identified when two detectors detect the arrival of two photons within a short time window, indicating that the two photons originated from the same positron annihilation. Because the two “overlapping” photons travel in approximately opposite directions, the positions of the two detector crystals define the response line (LoR) along which the annihilation may have occurred. PET data represents each detected annihilation as the LoR between the two detector crystals. Time-of-flight (TOF) PET data also includes the difference in detection time between the two photons originating from the annihilation. This difference can be used to estimate the specific location of the LoR along which the annihilation event occurred. The PET data is attenuated and corrected, and known algorithms such as Filtered Back Projection (FBP) and Ordered Subset Expectation-Maximization (OSEM) are used to reconstruct a 3D PET image from the PET data.
[0004] Attenuation correction for PET data is performed based on the attenuation properties of the materials that photons pass through before reaching the detector. These materials include the patient's tissue and any hardware components, such as the patient stage, that are also present within the detector ring. In the case of PET magnetic resonance (MR) imaging, these hardware components may also include local MR transmit / receive coils.
[0005] Attenuation characteristics are typically represented by one or more graphs, or mu-maps, of linear attenuation coefficients. A patient's mu-map can be determined from their MR or computed tomography (CT) data. This approach is unsuitable for PET-MR imaging because MR imaging does not measure electron density but relies on the presence of protons within the object being imaged. Typical clinical MR imaging therefore does not receive signals from hardware components such as the patient stage and the MR coil located in the PET field of view and contributing to photon attenuation. Consequently, MR imaging cannot be used to generate appropriate attenuation correction maps corresponding to such hardware components.
[0006] Traditional methods for determining the mu-map of hardware components include calculating the mu-map based on the material composition of the hardware component and the attenuation characteristics of the material relative to 511 keV photons. Such calculated mu-maps are known to be inaccurate and poorly reflect the attenuation complexity of the hardware component. Alternatively, a hardware component mu-map can be generated based on CT scans of the hardware component. These CT scans exhibit inherent misalignment, and the mu-maps generated from them are difficult to align with patient mu-maps and subsequently acquired PET data.
[0007] Inaccurate mu-maps result in PET images displaying inaccurate quantification values. Inaccurate quantification can hinder diagnosis, treatment planning, and treatment evaluation. The system is expected to efficiently generate improved linear attenuation coefficient maps for use in PET image reconstruction. Attached Figure Description
[0008] Figure 1 This is a block diagram illustrating the generation of a mu-map for hardware components according to some embodiments.
[0009] Figure 2A and 2B This includes flowcharts of a process for generating a mu-map for hardware components, according to some embodiments.
[0010] Figure 3 This is a block diagram illustrating the calculation of a mu-map for hardware components according to some embodiments.
[0011] Figures 4A to 4C The illustration shows the acquisition of CT images using hardware components according to some embodiments.
[0012] Figures 5A to 5C The illustration shows the generation of a mu-map from a hardware-based CT image according to some embodiments.
[0013] Figure 6 The illustration shows the correspondence between a mu-map generated from a hardware-based CT image according to some embodiments and a portion of the mu-map calculated by the hardware element.
[0014] Figures 7A to 7C The illustration shows the determination of the transformation between a mu-map generated from a hardware-based CT image and a corresponding portion of a calculated mu-map from the hardware element, according to some embodiments.
[0015] Figures 8A to 8C The illustration shows the application of mu-map generated from hardware-based CT images, according to some embodiments.
[0016] Figure 9The illustration shows a combination of a transformed mu-map and a hardware mu-map according to some embodiments.
[0017] Figure 10 This is a block diagram of a PET-MR system for reconstructing PET images based on hardware mu-map, according to some embodiments.
[0018] Figure 11 This is a view of the PET-MR system, head / neck coil, and body coil prior to scanning a patient, according to some embodiments.
[0019] Figure 12 This is a block diagram of a CT imaging system according to some embodiments.
[0020] Figure 13 This is a block diagram of a PET-MR imaging system according to some embodiments. Detailed Implementation
[0021] The following description is provided to enable those skilled in the art to make and use the described embodiments. However, various modifications will still be apparent to those skilled in the art.
[0022] In traditional CT, data is acquired in blocks or slices as the object of interest (in this case, a hardware element) moves through the system. The movement of the hardware element can be periodic (i.e., static intensity-modulated) or continuous. The CT data for each segment is reconstructed into a segment-specific volume, and the volumes are combined to generate a final volume representing the desired span of the hardware element. Some hardware (e.g., a patient table) is too large to be captured in a single pass and must be stitched together using multiple CT scans. Known techniques can then be used to generate a mu-map from the final volume. A mu-map can be referred to, for example, as a “linear attenuation correction map,” an “attenuation correction map,” and an “attenuation map.”
