Systems and methods for direct multi-planar reformat of images from an imaging system

Abstract

Methods and systems for performing a Direct Multi-Planar Reformat task are described. An example method includes acquiring, via the medical imaging system, an image of a subject, automatically straightening, via a Direct Multi-Planar Reformat module, the image based a transformation matrix, and applying, via the Direct Multi-Planar Reformat module, the transformation matrix to a plurality of images having a corresponding orientation of the image.

Description

SYSTEMS AND METHODS FOR DIRECT MULTI-PLANAR REFORMAT OF IMAGES FROM AN IMAGING SYSTEMCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to 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] The present disclosure relates to medical imaging, and in particular to methods and systems for automating multi-planar reconstruction of medical imaging acquisitions.BACKGROUND

[0003] Not all patients are set up straight on the scan table, in particular trauma and acute care patients may be kyphotic or positioned such that at least a portion of the body of the patient is angled relative to the table. There is no multi -oblique option when using current Multi-Planar Reconstructions (MPR) or Direct Multi-Planar Reformat (DMPR) methods to straighten the patient dataset. Each fine slice dataset of the same series (Bone, Soft Tissue, Lung) requires manual straightening and batching. Thus, the current MPR or DMPR process can take up to 300 clicks to complete for trauma reconstructions, which is time consuming for the technologist or radiologist, and can take as long as 30 minutes to complete. Additionally, reconstructions are not at consistent angles across different algorithms within the same region of the body, which makes reporting challenging because the multiple algorithms cannot be directly compared. The current process requires complex training and consistency.SUMMARY

[0004] In view of the above technical problems, an embodiment of the present application provides a method for performing a Direct Multi-Planar Reformat task for a medical imaging system. An example method includes acquiring, via the medical imaging system, an image of a subject, automatically straightening, via a Direct Multi -Planar Reformat module, the image baseda transformation matrix, and applying, via the Direct Multi-Planar Reformat module, the transformation matrix to a plurality of images having a corresponding orientation of the image.

[0005] An example medical imagining system for performing a Direct Multi-Planar Reformat task includes an X-ray source and a detector, where the X-ray source and the detector acquire images of a subject. The medical imaging system also includes a computing device in communication with a mass storage to store and retrieve the images of a subject and a Direct Multi-Planar Reformat module coupled to the computing device. The Direct Multi-Planar Reformat module is configured to retrieve, via the computing, an image of a subject acquired by the X-ray source and detector, automatically straighten the image based a transformation matrix, and apply the transformation matrix to a plurality of images having a corresponding orientation of the image.

[0006] Another example method includes retrieving, from a mass storage, an image of a subject, retrieving a transformation matrix associated with a Direct Multi-Planar Reformat task associated with the image, and applying, via a Direct Multi-Planar Reformat module, the transformation matrix to a plurality of images having a corresponding orientation of the image.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The accompanying drawings included in the present application are intended to help to further understand embodiments of the present application, constitute a part of the specification, and are used to illustrate implementations of the present application and set forth the principles of the present application together with textual description. Obviously, the accompanying drawings in the following description are merely some embodiments of the present application, and a person of ordinary skill in the art could obtain other implementations according to the accompanying drawings without the exercise of inventive effort. In the accompanying drawings:

[0008] FIG. 1 shows a perspective view of a CT imaging system according to an embodiment of the present disclosure.

[0009] FIG. 2 shows a block diagram of a CT imaging system according to an embodiment of the present disclosure.

[0010] FIG. 3 is a flowchart of a method for a Direct Multi-Planar Reformat workflow that may be performed during protocol management.

[0011] FIG. 4 is a flowchart of a method for a Direct Multi -Planar Reformat workflow that maybe performed at scan time.

[0012] FIG. 5 is a flowchart of a method for a Direct Multi -Planar Reformat workflow that may be performed after a scan image is acquired.

[0013] FIG. 6 an example dashboard presented to a user indicating a Multi-Planar Reformat task is available for an imaging scan.

[0014] FIG. 7 depicts an example dashboard presented to a user when initiating the Multi-Planar Reformat task.

[0015] FIG. 8 depicts an example dashboard presented to a user to perform an align component of the Multi-Planar Reformat task.

