Systems and methods for direct multiplanar reformatting of images from an imaging system

Abstract

Methods and systems for performing direct multiplanar reformatting tasks are described. An example method includes acquiring an image of a subject via a medical imaging system, automatically straightening the image via a direct multiplanar reformatting module based on a transformation matrix, and applying the transformation matrix to a plurality of images having corresponding orientations of the image via the direct multiplanar reformatting module.

Description

Cross-reference to related applications

[0001] This application claims the benefit and priority of U.S. Provisional Application No. 63 / 594,278, filed on October 30, 2023, the disclosure of which is incorporated herein by reference in its entirety. Technical Field

[0002] This disclosure relates to medical imaging, and more particularly to methods and systems for automating multiplanar reconstruction of medical imaging acquisition. Background Technology

[0003] Not all patients can lie upright on the scanning table, especially trauma and acute care patients who may have kyphosis or be positioned such that at least part of their body is at an angle relative to the scanning table. When using current multiplanar reconstruction (MPR) or direct multiplanar rearrangement (DMPR) methods to straighten patient datasets, there is no multi-tilt option. Each fine slice dataset of the same series (bone, soft tissue, lung) requires manual straightening and batch processing. Therefore, current MPR or DMPR processes can require up to 300 clicks to complete a trauma reconstruction, which is time-consuming for technicians or radiologists and can take up to 30 minutes to complete. Additionally, the reconstructed angles across different algorithms within the same area of ​​the body are not consistent, making reporting challenging because multiple algorithms cannot be directly compared. Current processes require complex training and consistency. Summary of the Invention

[0004] In view of the above-mentioned technical problems, embodiments of this application provide a method for performing a direct multiplane rearrangement task in a medical imaging system. An example method includes acquiring an image of a subject via a medical imaging system, automatically straightening the image based on a transformation matrix via a direct multiplane rearrangement module, and applying the transformation matrix to multiple images having the corresponding orientation of the image via the direct multiplane rearrangement module.

[0005] An example medical imaging system for performing a direct multiplanar rearrangement task includes an X-ray source and a detector, wherein the X-ray source and detector acquire images of a subject. The medical imaging system also includes a computing device communicating with a mass storage device to store and retrieve images of the subject, and a direct multiplanar rearrangement module coupled to the computing device. The direct multiplanar rearrangement module is configured to retrieve the image of the subject acquired by the X-ray source and detector via computation, automatically straighten the image based on a transformation matrix, and apply the transformation matrix to multiple images having the corresponding orientation of the image.

[0006] Another example method includes: retrieving an image of the subject from a mass storage device; retrieving a transformation matrix associated with a direct multiplane rearrangement task associated with the image; and applying the transformation matrix to multiple images having the corresponding orientation of the image via a direct multiplane rearrangement module. Attached Figure Description

[0007] The accompanying drawings included in this application are intended to help further understand the embodiments of this application and form part of the specification. They, together with the textual description, are used to explain the specific implementations of this application and to elucidate its principles. Obviously, the drawings described below are merely some embodiments of this application. For those skilled in the art, other specific implementations can be obtained from these drawings without any inventive effort. In the drawings:

[0008] Figure 1 A perspective view of a CT imaging system according to an embodiment of the present disclosure is shown.

[0009] Figure 2 A block diagram of a CT imaging system according to an embodiment of the present disclosure is shown.

[0010] Figure 3 It is a flowchart of a method for direct multiplane rearrangement workflows that can be executed during protocol management.

[0011] Figure 4 This is a flowchart of a method for direct multiplane rearrangement workflows that can be executed at scan time.

[0012] Figure 5 This is a flowchart of a method for a direct multiplane rearrangement workflow that can be executed after acquiring scanned images.

[0013] Figure 6 This is an example dashboard presented to the user that indicates the multiplanar rearrangement task available for imaging scans.

[0014] Figure 7 This describes a sample dashboard presented to the user when a multiplane rearrangement task is initiated.

[0015] Figure 8 An example dashboard depicting an alignment component used to perform multi-plane rearrangement tasks is presented to the user.

[0016] Figure 9 This describes another example dashboard presented to the user when aligning components are performing a direct multiplane rearrangement task.

[0017] Figure 10 This describes a sample dashboard presented to the user when a straightening component performs a direct multiplane rearrangement task.

[0018] Figure 11 This describes a sample dashboard presented to the user when performing a geometry selection component in a direct multiplane rearrangement task.

[0019] Figure 12 This describes a sample dashboard presented to the user when reviewing and selecting components during a direct multiplane rearrangement task.

[0020] Figure 13 This describes a sample dashboard presented to the user after the changes made during the direct multiplane rearrangement task are applied.