[0023] The final volume and the resulting mu-map can suffer from inaccuracies due to misalignment between specific portions of the volume, especially in cases requiring multiple CT scans. Operators can attempt to manually correct these misalignments, but this is time-consuming and error-prone. The inventors of this invention have discovered that these misalignments can be caused by stage displacement occurring between the acquisition of CT data from various portions. Embodiments address these and other displacements by using design data for the hardware components to align them before combining specific portions of the CT volume into the final volume. The mu-map generated from this final volume exhibits improved accuracy.
[0024] Figure 1This is a block diagram of a system 100 for generating a mu-map of in-machine hardware components according to some embodiments. The components illustrated in system 100 can be implemented in computer hardware, program code, and / or one or more computing systems executing such program code, as is known in the art. Such a computing system may include one or more processing units that execute program code stored in one or more non-transient storage media. In some embodiments, more than one functional component may be implemented by a single computing system. One or more computing systems may include virtual machines, and one or more computing systems may include cloud-based computing resources that provide on-demand scalability and fault tolerance.
[0025] Scanner 110 may include a CT scanner capable of generating CT data of an object. Embodiments may employ any type of CT scanner, including but not limited to PET-CT scanners, SPECT-CT scanners, and C-arm scanners.
[0026] Scanner 110 includes an x-ray source 112 and an x-ray detector 114. A CT scanner according to some embodiments may include one or more detectors and one or more x-ray sources. Each of the one or more detectors may include a photon-counting CT detector or an energy-integrated detector. Generally, CT scanner 110 acquires one or more projected images of an object located in the field of view between x-ray source 112 and x-ray detector 114 at each of a variety of projection angles. In the illustrated example, a first portion of patient table 120 is positioned in the field of view between x-ray source 112 and x-ray detector 114, and therefore, the projected image depicts the attenuation characteristics of the first portion. CT images are reconstructed based on the projected images, as is known in the art. CT image 130a, for example, represents the three-dimensional volume of the first portion of patient table 120 reconstructed from a projected image acquired when the first portion is positioned between x-ray source 112 and x-ray detector 114.
[0027] The stand 120 includes a support 122, a stand 124, and a base 126. The support 122 is designed to support the patient during PET scans. The support 122 may consist of a number of individual components, including but not limited to plastics, composites, metals, wires, circuits, mechanical parts, etc.
[0028] The support 122 can be configured to move relative to the frame 124 and further into the chamber 111 to position a second portion of the stage 120 in the field of view between the x-ray source 112 and the x-ray detector 114. In some embodiments, the frame 124 may also, or alternatively, be movable relative to the base 126 to move the support relative to the scanner 110. Embodiments are not limited to any particular stage configuration or design.
[0029] The projection image of the second part is acquired as described above. CT image 130b represents the three-dimensional volume of the second part of the patient stage 120 reconstructed from the projection image acquired when the second part is positioned between the x-ray source 112 and the x-ray detector 114. The process can continue as described above until N CT images of adjacent parts of stage 122 are reconstructed. Figure 1 The illustration shows N=4 CT images 130a to 130d.
[0030] Each of the CT images 130a-130d is converted into a corresponding one in mu-map 140a-140d. The value of each voxel in the CT images 130a-130d is converted from Hounsefield units to a linear attenuation coefficient, as is known in the art. Then, since CT scanners typically use multicolor X-rays in the range of ~80-140 keV, an energy scaling transformation (bilinear or piecewise linear) is performed to convert the linear attenuation coefficient to a 511 keV attenuation coefficient.
[0031] Figure 1 A structural design document 150 is also described. Document 150 may include three-dimensional models representing the elements of stage 120 (or at least the elements of relevant portions of stage 120). Document 150 may define the variation of each element in three-dimensional space relative to each other element and may indicate the composition (i.e., material) of each element. Document 150 may represent the elements of stage 120 at any suitable resolution and / or level of accuracy. Document 150 may conform to any CAD or other design format.
[0032] The mu-map determination component 155 determines a mu-map 160 based on document 150 and reference document 165. According to some embodiments, reference document 165 specifies a linear attenuation coefficient for each of a plurality of materials, including those specified in document 150. For each element specified in document 150, mu-map determination component 155 determines the linear attenuation coefficient of the material constituting the element and correlates the determined coefficient with the three-dimensional position of the element within stage 120. According to some embodiments, document 150 uses polygons to represent the elements of stage 120. The mu-map determination component 155 can thus convert the polygons into voxels before correlating the determined coefficients with voxels.
