System and method for automated image sizing

Abstract

The invention relates to a system and a method for automatically determining image size. A system and method includes obtaining, at a processor, a clinical task of scanning a subject with a computed tomography imaging system. The system and method also include obtaining, at the processor, scan parameters for scanning. The system and method also includes automatically determining, via the processor, reconstruction matrix parameters based at least on the clinical task and the scan parameters for generating a reconstructed image from tomography data of the subject obtained using the scan.

Description

Background Technology

[0001] The subject matter disclosed herein relates to medical imaging systems, and more specifically to the automatic determination of image size from computed tomography (CT) imaging data acquired using a computed tomography (CT) imaging system.

[0002] In CT, X-ray radiation crosses the subject of interest (such as a human patient), and a portion of the radiation impacts the detectors that collect image data. In digital X-ray systems, photodetectors generate signals representing the amount or intensity of radiation impacting discrete pixel areas on the detector surface. These signals can then be processed to generate images that can be displayed for viewing. From images produced by such systems, it is possible to identify and examine internal structures and organs within the patient's body. In CT imaging systems, as the gantry moves around the patient, an array of detector elements or sensors generates similar signals at various locations, enabling volumetric reconstruction.

[0003] High-resolution CT scanners utilize large image matrix sizes to provide an improved spatial resolution experience. In some cases, these large image matrix sizes improve image quality at the expense of increased disk space utilization. Summary of the Invention

[0004] The following outlines some embodiments commensurate with the scope of the originally claimed subject matter. These embodiments are not intended to limit the scope of the claimed subject matter, but rather to provide only a brief overview of the possible forms of the subject matter. In reality, the subject matter may include many forms that may be similar to or different from the embodiments described below.

[0005] In one embodiment, a computer-implemented method is provided. The computer-implemented method includes obtaining, at a processor, a clinical task of scanning a subject using a computed tomography imaging system. The computer-implemented method also includes obtaining scan parameters for the scan at the processor. The computer-implemented method further includes automatically determining, via the processor, reconstruction matrix parameters based at least on the clinical task and the scan parameters, the reconstruction matrix parameters being used to generate a reconstructed image based on tomographic data obtained from the subject using the scan.

[0006] In another embodiment, a system is provided. The system includes memory encoding processor-executable routines. The system also includes a processor configured to access the memory and execute processor-executable routines, wherein the processor-executable routines, when executed by the processor, cause the processor to perform actions. These actions include obtaining a clinical task of scanning a subject using a computed tomography imaging system. The actions also include obtaining scan parameters for the scan. The actions further include automatically determining reconstruction matrix parameters, based at least on the clinical task and the scan parameters, for generating a reconstructed image from the tomographic data obtained from the scan of the subject.

[0007] In yet another embodiment, a non-transitory computer-readable medium includes processor-executable code that, when executed by a processor, causes the processor to perform actions. These actions include obtaining a clinical task of scanning a subject using a computed tomography imaging system. The actions also include obtaining scan parameters for the scan. Furthermore, the actions include automatically determining reconstruction matrix parameters, based at least on the clinical task and the scan parameters, for generating a reconstructed image from the tomographic data obtained from the scan of the subject. Attached Figure Description

[0008] These and other features, aspects, and advantages of the disclosed subject matter of the invention will be better understood when reading the following detailed description with reference to the accompanying drawings, in which like reference numerals denote like parts throughout the drawings, wherein:

[0009] Figure 1 It is a graphical representation of a CT imaging system (e.g., a LiDAR scanning system) according to various aspects of this disclosure;

[0010] Figure 2 Based on all aspects of this disclosure Figure 1 A block diagram of a CT imaging system;

[0011] Figure 3 It is a graphical representation of CT imaging systems and optical imaging systems according to various aspects of this disclosure;

[0012] Figure 4 This is a schematic diagram of a scanning chamber having a CT imaging system and an optical imaging system (e.g., a camera on a scanner) according to various aspects of this disclosure;

[0013] Figure 5 This is a flowchart of a method for reconstructing CT imaging data according to various aspects of this disclosure;

[0014] Figure 6 This is a flowchart of another method for reconstructing CT imaging data according to various aspects of this disclosure;

[0015] Figure 7 This is a flowchart of another method for reconstructing CT imaging data according to various aspects of this disclosure;

[0016] Figure 8 This is a flowchart of a method for determining a reconstructed field of view (e.g., using an initial reconstructed image) according to various aspects of this disclosure;

[0017] Figure 9 Examples of initial reconstructed CT images according to various aspects of this disclosure are depicted;

[0018] Figure 10 Examples of subsequent reconstructed CT images according to various aspects of this disclosure are depicted;

[0019] Figure 11 This is a flowchart of a method for determining a reconstructed field of view (e.g., a surface map using LiDAR data) according to various aspects of this disclosure;