[0021] It is anticipated that elements in one embodiment of this disclosure may be advantageously applied to other embodiments without further explanation. Detailed Implementation

[0022] The disclosure and techniques described herein provide a workflow for automating multiplanar reconstruction. Direct multiplanar rearrangement (DMPR) or multiplanar reconstruction (MPR) is created from image acquisition, and technicians or radiologists can typically create subsets, such as the face and head, from a single acquisition. As used herein, direct multiplanar rearrangement (DMPR) can also be used to include multiplanar reconstruction. Within a patient's body, there are different regions (e.g., chest, abdomen, pelvis) for which separate, specific image series can be created or generated. Multiplanar images or image reconstruction include the ability to transform views in different planes (e.g., axial, sagittal, coronal). Direct multiplanar rearrangement (DMPR) allows users to move from a typical 2D image review mode to a prospective 3D image review mode in axial, sagittal, coronal, and oblique planes. Typically, this type of reconstruction process requires significant work to create artificial intelligence (AI) or deep learning (DL) algorithms for body regions to help manipulate anatomical structures, such as straightening (automatic view of the spine, automatic view of the head), or requires very tedious manual work by technicians or radiologists to physically manipulate anatomical structures. Previously, this manipulation had to be performed individually for each rebuild. In some cases, this manipulation and manual work was estimated to exceed 45 to 300 clicks, or sometimes more, and could take more than 40 minutes. This manual work could be very large and time-consuming if a large number of DMPRs and MPRs were being processed and created. Additionally, the process might require multiple applications.

[0023] The novel method and system disclosed herein batch all these tasks into bundled steps, reducing the amount of time technicians or radiologists spend manipulating images—potentially as few as eight clicks and processing time as short as three to four minutes. Users can also automatically create batch rearrangements using predefined rearrangement protocols and connect the rearranged image networks to selected reading locations, thereby reducing overall examination time and increasing productivity. Additionally, the orientation task allows users to graphically define transformation matrices from images (e.g., defining a matrix for the rotation angle used to straighten subsequent images) and automatically apply them to one or more DMPRs. DMPR images can be anatomically oriented to the technician, with the anterior side at the top, the posterior side at the bottom, the right side on the left, and the left side on the right. For example, if the dataset comes from image acquisitions in which a patient is scanned in a prone position, the display will show this orientation of the images.

[0024] The concept disclosed herein is to create new tasks or operations for users that allow technicians or radiologists to manipulate images and apply the manipulation across any number of DMPR / MPR output image series. Within the task, the user will set a title for the task (i.e., face orientation, head orientation, brain orientation, neck orientation, etc.), be able to enter free text or instructional text in the annotation field (i.e., increase compliance, improve efficiency, reduce the recall / cognitive load required to remember the steps for the image view), be able to choose between manual manipulation and algorithms (thus allowing both system-guided orientation / computer straightening / segmentation and fully manual manipulation), be able to select the graphics view port and manipulate the image, be able to select the DMPR / MPR reconstruction (1 to n) to apply the transformation to, and finally be able to apply / confirm / run the transformation. The proposed concept also allows manipulation of algorithmic outputs and batch sending of outputs to multiple locations. This reduces the differences between different images due to human error or variation in the case of manual manipulation. Implementations of this specification and the subject matter disclosed herein can relate to phantoms for calibration scans of imaging systems such as photon-counting computed tomography (PCCT) systems. However, the methods and systems described herein for direct multiplanar rearrangement can be used with other imaging systems, including but not limited to conventional computed tomography (CT) imaging systems, magnetic resonance imaging (MRI) systems, positron emission tomography (PET) imaging systems, single-photon emission computed tomography (SPECT) imaging systems, and / or combinations of imaging systems.

[0025] Figure 1An exemplary PCCT system 100 (also referred to as a photon-counting X-ray imaging system) configured for CT imaging using a photon-counting detector is illustrated. Specifically, the PCCT system 100 is configured to image a subject 112 (such as a patient, inanimate object, one or more manufactured parts) and / or foreign objects (such as dental implants, stents, and / or contrast agents present in the body). The PCCT system 100 includes a gantry 102, which may further include at least one X-ray source 104 configured to project an X-ray radiation beam 106 (see [link to documentation]). Figure 2 An X-ray source 104 is configured to project an X-ray radiation beam 106 toward a detector array 108 positioned on the opposite side of the gantry 102, used to image a subject 112 lying on an examination table 114. Specifically, the X-ray source 104 is configured to project an X-ray radiation beam 106 toward a detector array 108 positioned on the opposite side of the gantry 102. Although Figure 1 A single X-ray source 104 is depicted, but in some embodiments, multiple X-ray sources and detectors may be employed to project multiple X-ray radiation beams for acquiring projection data at the same or different energy levels corresponding to the patient. In some embodiments, the X-ray source 104 can achieve dual-spectrum imaging via rapid peak kilovolt (kVp) voltage switching. In the embodiments described herein, the X-ray detector employed is a photon counting detector capable of distinguishing X-ray photons of different energies.

[0026] In some embodiments, the PCCT system 100 also includes an image processor unit 110 configured to reconstruct an image of the target volume of the subject 112 using iterative or analytical image reconstruction methods. For example, the image processor unit 110 may use an analytical image reconstruction scheme, such as filtered back projection (FBP), to reconstruct an image of the patient's target volume. Alternatively, the image processor unit 110 may use iterative image reconstruction schemes, such as advanced statistical iterative reconstruction (ASIR), conjugate gradient (CG), maximum likelihood expectation maximization (MLEM), model-based iterative reconstruction (MBIR), etc., to reconstruct an image of the target volume of the subject 112. In some examples, in addition to iterative image reconstruction methods, the image processor unit 110 may also use analytical image reconstruction methods, such as FBP.