[0016] FIG. 9 depicts another example dashboard presented to a user when performing an align component of the Direct Multi-Planar Reformat task.

[0017] FIG. 10 depicts an example dashboard presented to a user when performing a straightening component of the Direct Multi-Planar Reformat task.

[0018] FIG. 11 depicts an example dashboard presented to a user when performing a geometry selection component of the Direct Multi-Planar Reformat task.

[0019] FIG. 12 depicts an example dashboard presented to a user when performing a review and selection component of the Direct Multi-Planar Reformat task.

[0020] FIG. 13 depicts an example dashboard presented to a user after applying changes made during the Direct Multi -Planar Reformat task.

[0021] It can be expected that the elements in one embodiment of the present disclosure may be advantageously applied to the other embodiments without further elaboration.DETAILED DESCRIPTION

[0022] The present disclosure and techniques described herein provide a workflow for automating multiplanar reconstruction. Direct Multi-Planar Reformat (DMPR) or multi-planar reconstructions (MPRs) are created from image acquisitions and often the technologist or radiologist may create sub-sets from the single acquisition such as a face and head. As used herein, Direct Multi-Planar Reformat (DMPR) may also be used to include multi-planar reconstructions. Within the body of a patient, there are different regions (e.g., chest, abdomen, pelvis) for which separate specific image series can be created or generated. A multiplanar image or image reconstruction includes the ability to transform the view in different planes (e.g.,axial, sagittal, coronal). Direct Multi Planar Reformat (DMPR) allows the user to move from the usual 2D image review mode to a prospective 3D image review mode in the axial, sagittal, coronal and oblique planes. Typically, this type of reconstruction process either requires a large effort for an Artificial Intelligence (Al) or Deep Learning (DL) algorithm to be created for the region of the body to aid in manipulation of the anatomy like straightening (Spine AutoViews, Head AutoViews), or very tedious manual effort from the technologist or radiologist to physically manipulate the anatomy. Previously, this manipulation had to be done individually for each reconstruction. In some circumstances, this manipulation and manual effort was estimated at over 45-300 clicks, or sometimes more, and could take longer than 40 minutes. This manual effort can be really large and time-consuming if there are massive amounts of DMPRs and MPRs being processed and created. Additionally, the process may require multiple applications.

[0023] The proposed new method and systems disclosed herein batches all of that effort into bundled steps and can reduce the amount of time the technologists or radiologists spend to manipulate the images, potentially as few as 8 clicks and only 3-4 minutes of processing time. The user can also automatically create batch reformats using predefined reformat protocols and network reformatted images to selected reading locations, reducing total exam time and increasing productivity. Additionally, an orientation task allows the user to define a transformation matrix (e.g., a matrix defining an angle of rotation to be used to straighten subsequent images) graphically from the image and apply it automatically to one or more DMPRs. DMPR images may be displayed to the technician in anatomical orientation where anterior is at the top, posterior is at the bottom, right is on the left and left is on the right. For example, if the data set is from an image acquisition where the patient was scanned prone, the display will display this orientation of the images.

[0024] The concept disclosed herein is to create a new task or operation for the user that allows the technologist or radiologists to manipulate the image and apply that manipulation across any number of DMPR / MPR output image series. Within this task, the user would have a title for the task (i.e., Face Orientation, Head Orientation, Brain Orientation, Neck Orientation, etc.), the ability to input free text or instructional text in a notes field (i.e., increase compliance, increase efficiency, reduce recall / cognitive load to remember steps required for the image view), the ability to select between manual manipulation and algorithms (allowing for both system led orientation / computer straightening / segmentation and full manual manipulation), the ability toselect graphical view ports with the ability to manipulate the images, the ability to select with DMPR / MPR reconstruction(s) (1 to n) to apply this transformation to, and finally can apply / confirm / run the transformation. The proposed concept also allows for manipulation of the algorithm outputs and bulking the outputs to multiple places. This can reduce discrepancies throughout the different images that occur with manual manipulation due to human error or variances. The description and embodiments of the subject matter disclosed herein may relate to a phantom for calibration scans of an imaging system, such as a photon counting computed tomography (PCCT) system. However, the methods and systems described herein for direct multi-planar reformat may be used with other imaging systems, including but not limited to, traditional computed tomography (CT) imaging systems, magnetic resonance imaging (MRI) system, positron emission tomography (PET) imaging systems, Single-photon emission computed tomography (SPECT) imaging systems, etc., and / or a combination of imaging systems.