[0033] Optimizer 170 determines transformation matrix 175 based on mu-maps 140a-140d and mu-map 160. Generally, and as will be described in more detail below, optimizer 170 determines the transformation used to align each of mu-maps 140a-140d with the corresponding portion of mu-map 160. Each transformation matrix 175 is applied to its corresponding mu-map 140a-140d. Since the corresponding portions of mu-map 160 are aligned with each other, the resulting mu-maps 180a-180d are also aligned with each other. Mu-maps 180a and 180d are combined without requiring further alignment to obtain mu-map 190 for part of stage 120.
[0034] mu-map 190 can be used to perform attenuation correction on PET data acquired (or acquired) when part of the patient was (or was) in the field of view during the acquisition of the PET data. Assuming that the patient was also in the field of view during the acquisition of the PET data, mu-map 190 can be combined with the patient's mu-map (e.g., obtained via CT or MR data) to perform attenuation correction.
[0035] Figure 2A and 2B This includes a flowchart of a mu-map process 200 for generating hardware elements according to some embodiments. Process 200 can be executed by any combination of known or becoming known hardware and software. The program code embodying the process described herein can be stored on any one or more non-transient tangible media and executed by any suitable processing unit, including fixed disks, volatile or non-volatile random access memory, DVDs, flash drives, and magnetic tapes, including but not limited to one or more microprocessors, microcontrollers, processor cores, and processor threads. Embodiments are not limited to the examples described below.
[0036] Initially, at S210, the structural design of the hardware is obtained. The hardware may consist of physical structures that are expected to be positioned in the field of view during a PET-MR scan. The hardware may include components of a patient table as described above, and may also include MR coils (e.g., head coil, body coil). The structural design may include: a three-dimensional model representing the variations of the hardware's components in three-dimensional space relative to each other component. The design may also specify the material composition of each component. The structural design may be obtained from one or more CAD files, such as, but not limited to, .stl, .dxf, .iges, CATIA, Autodesk inventor, and Solid Edge files.
[0037] The mu-map of the hardware is calculated at S215. The mu-map is calculated based on the structural design and the linear attenuation coefficient of the hardware materials. Figure 3 This is a block diagram illustrating the calculation of a mu-map for hardware components according to some embodiments. Figure 3 This includes a computing system 310 capable of executing applications such as CAD. At S210, structural design files 150 are obtained from system 310.
[0038] The sorter / voxostat 320 can sort the data in file 150 by material and save the sorted data individually in CAD format. For example, file plastic.stl contains CAD data for the plastic components of the table, file copper.stl contains CAD data for the copper components of the table, and file fiberglass.stl contains CAD data for the fiberglass components of the table.
[0039] Each file can be represented using a polygon to represent a hardware component. The sorter / voxelizer 320 converts each file from polygons to voxels, resulting in images 330, 332, and 334. The resolution of images 330, 332, and 334 can be the same as the resolution of the CT image to be acquired at S220. The coefficient discriminator 340 determines one of the linear attenuation coefficients 345 for each image 330, 332, and 334, and assigns the determined linear attenuation coefficient for each image to each voxel of that image. The resulting image is... Figure 3 The images are depicted as images 350, 352, and 354. According to some embodiments, the material-specific linear attenuation coefficient images are combined by summing the voxel values of the material-specific linear attenuation coefficient images to obtain the calculated mu-map 360. Any other type of combination can be used at S215.
[0040] A CT scanner is used at S220 to acquire CT images of the hardware components. For example... Figure 2A As indicated by the dashed arrow, CT images can be acquired entirely after the calculation of the mu-map at S215 and / or at locations different from the calculation of the mu-map at S215. Figures 4A to 4C The illustration shows the acquisition of CT images using hardware components according to some embodiments.
[0041] like Figure 4AAs shown, the first portion 122a of the patient stage 120 is located in the field of view between the x-ray source 112 and the x-ray detector 114 of the CT scanner 110. In the case where the hardware for which a mu-map is calculated includes one or more MR coils, these MR coils are placed on the stage 120 at the positions intended for the PET scan during S220. The x-ray source 112 and the x-ray detector 114 are rotated about the portion 122a to acquire a projected image 410a from a P-projection angle relative to the portion 122a. The reconstruction unit 420 reconstructs a three-dimensional CT image 430a of the portion 122a from the projected image 410a.