[0020] Figure 12 An example depicting a 2D contour representation of a surface of a subject generated from LiDAR data according to various aspects of this disclosure;

[0021] Figure 13 This is a flowchart of a method for determining a reconstructed field of view (e.g., using body contours) according to various aspects of this disclosure;

[0022] Figure 14 An example of a lookup table for determining matrix size according to various aspects of this disclosure is depicted;

[0023] Figure 15 Examples of user interfaces or initial reconstructions utilizing automatic image size features, based on various aspects of this disclosure, are depicted; and

[0024] Figure 16 An example of a user interface for secondary reconstruction using automatic image size features, according to various aspects of this disclosure, is depicted. Detailed Implementation

[0025] One or more specific implementations will be described below. To provide a concise description of these implementations, not all features of an actual implementation will be described in this specification. It should be understood that in the development of any such actual implementation, as in any engineering or design project, many implementation-specific decisions must be made to achieve the developer's specific objectives, such as complying with system-related and business-related constraints that may differ from implementation to implementation. Furthermore, it should be understood that such development efforts may be complex and time-consuming, but remain routine tasks of design, fabrication, and manufacturing for those skilled in the art who benefit from this disclosure.

[0026] When describing elements of various embodiments of the subject matter of this invention, the articles “a,” “an,” “the,” and “described” are intended to indicate the presence of one or more elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that additional elements may be present in addition to the listed elements. Furthermore, any numerical examples in the following discussion are intended to be non-limiting, and therefore the additional values, ranges, and percentages are within the scope of the disclosed embodiments.

[0027] While the aspects discussed below are provided within the context of medical imaging, it should be understood that this technique is not limited to such a medical context. In fact, the examples and explanations provided in this medical context are merely for illustrative purposes by offering real-world examples of implementation and application. However, this method can also be used in other contexts, such as tomographic image reconstruction from industrial computed tomography (CT) used for non-destructive inspection of manufactured parts or finished products (i.e., quality control or quality audit applications) and / or non-invasive inspection of packages, boxes, luggage, etc. (i.e., security screening or screening applications). In general, this method can be used in any imaging or screening context to automatically provide optical matrix dimensions or image dimensions.

[0028] This disclosure provides systems and methods for automatically determining reconstruction matrix parameters used to generate reconstructed images of optimal size. Automatically determining the optimal image matrix size allows users to benefit from using larger matrix sizes while minimizing disk space utilization, thus optimizing resource utilization. Specifically, the systems and methods disclosed in this invention enable the use of higher resolution CT scanners without consistently generating large images. Furthermore, the disclosed systems and methods enable faster reconstruction. Moreover, the systems and methods disclosed in this invention improve both workflow and resource management without compromising image quality.

[0029] The system and method include obtaining a clinical task for scanning a subject using a computed tomography imaging system. In some embodiments, the clinical task is obtained through user input. In some embodiments, it is obtained from a hospital information system or a radiology information system. The system and method also include obtaining scan parameters for the scan. The system and method disclosed in this invention further include automatically determining reconstruction matrix parameters, at least based on the clinical task and scan parameters, for generating reconstructed images from the tomographic data obtained from the scan of the subject.

[0030] In some embodiments, automatically determining the reconstruction matrix parameters includes automatically determining the reconstruction field of view via a processor. In some embodiments, automatically determining the reconstruction field of view includes obtaining initial tomographic data of a subject using a computed tomography imaging system; performing full-field reconstruction on the initial tomographic data to generate an initial reconstructed image, wherein the resolution of the initial reconstructed image is lower than that of the reconstructed image generated using the reconstruction matrix parameters from the tomographic data (e.g., lower image quality) (typically the first reconstruction may be very fast to save time and use a smaller matrix size); and automatically determining the reconstruction field of view based on the initial reconstructed image.

[0031] In some implementations, automatically determining the reconstructed field of view includes obtaining LiDAR data of the subject at the processor, acquired using an optical detection and ranging (LiDAR) scanning system on a gantry coupled to a computed tomography imaging system; generating a surface map of the subject based on the LiDAR data via the processor; and automatically determining the reconstructed field of view via the processor based on the surface map.

[0032] In some implementations, automatically determining the reconstructed field of view includes obtaining imaging data of a subject at a processor using a three-dimensional camera attached to a gantry of a computed tomography imaging system; generating a body contour of the subject based on the imaging data via the processor; and automatically determining the reconstructed field of view via the processor based on the body contour.

[0033] In some implementations, automatically determining the reconstruction matrix parameters includes automatically determining the matrix size via a processor, based at least on the clinical task, scan parameters, and the reconstructed field of view. In some implementations, automatically determining the matrix size includes determining the matrix size via a processor using a lookup table.