[0025] FIG. 1 illustrates an exemplary PCCT system 100 (also referred to as a photon counting X-ray imaging system) configured for CT imaging with photon counting detectors. Particularly, the PCCT system 100 is configured to image a subject 112 such as a patient, an inanimate object, one or more manufactured parts, and / or foreign objects such as dental implants, stents, and / or contrast agents present within the body. The PCCT system 100 includes a gantry 102, which in turn, may further include at least one X-ray source 104 configured to project a beam of X-ray radiation 106 (see FIG. 2) for use in imaging the subject 112 laying on a table 114. Specifically, the X-ray source 104 is configured to project the X-ray radiation beams 106 towards a detector array 108 positioned on the opposite side of the gantry 102. Although FIG. 1 depicts a single X- ray source 104, in certain embodiments, multiple X-ray sources and detectors may be employed to project a plurality of 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 may enable dual -energy spectral imaging by rapid peak kilovoltage (kVp) switching. In the embodiments described herein, the X-ray detector employed is a photon counting detector which is capable of differentiating X-ray photons of different energies.

[0026] In certain embodiments, the PCCT system 100 further includes an image processor unit 110 configured to reconstruct images of a target volume of the subject 112 using an iterative or analytic image reconstruction method. For example, the image processor unit 110 may use ananalytic image reconstruction approach such as filtered back projection (FBP) to reconstruct images of a target volume of the patient. As another example, the image processor unit 110 may use an iterative image reconstruction approach such as advanced statistical iterative reconstruction (ASIR), conjugate gradient (CG), maximum likelihood expectation maximization (MLEM), model-based iterative reconstruction (MBIR), and so on to reconstruct images of a target volume of the subject 112. In some examples the image processor unit 110 may use an analytic image reconstruction approach such as FBP in addition to an iterative image reconstruction approach.

[0027] In some CT imaging system configurations, an X-ray source projects a cone-shaped X- ray radiation beam which is defined with respect to an X-Y-Z Cartesian coordinate system and generally referred to as an "imaging volume." The X-ray radiation beam passes through an object being imaged, such as the patient or subject. The X-ray radiation beam, after being attenuated by the object, impinges upon an array of detector elements. The intensity of the attenuated X-ray radiation beam received at the detector array is dependent upon the attenuation of an X-ray radiation beam by the object. Each detector element of the array produces a separate electrical signal that is a measurement of the X-ray beam attenuation at the detector location.The attenuation measurements from all the detector elements are acquired separately to produce a transmission profile.

[0028] In some CT systems, the X-ray source and the detector array are rotated with a gantry within the imaging volume and around the object to be imaged such that an angle at which the X- ray beam intersects the object constantly changes. A group of X-ray radiation attenuation measurements, e.g., projection data, from the detector array at one gantry angle is referred to as a "view." A "scan" of the object includes a set of views made at different gantry angles, or view angles, during one revolution of the X-ray source and detector.

[0029] FIG. 2 illustrates an exemplary imaging system 200 similar to the PCCT system 100 of FIG. 1. In accordance with aspects of the present disclosure, the imaging system 200 is configured for imaging a subject 204 (e.g., a patient, the subject 112 of FIG. 1). In one embodiment, the imaging system 200 includes the detector array 108 (see FIG. 1). The detector array 108 further includes a plurality of detector elements 202 that together sense the X-ray radiation beam 106 (see FIG. 2) that passes through the subject 204 (such as a patient) to acquire corresponding projection data. In some embodiments, the detector array 108 may be fabricatedin a multi-slice configuration including the plurality of rows of cells or detector elements 202, where one or more additional rows of the detector elements 202 are arranged in a parallel configuration for acquiring the projection data. The detector elements 202 may also be referred to as pixels or detector pixels.

[0030] In certain embodiments, the imaging system 200 is configured to traverse different angular positions around the subject 204 for acquiring desired projection data. Accordingly, the gantry 102 and the components mounted thereon may be configured to rotate about a center of rotation 206 for acquiring the projection data, for example, at different energy levels. Alternatively, in embodiments where the projection angle relative to the subject 204 varies as a function of time, the mounted components may be configured to move along a general curve rather than along a segment of a circle.