[0042] Returning to process 200, at S225 it is determined whether to acquire an image of an additional portion of the hardware. If so, the hardware is moved relative to the CT scanner to place another portion of the hardware within the CT scanner's field of view. Figure 4B The portion 122b of stage 120 that has moved between tube 112 and detector 114 is depicted. Returning to S220, the x-ray source 112 and x-ray detector 114 are rotated around portion 122b to obtain projection image 410b, and reconstruction unit 420 reconstructs a three-dimensional CT image 430b of portion 122b from projection image 410b.
[0043] Assume that the determination at S225 is again affirmative and as follows: Figure 4C The stage 120 is moved to place portion 122c between tube 112 and detector 114. At S220, projection image 410c is acquired, and reconstruction unit 420 reconstructs a 3D CT image 430c of portion 122c from projection image 410c. It will now be assumed that the determination at S225 is negative (i.e., all desired portions have been acquired) and the process continues to S235.
[0044] At S235, each CT image acquired at S220 will be converted into a corresponding mu-map. Figures 5A to 5C The illustration shows the generation of mu-maps based on hardware elements of CT images at S235 according to some embodiments. The mu-map generation unit 510 converts the value of each voxel in the CT images 430a-430c from Huntsfield units to a linear attenuation coefficient, as is known in the art, and performs an energy scaling transformation to convert the linear attenuation coefficient to a 511 keV attenuation coefficient, thereby obtaining the corresponding mu-maps 530a, 530b, and 530c.
[0045] Each mu-map determined at S235 corresponds to a portion of the calculated mu-map at S215 spanning the same range across the z-axis. Figure 6The illustration shows mu-maps 530a, 530b, and 530c according to some embodiments, and corresponding portions 360a, 360b, and 360c of the calculated mu-map 360. Due to the displacement of stage 120 (e.g., roll, pitch, yaw) between the acquisition of CT images at S220, CT images 430a, 430b, and 430c may be misaligned with each other. Consequently, the mu-maps 530a, 530b, and 530c determined therefrom may also be misaligned with each other. However, the corresponding portions 360a, 360b, and 360c of the calculated mu-map 360 are aligned with each other. Accordingly, aligning each of mu-maps 530a, 530b, and 530c with its corresponding portion 360a, 360b, or 360c will effectively align mu-maps 530a, 530b, and 530c with each other.
[0046] At S240, a transformation matrix is determined between each determined mu-map and its corresponding portion calculated at S215. The transformation matrix determined for each mu-map is intended to align the mu-map with its corresponding portion of the calculated mu-map. Figures 7A to 7C The illustration shows the determination of the transformation matrix according to some embodiments.
[0047] In the xy-plane, mu-map 530a is rotated relative to part 360a. Mu-map 530b is shifted relative to part 360b in the x-direction, and both rotated in the xy-plane and shifted relative to part 360c in the x and y directions. Optimizer 170 determines the transformation matrix by comparing mu-map 530a with part 360a, comparing mu-map 530b with part 360b, and comparing mu-map 530c with part 360c. T 1 , T 2 and T 3 The optimizer 170 can determine the value of each of the six degrees of freedom (i.e., x, y, z, roll, pitch, yaw) of the transformation matrix for a given mu-map by independently varying the parameters until the sum of the voxel differences between the mu-map and the corresponding CAD-derived mu-map portion is minimized.
[0048] Advantageously, the above techniques allow optimizer 170 to determine a suitable transformation matrix even if the linear attenuation coefficient of the mu-map portion derived from CAD does not precisely match the linear attenuation coefficient of the mu-map portion derived from CT (or even reflect the same proportion as the linear attenuation coefficient of the mu-map portion derived from CT). A suitable transformation matrix can be determined even though there are significant differences in the linear attenuation coefficients of the voxels of the PCB and cables in the display stage or local MR coil, which are difficult to calculate with respect to CAD data, as long as the data are well correlated for most voxels.
[0049] At S245, each determined transformation matrix is applied to its corresponding mu-map. Figures 8A to 8C The diagram illustrates the image transformation unit 810 transforming the matrix. T 1 , T 2 and T 3 These are applied to mu-maps 530a, 530b, and 530c respectively, resulting in mu-maps 830a, 830b, and 830c. Since corresponding parts 360a, 360b, and 360c are aligned with each other, mu-maps 830a, 830b, and 830c are also aligned with each other. At S250, these aligned mu-maps are combined to form the final mu-map. Figure 9 The illustration shows a hardware mu-map 900 generated from a combination of aligned mu-maps 830a, 830b, 830c and other aligned mu-maps, according to some embodiments.