[0034] In some embodiments, the systems and methods disclosed in this invention include obtaining additional selected parameters affecting the matrix size at a processor, wherein the matrix size is automatically determined based on a clinical task, scan parameters, reconstructed field of view, and the additional selected parameters. In some embodiments, automatically determining the matrix size includes calculating the matrix size via a processor based on one or more of the scan parameters and the additional selected parameters. In some embodiments, the additional selected parameters are obtained via user input. In some embodiments, the additional selected parameters are automatically determined via a processor based on an obtained clinical task. In some embodiments, the additional selected parameters include a reconstruction kernel, iterative reconstruction, and a post-processing filter.

[0035] In some embodiments, the systems and methods disclosed in this invention include automatically updating the reconstruction strategy to include the reconstructed field of view and matrix size. In some embodiments, the systems and methods disclosed in this invention include generating a reconstructed image using the updated reconstruction strategy.

[0036] Taking into account the foregoing and referring to Figure 1 and Figure 2 A CT imaging system 10 is illustrated by way of example. The CT imaging system 10 includes a gantry 12 coupled to a housing 13 (e.g., a gantry housing). The gantry 12 has rotating and stationary components. The gantry 12 has an X-ray source 14 that projects a beam of X-rays 16 toward an X-ray detector assembly or X-ray detector array 15 (e.g., having multiple detector modules) on the opposite side of the gantry 12. The X-ray source 14 and the X-ray detector assembly 15 are disposed on the rotating portion of the gantry 12. The X-ray detector assembly 15 is coupled to a data acquisition system (DAS) 33. The multiple detector modules of the X-ray detector assembly 15 detect the projected X-rays passing through a patient or subject 22 (disposed on a support 23 of a worktable 36), and the DAS 33 converts this data into a digital signal for subsequent processing. Each detector module of the X-ray detector assembly 15 in a conventional system generates an analog electrical signal representing the intensity of the incident X-ray beam and therefore the intensity of the attenuated beam as the incident X-ray beam passes through the patient 22. During the scan to acquire X-ray projection data, the gantry 12 and the components mounted thereon rotate about a rotation center 24 (e.g., an isocenter) to collect attenuation data from multiple viewpoints relative to the imaging volume.

[0037] The rotation of the gantry 12 and the operation of the X-ray source 14 are controlled by the control mechanism 26 of the CT imaging system 10. The control mechanism 26 includes an X-ray controller 28 and a gantry motor controller 30. The X-ray controller provides power and timing signals to the X-ray source 14, and the gantry motor controller controls the rotation speed and position of the gantry 12.

[0038] In some embodiments, the imaging system 10 also includes a light detection and ranging (LiDAR) scanning system 32 physically coupled to the imaging system 10. The LiDAR scanning system 32 includes one or more LiDAR scanners or instruments 34. As depicted, the LiDAR scanning system 32 has a LiDAR scanner 34. One or more LiDAR scanners 34 are used to acquire depth-related information (LiDAR data or light images) of the patient 22 with high spatial fidelity. The depth-related information is utilized in subsequent workflows for CT scanning. The one or more LiDAR scanners 34 emit pulsed light 35 (e.g., laser) at the patient 22 and detect the pulsed light reflected from the patient 22. The LiDAR scanning system 32 is configured to acquire LiDAR data from multiple different views (e.g., at different angular positions relative to the rotation axis 24).

[0039] In some implementations, such as Figure 1 and Figure 2 As depicted, a LiDAR scanner 34 is coupled to a rack 12. Specifically, the LiDAR scanner 34 is positioned outside the scanning window within the rack housing 13. The LiDAR scanner 34 rotates across the patient 22 to acquire LiDAR data at different angular positions. In some embodiments, multiple LiDAR scanners 34 may be coupled to the rack 12 and rotate to acquire LiDAR data at different angular positions.

[0040] In some implementations, multiple LiDAR scanners 34 may be coupled to the rack 12 in a fixed position, but positioned at different angular locations (e.g., relative to the axis of rotation 24). The LiDAR scanners 34 in the fixed position can acquire LiDAR data while remaining stationary.

[0041] In some embodiments, the LiDAR scanning system 32 may be external to the rack 12 but still physically coupled to the imaging system 10. For example, multiple LiDAR scanners 34 may be coupled to a LiDAR panel (e.g., at different angular positions relative to the axis of rotation 24), which is coupled to a guide rail system. The guide rail system may be coupled to the rack housing 13 or the stage 36 of the system 10. The guide rail system may be configured to move the LiDAR panel toward and away from the rack 12. In some embodiments, the guide rail system may also be configured to rotate the LiDAR panel about the axis of rotation 24.