[0031] As the X-ray source 104 and the detector array 108 rotate, the detector array 108 collects data of the attenuated X-ray beams. The data collected by the detector array 108 undergoes preprocessing and calibration to condition the data to represent the line integrals of the attenuation coefficients of the scanned subject 204. The processed data are commonly called projections. In some examples, the individual detectors or detector elements 202 of the detector array 108 may include photon counting detectors which register the interactions of individual photons into one or more energy bins.

[0032] The acquired sets of projection data may be used for basis material decomposition (BMD). During BMD, the measured projections are converted to a set of material -density projections. The material-density projections may be reconstructed to form a set of materialdensity maps or images of each respective basis material, such as bone, soft tissue, and / or contrast agent maps. The density maps or images may be, in turn, associated to form a 3D volumetric image of the basis material, for example, bone, soft tissue, and / or contrast agent, in the imaged volume.

[0033] Once reconstructed, the basis material image produced by the imaging system 200 reveals internal features of the subject 204, expressed in the densities of two basis materials. The density image may be displayed to show these features. In traditional approaches to diagnosis of medical conditions, such as disease states, and more generally of medical events, a radiologist or physician would consider a hard copy or display of the density image to discern characteristic features of interest. Such features might include lesions, sizes and shapes of particular anatomiesor organs, and other features that would be discernable in the image based upon the skill and knowledge of the individual practitioner.

[0034] In one embodiment, the imaging system 200 includes a control mechanism 208 to control movement of the components such as rotation of the gantry 102 and the operation of the X-ray source 104. In certain 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 a rotational speed and / or position of the gantry 102 based on imaging requirements.

[0035] In certain embodiments, the control mechanism 208 further includes a data acquisition system (DAS) 214 configured to sample analog data received from the detector elements 202 and convert the analog data to digital signals for subsequent processing. The DAS 214 may be further configured to selectively aggregate data from a subset of the detector elements 202 into so-called macro-detectors. The data sampled and digitized by the DAS 214 is transmitted to a computer or computing device 216 via a slip ring 213. In one example, the computing device 216 stores the data in a storage device or mass storage 218. The storage device 218, for example, may be any type of non-transitory memory and may include a hard disk drive, a floppy disk drive, a compact disk-read / write (CD-R / W) drive, a Digital Versatile Disc (DVD) drive, a flash drive, and / or a solid-state storage drive.

[0036] Additionally, the computing device 216 provides commands and parameters to one or more of the DAS 214, the X-ray controller 210, and the gantry motor controller 212 for controlling system operations such as data acquisition and / or processing. In certain embodiments, the computing device 216 controls system operations based on operator input. The computing device 216 receives the operator input, for example, including commands and / or scanning parameters via an operator console 220 operatively coupled to the computing device 216. The operator console 220 may include a keyboard (not shown) or a touchscreen to allow the operator to specify the commands and / or scanning parameters.

[0037] Although FIG. 2 illustrates one operator console 220, more than one operator console may be coupled to the imaging system 200, for example, for inputting or outputting system parameters, requesting examinations, plotting data, and / or viewing images. Further, in certain embodiments, the imaging system 200 may be coupled to multiple displays, printers, workstations, and / or similar devices located either locally or remotely, for example, within aninstitution or hospital, or in an entirely different location via one or more configurable wired and / or wireless networks such as the Internet and / or virtual private networks, wireless telephone networks, wireless local area networks, wired local area networks, wireless wide area networks, wired wide area networks, etc.

[0038] In one embodiment, for example, the imaging system 200 either includes, or is coupled to, a picture archiving and communications system (PACS) 224. In an exemplary implementation, the PACS 224 is further coupled to a remote system such as a radiology department information system, hospital information system, and / or to an internal or external network (not shown) to allow operators at different locations to supply commands and parameters and / or gain access to the image data.

[0039] The computing device 216 uses the operator-supplied and / or system-defined commands and parameters to operate a table motor controller 226, which in turn, may control a table 114 which may be a motorized table. Specifically, the table motor controller 226 may move the table 114 for appropriately positioning the subject 204 in the gantry 102 for acquiring projection data corresponding to the target volume of the subject 204.