[0050] According to some embodiments, S235 is not performed, and a transformation matrix is determined from CT images 430a, 430b, and 430c and applied to CT images 430a, 430b, and 430c. Then, a mu-map is determined from each of the thus transformed CT images 430a, 430b, and 430c, and the mu-maps are combined at S250.
[0051] According to some embodiments, mu-map 900 can be used in the reconstruction of PET images from PET data. Figure 10 This is a block diagram of a PET-MR system for reconstructing PET images based on hardware mu-map, according to some embodiments. Figure 10 The image includes an axial view of a PET-MR imaging system 1010, which can generate MR and PET images of an imaging subject 1020 disposed therein, as is known in the art. The imaging subject 1020 may include a human body, a phantom, or any other suitable subject.
[0052] The PET-MR imaging system 1010 includes a housing 1012, which includes an MR imaging component (not shown). The MR imaging component can operate in conjunction with one or more MR imaging hardware accessories 1014 disposed within the housing 1012 and adjacent to the body 1020 to generate and acquire MR signals from which MR images of the body 1020 can be generated. The housing 1012 also includes a PET detector ring 1016 and other components necessary for generating PET data from which PET images can be reconstructed. The detector ring 1016 can consist of any number (e.g., eight) adjacent and coaxial detector rings.
[0053] The PET-MR system 1010 also includes a stage 1018 on which the body 1020 rests during imaging. As described above, photons emitted from the body 1020 during PET imaging can be attenuated by the MR imaging hardware accessory 1014 and the stage 1018 before being detected by the PET detector ring 1016. Therefore, it would be advantageous to correct the acquired PET data using one or more attenuation correction maps representing the MR imaging hardware accessory 1014 and the stage 1018 and aligned with the PET imaging component. However, since the MR imaging hardware accessory 1014 and the stage 1018 are substantially invisible to the MR imaging component of the system 1010, such attenuation correction maps cannot be generated from the MR signals acquired by the MR imaging component when the body 1020 is positioned in the desired PET imaging location.
[0054] Photons emitted from the body 1020 are also attenuated by the body 1020 before reaching the detector ring 1016. Accordingly, as... Figure 10 As shown, some embodiments operate to obtain MR data 1030 representing the body 1020 and generate a body mu-map 1045 based on the MR data 1030. The attenuation map determination component 1040 can generate the body mu-map 1045 based on the MR data 1030, as is known in the art.
[0055] Figure 10 A hardware mu-map 900, which may have been generated as described herein, is also shown. The mu-map 900 includes linear attenuation coefficients for voxels of stage 1018 and any hardware attachments located within the PET field of view of the PET-MR system 1010. The PET imaging component of the PET-MR system 1010 also operates to generate PET data 1070 based on photons emitted from within the body 1020, as is known in the art.
[0056] Generally, each PET detector in detector ring 1016 includes one or more scintillation elements and one or more electrical transducers. In response to receiving 511 keV photons generated by an annihilation event within body 1020, the scintillation element generates photons with energies of several electron volts (eV). The electrical transducer converts the low-energy photons created by the scintillation element into electrical signals. According to some embodiments, the electrical transducer may include, for example, a SiPM, PMT, or semiconductor-based detector.
[0057] PET data 1050 can represent detected coincidences as raw (i.e., list mode) data and / or sine plots. List mode data can represent each coincidence using data specifying the LoR between two crystals, the time it takes for each annihilated photon to reach each crystal, photon energy, etc. A sine plot is a data array of angles relative to displacement for each detected coincident LoR. The sine plot consists of: a row containing the LoRs for a specific azimuth angle φ; each of these rows corresponding to a one-dimensional parallel projection of the tracer distribution at a different coordinate; and the sine plot storing the position of each coincident LoR such that all LoRs passing through a single point in the volume trace a sine curve in the sine plot.
[0058] "True" coincidence refers to the detection of two coincident photons originating from a single annihilation event located on the LoR between the two detectors. "Random" coincidence refers to two coincident photons not originating from the same annihilation event. "Scattered" coincidence is a true coincidence of the following type: where two coincident photons originate from the same annihilation event, but the annihilation event is not located along the LoR of the two detectors because one or both photons interact with and scatter within the body or with other materials.
[0059] Traditional PET scanners detect all overlaps without considering whether they are true, random, or scattered overlaps. Since only true overlaps represent spatial information related to the distribution of tracers within the body, random and scattered overlaps are often subtracted from them, or used to correct the acquired PET data before or during PET image reconstruction. Software- and / or hardware-based schemes can be used to estimate random overlaps and subtract them from the detected overlaps to obtain the PET data1050.