[0027] In some CT imaging system configurations, an X-ray source projects a cone-shaped X-ray beam, defined relative to the XYZ Cartesian coordinate system, often referred to as the "imaging volume." The X-ray beam passes through the object being imaged, such as a patient or subject. After being attenuated by the object, the X-ray beam strikes an array of detector elements. The intensity of the attenuated X-ray beam received at the detector array depends on the object's attenuation of the X-ray beam. Each detector element in the array generates a separate electrical signal, which is a measurement of the X-ray beam attenuation at the detector location. Attenuation measurements from all detector elements are acquired individually to produce a transmission distribution.

[0028] In some CT systems, a gantry rotates the X-ray source and detector array around the object being imaged within the imaging volume, causing the angle at which the X-ray beam intersects the object to continuously change. A set of X-ray radiation beam attenuation measurements (e.g., projection data) from the detector array at a given gantry angle is called a "view." A "scan" of the object comprises a set of views taken at different gantry angles or viewing angles during one rotation of the X-ray source and detector.

[0029] Figure 2 Examples similar to Figure 1 An exemplary imaging system 200 of a PCCT system 100. According to various aspects of this disclosure, the imaging system 200 is configured to image a subject 204 (e.g., a patient, ...). Figure 1 Imaging is performed on the subject 112. In one embodiment, the imaging system 200 includes a detector array 108 (see [link to image processing system]). Figure 1 The detector array 108 further includes a plurality of detector elements 202 that together sense an X-ray radiation beam 106 passing through the subject 204 (such as a patient) to acquire corresponding projection data (see [link]). Figure 2 In some embodiments, the detector array 108 may be fabricated as a multi-layer configuration comprising multiple rows of units or detector elements 202, wherein one or more additional rows of detector elements 202 are arranged in a parallel configuration for acquiring projection data. Detector elements 202 may also be referred to as pixels or detector pixels.

[0030] In some embodiments, the imaging system 200 is configured to traverse different angular positions around the subject 204 to acquire desired projection data. Therefore, the gantry 102 and the components mounted thereon may be configured to rotate about a center of rotation 206 to acquire projection data, for example, at different energy levels. Alternatively, in embodiments where the projection angle relative to the subject 204 varies over time, the mounted components may be configured to move along a generally curved path rather than along a circumference.

[0031] As the X-ray source 104 and detector array 108 rotate, detector array 108 collects data from the attenuated X-ray beam. The data collected by detector array 108 undergoes preprocessing and calibration to adjust the data to represent the line integral of the attenuation coefficient of the scanned subject 204. The processed data is often referred to as the projection. In some examples, individual detectors or detector elements 202 in detector array 108 may include photon counting detectors that record interactions of individual photons into one or more energy bins.

[0032] The acquired projection dataset can be used for Base Material Decomposition (BMD). During BMD, the measured projections are converted into a set of material density projections. The material density projections can be reconstructed to form a set of material density maps or images for each corresponding base material (such as bone, soft tissue, and / or contrast agent maps). The density maps or images can then be correlated to form a 3D volumetric image of the base material (e.g., bone, soft tissue, and / or contrast agent) in the imaging volume.

[0033] Once reconstructed, the base material image generated by imaging system 200 reveals the internal features of subject 204 represented by the densities of the two base materials. Density images can be displayed to illustrate these features. In conventional protocols for diagnosing medical conditions (such as disease states), and more generally, for diagnosing medical events, radiologists or physicians will consider a hard copy or display of the density image to identify features of interest. Such features may include lesions, size, and shape of specific anatomical structures or organs, as well as other features that should be identifiable in the image based on the individual practitioner's skill and knowledge.

[0034] In one embodiment, the imaging system 200 includes a control mechanism 208 to control the movement of components, such as the rotation of the gantry 102 and the operation of the X-ray source 104. In some embodiments, the control mechanism 208 further includes an X-ray controller 210 configured to provide power and timing signals to the X-ray source 104. Additionally, the control mechanism 208 includes a gantry motor controller 212 configured to control the rotational speed and / or position of the gantry 102 based on imaging requirements.

[0035] In some embodiments, control unit 208 further includes a data acquisition system (DAS) 214 configured to sample analog data received from detector element 202 and convert the analog data into digital signals for subsequent processing. DAS 214 may be further configured to selectively aggregate a subset of data from detector element 202 into a so-called macro detector. Data sampled and digitized by DAS 214 is transmitted via collector loop 213 to a computer or computing device 216. In one example, computing device 216 stores the data in a storage device or mass storage device 218. For example, storage device 218 can be any type of non-transitory memory and may include hard disk drives, floppy disk drives, optical disc read / write (CD-R / W) drives, digital versatile disc (DVD) drives, flash memory drives, and / or solid-state storage drives.

[0036] Additionally, computing device 216 provides commands and parameters to one or more of the DAS 214, X-ray controller 210, and rack motor controller 212 for system operation, such as data acquisition and / or processing. In some embodiments, computing device 216 controls system operation based on operator input. Computing device 216 receives operator input, such as commands and / or scan parameters, via an operator console 220 operably coupled to computing device 216. Operator console 220 may include a keyboard (not shown) or a touchscreen to allow the operator to specify commands and / or scan parameters.