[0042] The LiDAR scanning system 32 includes a LiDAR controller 38 configured to provide timing and control signals to one or more LiDAR scanners 34 for acquiring LiDAR data at different angular locations. LiDAR data can be acquired before, during, and / or after a CT scan of the patient 22. The LiDAR scanning system 32 also includes a LiDAR data processing unit 40 that receives or acquires LiDAR data from one or more LiDAR scanners 34. The LiDAR data processing unit 40 utilizes the time-of-flight information of the reflected pulsed light and processes the LiDAR data (e.g., acquired in different views) to generate accurate 3D measurements of the patient 22. The 3D measurements of the patient 22 have high spatial resolution (e.g., sub-millimeter accuracy). As described above, the 3D measurements can be utilized in subsequent workflows of the CT scan, as described in more detail below.

[0043] 3D measurement information from the LiDAR scanning system 32 (e.g., from the LiDAR data processing unit 40) and scan data from the DAS 33 are input to the computer 42. The computer 42 also includes a data correction unit 46 for processing or correcting CT scan data from the DAS 33. The computer 42 also includes an image reconstructor 48. The image reconstructor 48 receives sampled and digitized X-ray data from the DAS 33 and performs high-speed reconstruction. The reconstructed image is applied as input to the computer 42, which stores the image in a mass storage device 50. The computer 42 also receives commands and scan parameters from the operator via a console 52. An associated display 54 allows the operator to view the reconstructed image, as well as the 3D measurement data and other data from the computer 42. The computer 42 uses the commands and parameters provided by the operator to provide control signals and information to the DAS 33, X-ray controller 28, gantry motor controller 30, and LiDAR controller 38. In addition, computer 42 operates worktable motor controller 56, which controls electric worktable 36 to position patient 22 relative to rack 12. Specifically, worktable 36 (via support 23 supporting patient 22) moves parts of patient 22 through rack openings or apertures 58.

[0044] Both the computer 42 and the LiDAR processing unit 40 may each include processing circuitry. The processing circuitry may be one or more general-purpose or special-purpose microprocessors. The processing circuitry may be configured to execute instructions stored in memory to perform various actions. For example, the processing circuitry may be used to receive or acquire LiDAR data obtained by the LiDAR scanning system 32. Furthermore, the processing circuitry may generate 3D measurements of the patient 22. Additionally, the processing circuitry may utilize the 3D measurements in subsequent workflow processes used for CT scans of the patient performed by the CT imaging system 32.

[0045] In some embodiments, the CT imaging system 10 includes, for example: Figure 3 The optical imaging system 53 depicted replaces the LiDAR scanning system. The optical imaging system 53 may include one or more cameras or sensors 55. In some embodiments, the camera or sensor 55 includes a three-dimensional (3D) camera configured to acquire imaging data of the patient 22, which is used to generate or determine the patient's body contours. In some embodiments, one or more cameras 55 may be positioned as follows: Figure 4 The top of the housing of the gantry 12 of the CT imaging system 10 is depicted. In some embodiments, the camera 55 may be directly coupled to the gantry 12.

[0046] The processing circuitry of the CT imaging system 10 is configured to acquire a clinical task for scanning a patient using the CT imaging system 10. In some embodiments, the clinical task is acquired through user input. In some embodiments, it is acquired from a hospital information system or a radiology information system. The processing circuitry of the CT imaging system 10 is also configured to acquire scan parameters for the scan. The processing circuitry of the CT imaging system 10 is further configured to automatically determine reconstruction matrix parameters, which are used to generate a reconstructed image based on tomographic data obtained from the patient using the scan, at least based on the clinical task and the scan parameters.

[0047] In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically determine the reconstruction matrix parameters by automatically determining the reconstruction field of view. In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically determine the reconstruction field of view by acquiring initial tomographic data of the subject using a computed tomography imaging system. In some embodiments, the processing circuitry of the CT imaging system 10 is configured to perform full-field reconstruction on the initial tomographic data to generate an initial reconstructed image, wherein the resolution of the initial reconstructed image is lower than that of the reconstructed image generated using the reconstruction matrix parameters from the tomographic data (e.g., lower image quality). In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically determine the reconstruction field of view based on the initial reconstructed image.

[0048] In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically determine the reconstructed field of view by acquiring LiDAR data of the subject using a LiDAR scanning system coupled to a gantry of the computed tomography imaging system. In some embodiments, the processing circuitry of the CT imaging system 10 is configured to generate a surface map of the subject based on the LiDAR data. In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically determine the reconstructed field of view based on the surface map.

[0049] In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically determine the reconstructed field of view by acquiring imaging data of the subject at a processor using a three-dimensional camera coupled to the gantry of the computed tomography imaging system. In some embodiments, the processing circuitry of the CT imaging system 10 is configured to generate the subject's body contour based on the imaging data. In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically determine the reconstructed field of view based on the body contour.

[0050] In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically determine the reconstruction matrix parameters by automatically determining the matrix size based at least on the clinical task, scan parameters, and reconstructed field of view. In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically determine the matrix size by utilizing a lookup table to determine the matrix size.