[0060] Image alignment determination component 1060 receives a physical mu-map 1045, a hardware mu-map 900, and PET data 1050. Image alignment determination component 1060 operates to determine the alignment (and / or distortion) of the hardware mu-map 900 relative to the PET data 1050. According to some embodiments, the determined alignment is intended to obtain a more accurate attenuation correction of the PET data 1050 than in the present art. The determination of alignment and / or distortion may include the determination of the alignment and / or distortion of the hardware mu-map 900, which maximizes the consistency of the PET data 1050 so corrected when used for attenuation correction of the PET data 1050.
[0061] Image reconstruction unit 1070 receives PET data 1050, an aligned hardware mu-map 900, and a subject mu-map 1045, and reconstructs the PET image 1080 based on these. Image reconstruction unit 1070 can employ any known or becoming known reconstruction algorithm. Such a reconstruction algorithm can correct for random and scattering overlap and based on the received mu-map before or during reconstruction.
[0062] Figure 11 The body 1120 is shown before being placed into the chamber 1112 of the PET-MR system housing 1110. The MR head / neck coil 1114 and the MR body coil 1115 surround the body 1120 and will thus be positioned in the field of view for subsequent PET scans. Accordingly, S215 of process 200 may include the calculation of a mu-map based on the structural design of the MR head / neck coil 1114, the MR body coil 1115, and the stage 1116, as well as the attenuation correction map associated with the MR head / neck coil 1114, and the CT image acquired at S220 may include various portions of the MR head / neck coil 1114, the MR body coil 1115, and / or the stage 1116.
[0063] Figure 12 A CT scanner 1200 is illustrated for performing one or more of the steps described herein. Embodiments are not limited to scanner 1200. Scanner 1200 includes a gantry 1210 defining a chamber 1212. Gantry 1210 includes a CT imaging component for acquiring CT image data of an object (e.g., a patient, table, MR coil) disposed in the chamber 1212. The CT imaging component may include one or more X-ray tubes and one or more corresponding detectors, as known in the art.
[0064] Bed 1215 and base 1216 are operable to move a patient lying on bed 1215 into and out of chamber 1212 before, during, and after imaging. In some embodiments, bed 1215 is configured to translate over base 1216, and in other embodiments, base 1216 may be moved together with or alternatively from bed 1215. According to some embodiments, bed 1215 and base 1216 may provide continuous bed motion and / or static intensity-modulated motion during such scanning.
[0065] The control system 1220 may include any general-purpose or special-purpose computing system. The control system 1220 includes: one or more processing units 1222 (e.g., processors, processor cores, execution threads, etc.) configured to execute program code for enabling the system 1220 to acquire CT data and generate images therefrom; and a storage device 1230 for storing the program code. The storage device 1230 may include one or more fixed disks, solid-state random access memory, and / or removable media (e.g., thumb drives) mounted in a corresponding interface (e.g., a universal serial bus port).
[0066] Storage device 1230 stores the program code of control program 1231. One or more processing units 1222 can execute control program 1231 to control the CT imaging element of scanner 1200 using CT system interface 1223 and bed interface 1224 to acquire CT data 1232 of hardware disposed in chamber 1212 and reconstruct CT images 1233 therefrom. A mu-map 1234 can be exported from CT data 1232. A mu-map 1235 can be exported from CAD data of hardware disposed in chamber 1212, as described herein. Furthermore, control program 1231 can be executed as described herein to align corresponding portions of mu-map 1234 and mu-map 1235 and combine the aligned mu-maps into a hardware mu-map for use in future PET reconstruction.
[0067] CT images 1233, mu-map 1234, and mu-map 1235 can be transmitted to terminal 1240 via terminal interface 1226. Terminal 1240 may include a display device and an input device coupled to system 1220. Terminal 1240 may display the received CT images 1233, mu-map 1234, and mu-map 1235. Terminal 1240 may receive user input for controlling the display of data, operation of scanner 1200, and / or the processing described herein. In some embodiments, terminal 1240 is a separate computing device, such as, but not limited to, a desktop computer, laptop computer, tablet computer, and smartphone.
[0068] Figure 13The illustration shows a PET-MR system 1 for performing MR and PET scans according to some embodiments. System 1 includes: a chassis 2 defining a chamber 3 in which a patient 4 is disposed. The chassis 2 includes a polarized main magnet 5, a gradient coil 6, and an RF coil 7 disposed around the chamber 3. An MR head / neck coil 8 is disposed around the head and neck of the patient 4. According to some embodiments, the polarized main magnet 5 generates a uniform main magnetic field (B0), and the gradient coil 6 generates a magnetic field gradient. G x , G y and G z To select a specific part of the patient 4 to be imaged, the RF coil 7 emits an excitation field (B1), and the MR head / neck coil 8 receives the signal emitted from the patient 4 after the excitation field is removed.