[0037] Although Figure 2 An operator console 220 is illustrated, but more than one operator console may be coupled to the imaging system 200, for example, to input or output system parameters, request checks, plot data, and / or view images. Furthermore, in some embodiments, the imaging system 200 may be coupled via one or more configurable wired and / or wireless networks (such as the Internet and / or VPNs, wireless telephone networks, wireless LANs, wired LANs, wireless WANs, wired WANs, etc.) to multiple displays, printers, workstations, and / or similar devices, located locally or remotely, either within an institution or hospital or in entirely different locations.

[0038] In one implementation, for example, imaging system 200 includes or is coupled to a Picture Archiving and Communication System (PACS) 224. In an exemplary specific implementation, PACS 224 is further coupled to a remote system (such as a radiology information system, a hospital information system) and / or to an internal or external network (not shown) to allow operators in different locations to supply commands and parameters and / or obtain access to image data.

[0039] The computing device 216 operates the inspection table motor controller 226 using operator-provided and / or system-defined commands and parameters. This inspection table motor controller, in turn, controls the inspection table 114, which may be an electric inspection table. Specifically, the inspection table motor controller 226 can move the inspection table 114 to properly position the subject 204 within the rack 102, thereby acquiring projection data corresponding to the target volume of the subject 204.

[0040] As previously noted, DAS 214 samples and digitizes the projection data acquired by detector element 202. Subsequently, image reconstructor 230 uses the sampled and digitized X-ray data to perform high-speed reconstruction. Although Figure 2 Image reconstructor 230 is illustrated as a separate entity, but in some embodiments, image reconstructor 230 may be part of computing device 216. Alternatively, image reconstructor 230 may not be present in imaging system 200, and alternatively, computing device 216 may perform one or more functions of image reconstructor 230. Furthermore, image reconstructor 230 may be located locally or remotely and may be operatively connected to imaging system 200 using wired or wireless networks. Specifically, one exemplary embodiment may use computing resources in a "cloud" network cluster for image reconstructor 230.

[0041] In one embodiment, image reconstructor 230 stores reconstructed images in storage device 218. Alternatively, image reconstructor 230 may send the reconstructed images to computing device 216 to generate usable patient information for diagnosis and evaluation. In some embodiments, computing device 216 may send reconstructed images and / or patient information to a display or display device 232 communicatively coupled to computing device 216 and / or image reconstructor 230. In some embodiments, reconstructed images may be sent from computing device 216 or image reconstructor 230 to storage device 218 for short-term or long-term storage.

[0042] Information can be transmitted between components residing in gantry 102 and external devices (such as computing device 216 and / or image reconstructor 230) via slip ring 213, which facilitates electronic communication across the rotating gantry. In some examples, the gantry and internal components (e.g., control mechanism 208, X-ray source 104, detector array 108) may be collectively defined as a PCCT scanner, and thus the computing device 216 and image reconstructor 230 may reside outside the scanner.

[0043] Example imaging system 200 includes a Direct Multiplanar Rearrangement (DMPR) module 234. DMPR module 234 is communicatively coupled to imaging system 200 via, for example, a computing device 216. DMPR module 234 is coupled at least via computing device 216 to a display 232 to provide information and images to a user, to an operator console 220 to receive input from a user, and to a mass storage device 218 to store images processed by DMPR module 234, access protocols, or retrieve images stored in mass storage device 218 or PACS 224. Additionally, one or more of DMPR module 234, computing device 216, and mass storage device 218 may communicate with a server accessible by DMPR module 234 to receive protocol information images and provide DMPR-processed images to radiologists or other users remote from imaging system 200.

[0044] DMPR module 234 is a multiplanar reconstruction (or rearrangement) application integrated with scan acquisition and protocol-driven rearrangement specifications. The DMPR application provides advanced display capabilities to users (e.g., technicians, radiologists) during scanning by displaying multiplanar rearrangement views (e.g., axial, sagittal, coronal, oblique). The DMPR module 234 described herein provides orientation task capabilities to allow users to align and straighten the subject's anatomy and to use across multiple specified rearrangement images, which can be saved, captured, networked, or archived for later review by the radiologist. For example, the user can define the orientation of the first image, and the same orientation can be used for batch processing of other images acquired during scanning, thereby reducing the time spent by the user orienting each image.

[0045] When a scan event of the imaging system 200 triggers the computing device 216 to set up the DMPR module 234 and / or provide configuration information to it, the DMPR module 234 is initialized. This setup and configuration information may include information regarding batch protocol specifications. The imaging system 200 can then provide information related to image acquisition, including image data. The DMPR module 234 updates the rearranged image (axial, sagittal, coronal) views based on the received image data. The user can review the image volume in any rearranged view (including thick rearranged slices) before or after image acquisition is complete (e.g., after all images from the scan event have been received by the DMPR module 234). The user can specify a series of rearranged images to be created and stored. In some examples, the DMPR module 234 also automatically handles any pre-configured rearrangement protocols (e.g., protocols created before the initialization of the scan event).