[0051] In some embodiments, the processing circuitry of the CT imaging system 10 is configured to obtain additional selected parameters that affect the matrix size, wherein the matrix size is automatically determined based on the clinical task, scan parameters, reconstructed field of view, and additional selected parameters. In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically determine the matrix size by calculating the matrix size based on one or more of the scan parameters and additional selected parameters. In some embodiments, the additional selected parameters are obtained through user input. In some embodiments, the additional selected parameters are automatically determined via a processor based on the obtained clinical task. In some embodiments, the additional selected parameters include a reconstructed kernel, iterative reconstruction, and a post-processing filter.

[0052] In some embodiments, the processing circuitry of the CT imaging system 10 is configured to automatically update the reconstruction strategy to include the reconstruction field of view and matrix size. In some embodiments, the processing circuitry of the CT imaging system 10 is configured to generate a reconstructed image using the updated reconstruction strategy.

[0053] Figure 5 This is a flowchart of method 60 for reconstructing CT imaging data. Method 60 can be derived from... Figures 1 to 3 One or more components (e.g., processing circuitry) of the CT imaging system 10 in the system perform the operation. This can be performed simultaneously and / or in a manner different from [the previous operation]. Figure 5 One or more steps of the sequential execution method 60 shown can be executed automatically (and in some cases, all steps can be executed automatically).

[0054] Method 60 includes obtaining / determining a clinical task for scanning a subject (e.g., a patient) using a CT imaging system (box 62). In some embodiments, the clinical task is obtained (e.g., received) through user input. In some embodiments, the clinical task is obtained (e.g., acquired) from a hospital information system or a radiology information system. In some embodiments, the purpose of the clinical task is to scan the subject (e.g., detect lesions, assess the vascular system, detect fractures, etc.).

[0055] Method 60 also includes obtaining scan parameters for scanning (box 64). Examples of scan parameters include kVp, mA, rotation time, and pitch. Method 60 also includes automatically determining reconstruction matrix parameters, which are used to generate reconstructed images based on acquired tomographic data from the subject, at least based on the clinical task and scan parameters (box 66). In some embodiments, automatically determining the reconstruction matrix parameters includes automatically determining the reconstruction field of view (i.e., how much of the scan field of view is reconstructed into the image). In some embodiments, the reconstruction field of view is determined using the subject's body contours determined by a 3D camera. In some embodiments, the reconstruction field of view is determined using a surface map of the subject derived from acquired LiDAR data. In some embodiments, the reconstruction field of view is determined based on an initial reconstructed image derived from initial tomographic data (whose resolution, fidelity, or image quality is lower than that of the subsequent reconstructed image to be obtained).

[0056] In some embodiments, automatically determining the reconstruction matrix parameters includes automatically determining the matrix size based at least on the clinical task, scan parameters, and reconstructed field of view. In some embodiments, automatically determining the matrix size includes using a lookup table to determine the matrix size. In some embodiments, the matrix size is automatically determined based on at least one or more scan parameters.

[0057] Method 60 further includes automatically updating the reconstruction strategy to include the reconstruction field of view and matrix (box 68). Method 60 also includes generating a reconstructed image using the updated reconstruction strategy (box 70).

[0058] Figure 6 This is a flowchart of another method 72 for reconstructing CT imaging data. Method 72 can be derived from... Figures 1 to 3 One or more components (e.g., processing circuitry) of the CT imaging system 10 in the system perform the operation. This can be performed simultaneously and / or in a manner different from [the previous operation]. Figure 6 One or more steps of the sequential execution method 72 are shown. One or more steps of method 72 can be executed automatically (and in some cases, all steps can be executed automatically).

[0059] Method 72 includes obtaining / determining a clinical task for scanning a subject (e.g., a patient) using a CT imaging system (box 74). In some embodiments, the clinical task is obtained (e.g., received) through user input. In some embodiments, the clinical task is obtained (e.g., acquired) from a hospital information system or a radiology information system. In some embodiments, the purpose of the clinical task is to scan the subject (e.g., detect lesions, assess the vascular system, detect fractures, etc.).

[0060] Method 72 also includes obtaining scan parameters for scanning (box 76). Examples of scan parameters include kVp, mA, rotation time, and pitch.

[0061] Method 72 also includes obtaining (e.g., receiving) additional selected (e.g., user-selected) parameters that affect the matrix size (box 78). Examples of additional selected parameters that affect the matrix size include reconstruction kernels, iterative reconstruction, and post-processing filters. In some embodiments, the additional selected parameters are obtained via user input.