[0069] The PET detector 10 includes a loop of PET detectors disposed between the gradient coil 6 and the RF coil 7, but embodiments are not limited thereto. The PET detector 10 may include any number or type of detectors in any configuration, as is known in the art. Each detector may include one or more scintillation elements and one or more electrical transducers to generate an electrical signal in response to received gamma photons.
[0070] System 30 controls the operation of PET-MR system 1 via MR system electronics 20 and PET system electronics 25. MR system electronics 20 may include, for example, a sequence controller, a digital-to-analog converter, and a power amplifier to generate gradient pulses and RF pulses according to a desired MR imaging sequence. PET system electronics 25 may include electronic components to receive electrical signals generated by detector 10 and detect overlap accordingly.
[0071] System 30 may include any general-purpose or special-purpose computing system. Figure 13 The system 30 includes: one or more processing units 32 configured to execute processor-executable program code for causing the system 30 to operate as described herein; and a storage device 40 for storing the program code. The storage device 40 may include one or more fixed disks, solid-state random access memory, and / or removable media (e.g., thumb drive) installed in a corresponding interface (e.g., a USB port).
[0072] Storage device 40 stores the program code of control program 42. One or more processing units 32 can execute control program 42 to provide instructions to MR system electronics 20 via MR system interface 34. For example, control program 42 can be executed to instruct MR system electronics 20 to initiate a desired pulse sequence in order to acquire MR data for storage in MR data 44. Control program 42 can also be executed to instruct PET system electronics 25 to acquire PET data 45 representing overlap acquired by PET detector 10. Control program 42 may further include processor-executable program code for causing system 30 to perform attenuation correction on the acquired PET data based on a hardware mu-map 43 generated as described herein and a mu-map 43 of patient 4 generated from MR data 44.
[0073] The acquired and / or attenuation-corrected PET, MR, and combined images can be provided to the terminal 50 via the terminal interface 36 of the system 30. The terminal interface 36 can also receive input from the terminal 50, which can be used to provide commands to the control program 42 to control the elements of the system 1. Commands may include commands for initiating an imaging sequence to acquire image data of a subject. The terminal 50 may consist only of a display device and an input device coupled to the system 30. In some embodiments, the terminal 50 is a separate computing device, such as, but not limited to, a desktop computer, laptop computer, tablet computer, and smartphone.
[0074] Each component described herein may include any elements necessary for its operation, as well as additional elements for providing functionality beyond that described herein. Each functional component described herein may be implemented in computer hardware, in program code, and / or in one or more computing systems that execute such program code, as is known in the art. Such a computing system may include one or more processing units that execute processor-executable program code stored in a memory system.
[0075] Those skilled in the art will appreciate that various adaptations and modifications can be configured to fit the embodiments described above without departing from the claims. Therefore, it should be understood that the claims may be practiced in ways other than those specifically described herein.
Claims
1. A system comprising: X-ray tube; The detector receives photons emitted by the X-ray tube and generates corresponding projection data based on the received photons; The processing unit must: Operate the X-ray tube and the detector to generate corresponding projection data for each of the multiple portions of the patient support; Reconstruct the corresponding computed tomography (CT) image of each of the plurality of parts from the corresponding projection data of each of the plurality of parts; Generate a corresponding linear attenuation coefficient map for each of the plurality of portions from the corresponding CT images; Determine the corresponding transformation matrix between each of the linear attenuation coefficients in the linear attenuation coefficient diagram and the corresponding portion of the predetermined linear attenuation coefficient diagram of the patient support; The corresponding transformation matrix is applied to each of the linear attenuation coefficient graph; as well as Combine the transformed linear attenuation coefficient diagrams.
2. The system of claim 1, wherein the predetermined linear attenuation coefficient diagram of the patient support is calculated based on the structural design of the patient support, the material of the patient support, and the linear attenuation coefficient of the material.
3. The system of claim 2, wherein the structural design is specified in a computer-aided design document.
4. The system of claim 2, wherein determining the corresponding transformation matrix between each of the linear attenuation coefficient graphs and the corresponding portion of a predetermined linear attenuation coefficient graph of the patient support comprises: For each of the linear attenuation coefficients in the graph, determine ( x , y , z The position and the corresponding portion of the curve relative to the predetermined linear attenuation coefficient diagram. x , y and z The rotation angle of each axis.