[0046] Figure 3This is a flowchart of a method for a direct multi-plane rearrangement workflow that can be executed during protocol management. In some examples, the protocol is defined before the scan occurs and runs after the initial image reconstruction. In other examples, the protocol is defined before the scan occurs, but the slices need to be manually specified before execution. Alternatively, the protocol can be defined after the scanned images are acquired. Protocol management can be performed on an operator workstation or console 220 of imaging system 200, or on a workstation located remotely from one or more imaging systems 200, and can be applied across a cluster of imaging systems 200. Defining the DMPR protocol at the cluster level not only saves time but also produces more consistent images. The protocol can be stored on a server, cloud storage system, and / or imaging device 200.

[0047] The method begins at step 302, where an orientation is defined. That is, the user defines the region of interest for scanning. For example, the user can define an orientation, including but not limited to brain, face, head, neck, chest, trunk, abdomen, pelvis, etc. The defined orientation can be used across scan images from multiple patients. Defining an orientation may include determining the start and end points of the region of interest (e.g., brain, chest, pelvis, etc.), determining left-to-right parameters of the region of interest, and identifying or selecting the geometry of the region of interest. Defining an orientation can be performed by a technician or a user. In some examples, the defined orientation is saved so that the user can select the defined orientation for later use. For example, a head orientation and / or a face orientation can be defined during protocol management, and the user can select either the head orientation or the face orientation when running the DMPR module 234 on images received from the scanner.

[0048] Method 300 continues in step 304, where the Direct Multiplanar Rearrangement (DMPR) task is mapped to a defined orientation. The mapping DMPR task defines which DMPRs should be applied to for each given orientation. For example, after defining the transformation matrix, which DMPRs should be modified using the same transformation matrix for orientations such as head orientation. The mapping step may also define how the transformation matrix can be applied to each view of the image (e.g., sagittal, coronal, axial).

[0049] The method then determines in step 306 whether additional DMPR tasks should be mapped to defined orientations. For example, if a DMPR task has already been mapped to a head orientation, but no DMPR task has yet been mapped to a neck orientation, the method returns to step 304 to map the DMPR task to a neck orientation. If additional tasks need to be mapped, the method repeats step 302 until all tasks have been mapped to orientations and all defined orientations have DMPR tasks mapped to them. After mapping is complete, the method proceeds to step 308, which includes providing instructions to a user (e.g., a technician, radiologist) during the scan. Instructions may be provided to the user via the operator console 220 and / or display 232 of the imaging system 200. Method 300 is complete.

[0050] Figure 4 This is a flowchart depicting a scan-time operation method 400 for a direct multiplane rearrangement workflow. Figure 4 Method 400 can be performed on the operator console 220 and display 232 of the imaging system 200. Method 400 is preferably performed during scanning (e.g., while scanning data is being acquired, after the operator DMPR module 234 receives the first view or image from the imaging scan), but in some examples, it can be performed after the scan is completed.

[0051] Method 400 is initiated during a scan of the subject in step 402 by acquiring imaging data and images of the subject. Images can be acquired using, for example, a computed tomography imaging system. Alternatively, other types of imaging systems can be used to acquire patient images. In step 404, the image is reconstructed using any applicable reconstruction method. For example, the primary image reconstruction can reconstruct an image of the target volume of the subject 112 using iterative image reconstruction methods such as Advanced Statistical Iterative Reconstruction (ASIR), Conjugate Gradient (CG), Maximum Likelihood Expectation Maximization (MLEM), Model-Based Iterative Reconstruction (MBIR), etc. The DMPR module 234 receives the initial reconstruction of the image and / or has access to the initially reconstructed image (e.g., via computing device 216).

[0052] At step 406, the method continues, where the system provides the user with an indication that a DMPR task is available. For example, an alert may be provided to the user via a display 232 of the imaging system 200. In some examples, the alert may be a banner on the user interface of the display 232. Alternatively, the alert may be a pop-up window. An example dashboard including example DMPR task alerts is available in... Figure 6 Described in the text.

[0053] At step 408, the user initiates a DMPR task. Initiating a DMPR task may include opening the task (e.g., by clicking or selecting an alert) and selecting the orientation to which the DMPR task will be performed from a list of available orientations. Initiating a DMPR task may open different user interface screens on the display, allowing the user to view and / or manipulate the images. An example user interface may include multiple view ports, such as one view port for each of the sagittal, coronal, and axial views. An example dashboard is shown in... Figure 7 Described in the text.

[0054] At step 410, the user can perform initial alignment by aligning the image axis with the axis of a normal, ideal, or straight axis of the geometry. For example, the user can rotate the axis depicted in the viewport so that the axis is aligned with the center of the image view. Alternatively, the user can rotate the image so that the axis overlaid on the image is aligned with a normal, ideal, or straight axis. The initial alignment of each of the sagittal, coronal, and axial views in the respective viewports creates an initial transformation matrix. An example dashboard for alignment is shown in... Figure 8 and Figure 9 Described in the text.