[0062] Method 72 even includes automatically determining (e.g., selecting) the reconstructed field of view (box 80). In some embodiments, the reconstructed field of view is determined using the subject's body contours determined by a 3D camera. In some embodiments, the reconstructed field of view is determined using a surface map of the subject derived from acquired LiDAR data. In some embodiments, the reconstructed field of view is determined based on an initial reconstructed image derived from initial tomographic data (whose resolution, fidelity, or image quality is lower than that of the subsequent reconstructed image to be obtained).

[0063] Method 72 further includes automatically determining (e.g., selecting) the matrix size based at least on the clinical task, scan parameters, reconstructed field of view, and additional selected parameters (box 82). In some embodiments, automatically determining the matrix size includes using a lookup table. In some embodiments, the matrix size is automatically determined based on at least one or more of the scan parameters and additional selected parameters.

[0064] Method 72 further includes automatically updating the reconstruction strategy to include the reconstruction field of view and matrix (box 84). Method 72 also includes generating a reconstructed image using the updated reconstruction strategy (box 86). In some embodiments, method 72 includes receiving user input to change the reconstruction strategy after it has been updated (box 88). This allows the user to manually change automatically determined reconstruction matrix parameters (e.g., reconstruction field of view or matrix size) as needed.

[0065] Figure 7 This is a flowchart of another method 90 for reconstructing CT imaging data. Method 72 can be derived from... Figures 1 to 3One or more components (e.g., processing circuitry) of the CT imaging system 10 in the system perform the operation. This can be performed simultaneously and / or in a manner different from [the previous operation]. Figure 7 One or more steps of the sequential execution method 90 are shown. One or more steps of method 90 can be executed automatically (and in some cases, all steps can be executed automatically).

[0066] Method 90 includes obtaining / determining a clinical task for scanning a subject (e.g., a patient) using a CT imaging system (box 92). In some embodiments, the clinical task is obtained (e.g., received) through user input. In some embodiments, the clinical task is obtained (e.g., acquired) from a hospital information system or a radiology information system. In some embodiments, the purpose of the clinical task is to scan the subject (e.g., detect lesions, assess the vascular system, detect fractures, etc.).

[0067] Method 90 also includes obtaining scan parameters for scanning (box 94). Examples of scan parameters include kVp, mA, rotation time, and pitch. Method 90 also includes automatically determining (e.g., selecting) additional selected parameters that affect matrix size based on the obtained clinical task (box 96). Examples of additional selected parameters that affect matrix size include reconstruction kernel, iterative reconstruction, and post-processing filters.

[0068] Method 90 even includes automatically determining (e.g., selecting) the reconstructed field of view (box 98). In some embodiments, the reconstructed field of view is determined using the subject's body contours determined by a 3D camera. In some embodiments, the reconstructed field of view is determined using a surface map of the subject derived from acquired LiDAR data. In some embodiments, the reconstructed field of view is determined based on an initial reconstructed image derived from initial tomographic data (whose resolution, fidelity, or image quality is lower than that of the subsequent reconstructed image to be obtained).

[0069] Method 90 further includes automatically determining (e.g., selecting) the matrix size (box 100) based at least on the clinical task, scan parameters, reconstructed field of view, and additional selected parameters. In some embodiments, automatically determining the matrix size includes using a lookup table. In some embodiments, the matrix size is automatically determined based on at least one or more of the scan parameters and additional selected parameters.

[0070] Method 90 further includes automatically updating the reconstruction strategy to include the reconstruction field of view and matrix (box 102). Method 90 also includes generating a reconstructed image using the updated reconstruction strategy (box 104). In some embodiments, method 90 includes receiving user input to change the reconstruction strategy after it has been updated (box 106). This allows the user to manually change automatically determined reconstruction matrix parameters (e.g., reconstruction field of view or matrix size) as needed.

[0071] Figure 8 This is a flowchart of another method 108 for determining the reconstructed field of view (e.g., using the initial reconstructed image). Method 108 can be derived from... Figures 1 to 3 One or more components (e.g., processing circuitry) of the CT imaging system 10 in the system perform the operation. This can be performed simultaneously and / or in a manner different from [the previous operation]. Figure 8 One or more steps of the sequential execution method 108 are shown. One or more steps of method 108 can be executed automatically (and in some cases, all steps can be executed automatically).

[0072] Method 108 includes acquiring (e.g., acquiring) initial tomographic data of the subject using a CT imaging system (box 110). Method 108 also includes performing full-field reconstruction on the initial tomographic data to generate an initial reconstructed image (box 112). The resolution of the initial reconstructed image is higher than that obtained using the tomographic data, as shown in the example below. Figure 5 The automatically determined matrix parameters described in method 60 result in low (e.g., low fidelity or low image quality) reconstructed images generated during subsequent scans. The initial reconstructed image is not shown to the user. Figure 9 An example depicting the initial reconstructed image 114 of the subject (e.g., a low-resolution reconstructed image). Figure 10 An example of a reconstructed image 116 of a subject (e.g., a high-resolution reconstructed image) is depicted, based on tomographic data obtained during subsequent scans using automatically determined matrix parameters. Return to Figure 8 Method 108 also includes automatically determining the reconstructed field of view based on the initial reconstructed image (box 118). For example, in some embodiments, a computer algorithm may customize the patient size of the reconstructed field of view based on the initial reconstructed image.