5. The system of claim 2, wherein the proportion of the predetermined linear attenuation coefficient map is different from the proportion of the plurality of linear attenuation coefficient maps.
6. The system of claim 1, wherein determining the corresponding transformation matrix between each of the linear attenuation coefficient graphs and the corresponding portion of a predetermined linear attenuation coefficient graph of the patient support comprises: For each of the linear attenuation coefficients in the graph, determine ( x , y , z The position and the corresponding portion of the curve relative to the predetermined linear attenuation coefficient diagram. x , y and z The rotation angle of each axis.
7. The system of claim 6, wherein the proportion of the predetermined linear attenuation coefficient map is different from the proportion of the plurality of linear attenuation coefficient maps.
8. A method comprising: Acquire corresponding computed tomography (CT) images of each of the multiple adjacent portions of the patient support; Determine the corresponding transformation matrix between each portion of the CT image and the corresponding portion of the predetermined linear attenuation coefficient map of the patient support; The corresponding transformation matrix is applied to each of the CT images; Generate a corresponding linear attenuation coefficient map from each of the transformed CT images; The linear attenuation coefficient diagrams are combined to form a combined linear attenuation coefficient diagram; as well as PET images are reconstructed based on positron emission tomography (PET) data and a combined linear attenuation coefficient map.
9. The method of claim 8, further comprising: A predetermined linear attenuation coefficient diagram of the patient support is calculated based on the structural design of the patient support, the material of the patient support, and the linear attenuation coefficient of the material.
10. The method of claim 9, further comprising: The structural design is determined from computer-aided design documents.
11. The method of claim 9, wherein determining the corresponding transformation matrix between each portion of the CT image and the corresponding portion of a predetermined linear attenuation coefficient map of the patient support comprises: For each of the CT images, determine ( x , y , z The position and the corresponding portion of the curve relative to the predetermined linear attenuation coefficient diagram. x , y and z The rotation angle of each axis.
12. The method of claim 9, wherein the predetermined linear attenuation coefficient map includes values of linear attenuation coefficients, and the plurality of CT images include Huntsfield values.
13. The method of claim 8, wherein determining the corresponding transformation matrix between each portion of the CT image and the corresponding portion of a predetermined linear attenuation coefficient map of the patient support comprises: For each of the CT images, determine ( x , y , z The position and the corresponding portion of the curve relative to the predetermined linear attenuation coefficient diagram. x , y and z The rotation angle of each axis.
14. The method of claim 12, wherein the predetermined linear attenuation coefficient map includes values of linear attenuation coefficients, and the plurality of CT images include Huntsfield values.
15. One or more computer-readable media storing program code, said program code being executable by a processor to cause a system to perform operations including: Acquire corresponding computed tomography (CT) images of each of the multiple adjacent portions of the patient support; Determine the corresponding transformation matrix between each portion of the CT image and the corresponding portion of the predetermined linear attenuation coefficient map of the patient support; The corresponding transformation matrix is applied to each of the CT images; Generate a corresponding linear attenuation coefficient map from each of the transformed CT images; The linear attenuation coefficient diagrams are combined to form a combined linear attenuation coefficient diagram; as well as PET images are reconstructed based on positron emission tomography (PET) data and a combined linear attenuation coefficient map.
16. The computer-readable medium of claim 15, wherein the program code is executable by a processor to cause the system to perform operations further including: A predetermined linear attenuation coefficient diagram of the patient support is calculated based on the structural design of the patient support, the material of the patient support, and the linear attenuation coefficient of the material.
17. The program code of one or more computer-readable media as claimed in claim 16, wherein the program code is executable by a processor to cause the system to perform operations further including: determining the structural design from a computer-aided design document.
18. The one or more computer-readable media of claim 16, wherein determining the corresponding transformation matrix between each of the CT images and a corresponding portion of a predetermined linear attenuation coefficient map of the patient support comprises: For each of the CT images, determine ( x , y , z The position and the corresponding portion of the curve relative to the predetermined linear attenuation coefficient diagram. x , y and z The rotation angle of each axis.
19. The one or more computer-readable media of claim 16, wherein the predetermined linear attenuation coefficient map includes values of linear attenuation coefficients, and the plurality of CT images include Huntsfield values.
20. The one or more computer-readable media of claim 15, wherein determining the corresponding transformation matrix between each of the CT images and a corresponding portion of a predetermined linear attenuation coefficient map of the patient support comprises: For each of the CT images, determine ( x , y , z The position and the corresponding portion of the curve relative to the predetermined linear attenuation coefficient diagram. x , y and z The rotation angle of each axis.