[0055] Next, at step 412, the system automatically straightens the image based on the aligned axes and an initial transformation matrix defined when the user aligned the axes and the image. Automatic straightening can be applied to multiple views and / or multiple images simultaneously, thereby reducing the amount of time required to complete the task. An example dashboard depicting automatic straightening is shown in... Figure 10 As shown in the image.

[0056] After the system completes automatic straightening, in step 414, the user can fine-tune or refine the adjustment by manually straightening the image (e.g., freehand straightening). Fine-tuning or refining the straightened image also changes the transformation matrix accordingly. After fine-tuning is complete and the user proceeds to the next step, the transformation matrix can be saved for later use with the patient image. The transformation matrix can be saved in a server, cloud, or mass storage device accessible to the DMPR module 234, the imaging system 200, and / or an operator workstation remote from the imaging system 200. The transformation matrix can be saved as part of an image or scan record or a patient profile, allowing the transformation matrix for that scan to be recalled and applied to later image processing when needed.

[0057] At step 416, the user can define a geometry. Defining a geometry involves defining a bounding box on the image that surrounds the geometry (e.g., a region of interest). For example, if the geometry or region of interest is the brain, the bounding box defined by the user will surround the brain, but may exclude all areas of the skull. Similarly, if the geometry is the right lung, the bounding box will surround the right lung, but may exclude the left lung or other parts of the chest. An example dashboard depicting the bounding box defining the geometry is shown in [image / description]. Figures 11 to 13 As shown in the diagram. After defining the geometry, changes can be applied to the image in step 418. Applying changes can be done by the user, for example, by selecting the "Apply" or "Accept" option. Changes, including those to the transformation matrix, are applied to all images, slices, and views of the volume associated with the orientation (e.g., brain orientation). That is, changes are applied to the DMPR mapped to the orientation during protocol management.

[0058] At step 429, it is determined whether the additional orientation has a DMPR task. If the additional orientation has a DMPR task, the method returns to step 408, where the user initiates another DMPR task. If no additional orientation has a DMPR task, method 400 completes.

[0059] Figure 5 This is a flowchart of method 500, a direct multiplanar rearrangement workflow that can be performed after acquiring scanned images. For example, DMPR may have already been performed during scan time, but different filters may need to be applied to reveal different aspects of the patient scan. Method 500 is a workflow that allows the user to apply the same transformation matrix from a previously performed DMPR task for the patient to a new filtered version of the image. At step 502, the user opens a review of the previous scan. That is, the user can open images of previous scans of the patient stored in a database (e.g., a server, the cloud, etc.). The user can access the database from a workstation coupled to imaging system 200 or from a workstation remote from imaging system 200.

[0060] At step 504, the user creates a new DMPR task. In some examples, the user can choose from DMPR tasks created during the protocol management session. Alternatively, the user can create a new DMPR task to define an orientation after the steps of method 400. If needed, the user can then map the DMPR task to an orientation in step 506.

[0061] At step 508, the user retrieves a transformation matrix created from a previous DMPR task for the patient or the patient's imaging session. At step 510, the user applies the existing or retrieved transformation matrix to a DMPR task for the orientation of that session. At step 512, it is determined whether an additional orientation has a DMPR task. If an additional orientation with a DMPR task exists, method 500 returns to step 504. If no additional orientation with a DMPR task exists, method 500 completes.

[0062] Example methods 300, 400, and 500 can be combined or partially combined. Steps in any of methods 300, 400, and 500 can be reordered or removed. For example, some steps (including, but not limited to, refinement alignment) can be optional.

[0063] Figure 6 An example dashboard 600 is depicted, which can be displayed to the user as a notification that a DMPR task needs to be completed. Figure 6 An example main scan screen 602 is depicted. When using the example process described herein, the user's main scan screen 602 may display a notification 604 indicating the presence of a manual task tailored to their preferences. In the illustrated example, the notification is displayed as a banner. However, the notification may be displayed in the left task list and / or both as a banner. Alternatively, the notification may be displayed as a pop-up window.

[0064] Figure 7 Example dashboard 700 is shown, which can be presented to technicians or radiologists to select tasks for creating DMPR / MPR transformations using the proposed concepts. Figure 7 As depicted, the user can choose from either the example brain orientation 702 task or the neck orientation 702 task to straighten and correct image reconstruction. Additional or different tasks can also be created based on the user's needs. The selection of brain orientation 702 or neck orientation 704 can be made during protocol management time or during scan time. During protocol management time, brain orientation 702 or neck orientation 704 can be selected for modification. During scan time, the user can select the DMPR task to perform corrections outlined by protocol management on the images acquired during the scan.