[0073] Figure 11 This is a flowchart of another method 120 for determining a reconstructed field of view (e.g., a surface map using LiDAR data). Method 120 can be derived from... Figures 1 to 3 One or more components (e.g., processing circuitry) of the CT imaging system 10 in the system perform the operation. This can be performed simultaneously and / or in a manner different from [the previous operation]. Figure 11 One or more steps of method 120 are executed sequentially as shown. One or more steps of method 120 can be executed automatically (and in some cases, all steps can be executed automatically).

[0074] Method 120 includes acquiring LiDAR data of the subject using a LiDAR scanning system coupled to a gantry of a CT imaging system (box 122). Method 120 also includes generating a surface map of the subject based on the LiDAR data (box 124). Figure 12An example 126 depicts a 2D contour representation 126 of the surface of a region of interest generated from LiDAR data for the subject. Method 120 also includes automatically determining the reconstructed field of view based on the surface map (box 128).

[0075] Figure 13 This is a flowchart of another method 130 for determining the reconstructed field of view (e.g., using body contours). Method 130 can be derived from... Figures 1 to 3 One or more components (e.g., processing circuitry) of the CT imaging system 10 in the system perform the operation. This can be performed simultaneously and / or in a manner different from [the previous operation]. Figure 13 One or more steps of method 130 are executed sequentially as shown. One or more steps of method 130 can be executed automatically (and in some cases, all steps can be executed automatically).

[0076] Method 130 includes acquiring imaging data of a subject using a 3D camera coupled to a gantry of a CT imaging system (box 132). Method 130 also includes generating a body contour of the subject based on the imaging data (box 134). Method 130 further includes automatically determining a reconstructed field of view based on the body contour (box 136).

[0077] As described above, in some implementations, automatically determining the matrix size includes using a lookup table to determine (e.g., select) the matrix size. Figure 14 An example of a lookup table 138 for determining matrix size is depicted. As depicted, the matrix size is determined using the scan mode type, kernel type, and reconstructed field of view on lookup table 138. In some embodiments, the matrix size may be determined using other parameters in conjunction with the lookup table. For example, other parameters may include focus and / or slice thickness. Alternatively, in some embodiments, automatic determination of matrix size includes calculating the matrix size via a processor based on one or more of scan parameters and additional selected parameters. For example, the configuration of the reconstructed kernel, combined with the determined reconstructed field of view, determines the cutoff frequency to determine (e.g., select) the matrix size.

[0078] Automatic image size features can be used for both primary reconstruction (e.g., axial image from a scan) and secondary reconstruction (e.g., reconstructed orthogonal image from the primary reconstruction). Figure 15 A user interface 140 is depicted for initial reconstruction using automatic image size features. As depicted, arrow 142 indicates the portion of the user interface that enables automatic image size features and automatically determined matrix dimensions. Figure 16 A user interface 144 for secondary reconstruction using automatic image size features is depicted. As depicted, arrow 146 indicates a portion of the user interface that indicates enabling the automatic image size features and automatically determined matrix dimensions. In some embodiments, the user can override the automatic image size features by manually selecting the matrix dimensions.

[0079] The technical effects of the embodiments disclosed in this invention include automatically determining reconstruction matrix parameters used to generate reconstructed images of optimal size. The technical effects of the embodiments disclosed in this invention include enabling users to benefit from using larger matrix sizes while minimizing disk space utilization, thus optimizing resource utilization. The technical effects of the embodiments disclosed in this invention also include enabling the use of higher resolution CT scanners without consistently generating large images. The technical effects of the embodiments disclosed in this invention also include enabling rapid reconstruction. The technical effects of the embodiments disclosed in this invention even include improving both workflow and resource management without compromising image quality.

[0080] Referring to the technology presented herein and protected by the claims, and applying it to physical objects and concrete examples of practical nature, which explicitly improves the present art, and therefore is not abstract, intangible, or purely theoretical. Furthermore, if any claim appended to the end of this specification contains one or more elements designated as “means for [performing]…” or “steps for [performing]…”, such elements are intended to be interpreted according to 35U.SC112(f). However, for any claim containing elements designated in any other manner, such elements are not intended to be interpreted according to 35U.SC112(f).

[0081] This written description uses examples to disclose the subject matter, including best practices, and also enables those skilled in the art to practice the subject matter, including making and using any device or system and performing any included methods. The patent scope of this subject matter is defined by the claims and may include other examples that would occur to those skilled in the art. Such other examples are intended to fall within the scope of the claims if they have structural elements that are not indistinguishable from the literal language of the claims, or if they include equivalent structural elements that have minor differences from the literal language of the claims.

Claims

1. A computer-implemented method, the computer-implemented method comprising: The clinical task of scanning the subject using a computed tomography imaging system is obtained at the processor. The scan parameters for the scan are obtained at the processor; as well as The processor automatically determines reconstruction matrix parameters based at least on the clinical task and the scan parameters, the reconstruction matrix parameters being used to generate a reconstructed image from the tomographic data obtained by the subject using the scan.

2. The computer-implemented method of claim 1, wherein automatically determining the reconstruction matrix parameters includes automatically determining the reconstructed field of view via the processor.

3. The computer-implemented method according to claim 2, further comprising: Initial tomographic data of the subject are obtained at the processor using the computed tomography imaging system. The processor performs full-field reconstruction on the initial tomographic data to generate an initial reconstructed image, wherein the resolution of the initial reconstructed image is lower than that of the reconstructed image generated from the tomographic data using the reconstruction matrix parameters; as well as The processor automatically determines the reconstructed field of view based on the initial reconstructed image.

4. The computer-implemented method according to claim 2, further comprising: The processor acquires LiDAR data of the subject using a light detection and ranging (LiDAR) scanning system coupled to the gantry of the computed tomography imaging system. The processor generates a surface map of the subject based on the LiDAR data; as well as The processor automatically determines the reconstructed field of view based on the surface map.

5. The computer-implemented method according to claim 2, further comprising: Imaging data of the subject acquired by a three-dimensional camera using a gantry coupled to the computed tomography imaging system is obtained at the processor. The processor generates the subject's body contour based on the imaging data; as well as The processor automatically determines the reconstructed field of view based on the body contour.

6. The computer-implemented method of claim 2, wherein automatically determining the reconstruction matrix parameters comprises automatically determining the matrix size via the processor based at least on the clinical task, the scan parameters, and the reconstruction field of view.

7. The computer-implemented method of claim 6, wherein automatically determining the matrix size includes determining the matrix size via a lookup table using the processor.

8. The computer-implemented method of claim 6, further comprising obtaining additional selected parameters affecting the matrix size at the processor, wherein the matrix size is automatically determined based on the clinical task, the scan parameters, the reconstructed field of view, and the additional selected parameters.

9. The computer-implemented method of claim 8, wherein automatically determining the matrix size comprises calculating the matrix size via the processor based on one or more of the scan parameters and the additional selected parameters.

10. The computer-implemented method of claim 8, wherein the additional selected parameter is obtained at the processor via user input.

11. The computer-implemented method of claim 8, wherein the additional selected parameters are automatically determined via the processor based on the obtained clinical task.

12. The computer-implemented method of claim 8, wherein the additional selected parameters include a reconstructed kernel, iterative reconstruction, and a post-processing filter.

13. The computer-implemented method according to claim 6, further comprising: The reconstruction strategy is automatically updated via the processor to include the reconstructed field of view and the matrix size; as well as The processor generates a reconstructed image using the updated reconstruction strategy.

14. The computer-implemented method of claim 1, wherein the clinical task is obtained at the processor via user input.

15. The computer-implemented method of claim 1, wherein the clinical task from the hospital information system or the radiology information system is obtained at the processor.

16. A system comprising: A memory that encodes processor-executable routines; and A processor configured to access the memory and configured to execute processor-executable routines, wherein the processor-executable routines, when executed by the processor, cause the processor to: The clinical task was to scan the subject using a computed tomography imaging system. Obtain the scan parameters used for the scan; and Reconstruction matrix parameters are automatically determined based at least on the clinical task and the scan parameters, the reconstruction matrix parameters being used to generate reconstructed images from the tomographic data obtained by the subject using the scan.

17. The system of claim 16, wherein automatically determining the reconstruction matrix parameters includes automatically determining the reconstruction field of view.

18. The system of claim 17, wherein automatically determining the reconstruction matrix parameters includes automatically determining the matrix size based at least on the clinical task, the scan parameters, and the reconstruction field of view.

19. A non-transitory computer-readable medium comprising processor-executable code, the processor-executable code causing the processor, when executed by a processor, to: The clinical task was to scan the subject using a computed tomography imaging system. Obtain the scan parameters used for the scan; and Reconstruction matrix parameters are automatically determined based at least on the clinical task and the scan parameters, the reconstruction matrix parameters being used to generate reconstructed images from the tomographic data obtained by the subject using the scan.

20. The non-transitory computer-readable medium of claim 19, wherein automatically determining the reconstruction matrix parameters includes automatically determining the reconstruction field of view and automatically determining the matrix size based at least on the clinical task, the scanning parameters, and the reconstruction field of view.

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