[0065] Figure 8An example dashboard 800 is depicted, illustrating what can be displayed to the user after a task (e.g., Brain Orientation Task 702) is clicked. Opening Brain Orientation Task 702 includes a name 802, an algorithm 804, annotations 806 (which DMPR / MPR will be applied to 808), and graphical manipulation view ports 810a, 810b, 810c, and 810d. Annotations may include notes to the technician related to the task protocol and / or annotations created by the technician related to the imaging scan or the subject. The user can click on a spot or point (e.g., along an axis) in each of the view ports 810a, 810b, and 810c and drag to rotate axes 812a, 812b, and 812c to align axes 812a, 812b, and 812c with the image depicted in each view port 810a, 810b, and 810c. The lower right view port 810d may only be a depiction and may not be manipulable by the user. In some examples, the same transformation (e.g., the transformation matrix) is applied to multiple DMPR tasks. That is, changes made to the alignment of the images along axes 812a, 812b, 812c and in the viewport can be applied to multiple DMPR tasks from other images from the same scan (e.g., other views of similar geometry or regions of interest (such as the brain, neck, etc.) listed on the left side of the dashboard). After the user has aligned the axes, as... Figure 9 As depicted, users can click "Next" to proceed to the next screen. Although... Figure 9 The dashboard 800 depicts images with axes 812a, 812b, 812c rotated to align with the images in each viewport 810a, 810b, 810c, but in an alternative implementation, the images can be rotated to align with the axes.

[0066] Figure 10 A sample dashboard 1000 is depicted that can be presented to the user after automatic straightening has been completed. (See attached image.) Figure 4As discussed in method 400, the example automatic straightening can be applied to multiple views and images simultaneously. This reduces the total time spent by the user manipulating images to straighten them. In the illustrated example, view port 810d of dashboard 1000 includes the original orientation of the image, rather than a newer version. Including the original orientation indicates to the user what has changed in the image and also reminds the user of which version of the image is currently stored. For example, changes to the stored version of the image are not applied until the user completes the task and accepts or applies the changes. Additionally, after automatic straightening is complete, if needed, the user can manually rotate the image to fine-tune the image alignment by clicking on spots or dots in the image in view ports 810a, 810b, and 810c and dragging to rotate the image and / or axes. If the axes are rotated, the system can perform the automatic straightening task again, causing other views or images related to the DMPR task to be adjusted as well. If straightening is complete, the user can click Next to move to the next part of the DMPR task.

[0067] Figure 11 A sample dashboard 1100 is depicted that can be presented to the user after the straightening task is completed. Figure 11 The geometry of the scanned image is depicted as selected or identified by the user. In the illustrated example, the selected geometry is the subject's brain. The user highlights the brain geometry on each axial image displayed in view ports 810a, 810b, and 810c. In the illustrated example, selecting the geometry involves setting coverage and views in each view port 810a, 810b, and 810c using adjustable bounding boxes 1102a, 1102b, and 1102c. In some examples, the user can select the geometry, and the system can define preliminary bounding boxes 1102a, 1102b, and 1102c, which can then be adjusted by the user. For example, the bounding boxes 1102a, 1102b, and 1102c can be automatically defined based on landmarks identified and detected by the system (e.g., sternal notch, brainstem, etc.). In this type of example, the user validates the boundaries of bounding boxes 1102a, 1102b, and 1102c, and can manually refine or adjust these boundaries. In other examples, the user defines the boundaries of bounding boxes 1102a, 1102b, and 1102c. The geometry corresponds to the scanned region of interest.

[0068] Figure 12An example dashboard 1200 is depicted that can be presented to the user after the selection of geometry is completed. The user can then choose which views the alignment, straightening, and geometry selections are applied to. In the illustrated example, the user has already checked boxes 1202a, 1202b, and 1202c corresponding to the sagittal, coronal, and axial brain views. At this point, the user can add additional views and / or geometry (e.g., sagittal, coronal, and axial neck views), or apply changes to the selected views to create a reconstruction by selecting the apply button. In some examples, changes made via the DMPR task can be defined after the localization scan and also applied to images from the imaging scan.

[0069] Figure 13 This describes a sample dashboard 1300 that can be presented to the user after application changes. (Example:) Figure 13 The illustration depicts indicators (e.g., colors) that can be changed to indicate to the user which DMPR tasks have been completed (e.g., the image has been transformed). As shown, neck-related tasks need to be completed. The user can select a neck task and repeat the process of aligning, straightening, and selecting the geometry of the image to perform the neck DMPR task.

[0070] The benefits of the proposed workflow include reduced overall processing time for reconstruction—which is particularly important for traditionally time-consuming and time-critical trauma cases—reduced manual work performed by technicians or radiologists, and more consistent reconstruction by reducing variability caused by human error.

[0071] Figures 1 to 13Example configurations for the relative positioning of various components are shown. In at least one example, if such components are shown to be in direct contact or directly coupled, they may be referred to as being in direct contact or directly coupled, respectively. Similarly, in at least one example, components shown to be adjacent to or next to each other may be referred to as being adjacent to or next to each other, respectively. For example, components placed in coplanar contact with each other may be referred to as being in coplanar contact. As another example, in at least one example, components positioned to be spaced apart from each other and having only space between them without other components may be described as such. As yet another example, components shown to be above / below each other, on opposite sides of each other, or on the left / right side of each other may be described relative to each other. Furthermore, as shown, in at least one example, the topmost component or the point of the component may be referred to as the “top” of the component, and the bottommost component or the point of the component may be referred to as the “bottom” of the component. As used herein, top / bottom, upper / lower, above / below may be relative to the vertical axis of the figure and may be used to describe the positioning of the components in the figure relative to each other. Therefore, in one example, an element shown above other elements is vertically positioned above the other elements. Similarly, the shapes of the elements depicted in the figures may be described as having those shapes (e.g., such as circular, straight, planar, curved, rounded, chamfered, angled, etc.). Furthermore, in at least one example, elements shown intersecting each other may be described as intersecting elements or intersecting each other. Additionally, in one example, an element shown as being inside or outside another element may be described as such. It should be understood that one or more parts described as "substantially similar and / or identical" differ from each other according to manufacturing tolerances (e.g., within a deviation of 1% to 5%). Figures 1 to 13 This is shown approximately to scale, but other sizes may be used if desired.

[0072] When describing elements of various embodiments of this disclosure, the articles “a,” “an,” and “the” are intended to indicate the presence of one or more such elements. The terms “first,” “second,” etc., do not indicate any order, quantity, or importance, but are used to distinguish one element from another. The terms “comprising,” “including,” and “having” are intended to be inclusive and indicate that additional elements may exist in addition to the listed elements. As used herein, the terms “connected to,” “coupled to,” etc., indicate that an object (e.g., a material, element, structure, component, etc.) may be connected to or coupled to another object, regardless of whether the object is directly connected to or coupled to the other object, or whether one or more intermediary objects exist between the two objects. Furthermore, it should be understood that references to “an embodiment” or “an embodiment” of this disclosure are not intended to be construed as excluding the existence of additional embodiments also incorporating the referenced features.

[0073] In addition to any modifications previously indicated, many other variations and alternative arrangements can be devised by those skilled in the art without departing from the spirit and scope of this specification, and the appended claims are intended to cover such modifications and arrangements. Therefore, although the information has been described in particular and detail above in conjunction with what is now considered to be the most practical and preferred aspects, it will be apparent to those skilled in the art that many modifications, including but not limited to those in form, function, mode of operation, and purpose, can be made without departing from the principles and concepts set forth herein. Likewise, as used herein, examples and embodiments are intended to be illustrative only in all respects and should not be construed as limiting in any way.

Claims

1. A method for performing a direct multiplanar rearrangement task in a medical imaging system, the method comprising: Images of the subject are acquired via the medical imaging system; The image is automatically straightened based on the transformation matrix via a direct multiplane rearrangement module; as well as The transformation matrix is ​​applied to multiple images having the corresponding orientation of the image via the direct multiplane rearrangement module.

2. The method of claim 1, further comprising defining the orientation of the image and mapping the direct multiplane rearrangement task to the defined orientation.

3. The method of claim 1, further comprising aligning an axis overlaid on the image via a display of an operator console, wherein aligning the axis defines the transformation matrix.

4. The method of claim 3, wherein aligning the axis comprises rotating the axis that covers the image.

5. The method of claim 1, further comprising providing a notification to the user via a display that a direct multiplane rearrangement task is available.

6. The method of claim 1, further comprising refining the straightening of the image via input from a user.

7. The method of claim 1, further comprising selecting the geometry of the image by defining a bounding box on the image.

8. The method of claim 7, wherein the direct multiplane rearrangement module defines the bounding box based on geometry selected by the user, and wherein the user verifies the boundaries of the bounding box.

9. The method of claim 7, wherein the user defines the bounding box via the operator console.

10. The method of claim 1, wherein applying the transformation matrix to a plurality of images having a corresponding orientation of the image includes providing the user with an additional indication that the plurality of images have been transformed.

11. The method of claim 1, wherein the image is displayed in a sagittal view, a coronal view, and an axial view.

12. A medical imaging system, the medical imaging system comprising performing a direct multiplanar rearrangement task, the medical imaging system comprising: X-ray source; A detector, wherein the X-ray source and the detector acquire images of the subject; A computing device, wherein the computing device communicates with a mass storage device to store and retrieve the images of the subject; and A direct multiplane rearrangement module, coupled to the computing device, is configured to: Images of the subject acquired by the X-ray source and the detector are retrieved via the calculations. The image is automatically straightened based on the transformation matrix; and The transformation matrix is ​​applied to multiple images having the corresponding orientation of the image.

13. The system of claim 12, further comprising a display coupled to the computing system for displaying the image to a user.

14. The system of claim 13, wherein the transformation matrix is ​​determined by aligning the display with axes covering slices of the image.

15. The system of claim 14, wherein aligning the axis comprises rotating the axis overlying the slice of the image.

16. The system of claim 1, further comprising an operator console for selecting the geometry of the image by defining a bounding box on the image.

17. The system of claim 16, wherein the direct multiplane rearrangement defines the bounding box based on geometry selected by the user, and wherein the user uses the operator console to verify the boundaries of the bounding box.

18. The system of claim 1, wherein applying the transformation matrix to a plurality of images having a corresponding orientation of the image includes providing the user with an additional indication via a display that the plurality of images have been transformed.

19. A method for performing a direct multiplanar rearrangement task in a medical imaging system, the method comprising: Retrieve images of the subject from a large-capacity storage device; Retrieve the transformation matrix associated with the direct multiplane rearrangement task associated with the image; as well as The transformation matrix is ​​applied to multiple images having the corresponding orientation of the image via a direct multiplane rearrangement module.

20. The method of claim 19, further comprising defining the orientation of the image and mapping the direct multiplane rearrangement task to the defined orientation.

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