[0040] As previously noted, the DAS 214 samples and digitizes the projection data acquired by the detector elements 202. Subsequently, an image reconstructor 230 uses the sampled and digitized X-ray data to perform high-speed reconstruction. Although FIG. 2 illustrates the image reconstructor 230 as a separate entity, in certain embodiments, the image reconstructor 230 may form part of the computing device 216. Alternatively, the image reconstructor 230 may be absent from the imaging system 200 and instead the computing device 216 may perform one or more functions of the image reconstructor 230. Moreover, the image reconstructor 230 may be located locally or remotely, and may be operatively connected to the imaging system 200 using a wired or wireless network. Particularly, one exemplary embodiment may use computing resources in a "cloud" network cluster for the image reconstructor 230.

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

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

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

[0044] The DMPR module 234 is a multi-planar reconstruction (or reformat) application integrated with scan acquisition and protocol -driven reformat prescription. The DMPR application provides the user (e.g., a technologist, a radiologist) with advanced display capabilities during scanning by displaying multi-planar reformat views (e.g. axial, sagittal, coronal, oblique). The DMPR module 234 described herein provides orientation task capabilities to allow the user to align and straighten the anatomy of the subject and be used across multiple prescribed reformat images that may be saved, filmed, networked, or archived for later review by the radiologist. For example, the user may define an orientation of a first image, and the same orientation may be used for batch processing of other images acquired during the scan, thereby reducing time the user is spending orienting each image.

[0045] The DMPR module 234 is initialized when scanning events of the imaging system 200 trigger a computing device 216 to setup and / or provide configuration information to the DMPR module 234. This setup and configuration information may incldue information regarding batchprotocols prescriptions. The imaging system 200 may then provide information related to the image acquisition, including image data. The DMPR module 234 updates the reformatted image (axial, sagittal, coronal) views based on the received image data. The user may, either before or after image aquation is complete (e.g., all images from the scan event are received by the DMPR module 234) review the image volume at any reformat view (including thick reformat slices). The user may prescribe a series of reformatted images to be created and stored. In some examples, the DMPR module 234 also automatically process any pre-configured reformat protocols (e.g., protocols created prior to initialization of the scan event).

[0046] FIG. 3 is a flowchart of a method for a Direct Multi-Planar Reformat workflow that may be performed during protocol management. In some examples, the protocol is defined before the scan takes place and runs after the initial images are reconstructed. In other examples, the protocol is defined before the scan takes place, but requires manual prescription of the slices before it is run. Alternatively, the protocol can be defined after the scan images are acquired. The protocol management can be performed on the operator workstation or console 220 of the imaging system 200 or can be performed on a workstation remote from the imaging system 200 or systems, and applied across a fleet of imaging systems 200. Defining DMPR protocols at a fleet level not only saves time, but also results in more consistent images. Protocols can be stored on a server, a cloud storage system, and / or an imaging device 200.

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

[0048] The method 300 continues in step 304 with mapping direct multi-planar reformat tasks to the defined orientation. Mapping DMPR tasks defines, for each given ordination(s), whichDMPRs that transformation should apply to. For example, after the transformation matrix is defined, for an orientation such as a head orientation, which DMPRs should be modified using the same transformation matrix. The mapping step may also define how the transformation matrix may be applied to each of the views (e.g., sagittal, coronal, axial) of the image.

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

[0050] FIG. 4 is a flowchart depicting a method 400 for scan-time operations for a Direct Multi- Planar Reformat workflow. The method 400 of FIG. 4 may be performed on the operator consol 220 and display 232 of the imaging system 200. The method 400 is preferably performed at the time of the scan (e.g., as the scan data is being acquired, after the operator DMPR module 234 receives the first views or images from the imaging scan), but in some examples, may be performed after the scan is complete.

[0051] The method 400 initiates during scanning of the subject by acquiring imaging data and images of the subject in step 402. The images may be acquired, for example, using a computed tomography imaging system. Alternatively, other types of imaging systems may be used to acquire patient images. In step 404, the images are reconstructed using any applicable reconstruction methods. For example, the primary image reconstruction may use an iterative image reconstruction approach such as advanced statistical iterative reconstruction (ASIR), conjugate gradient (CG), maximum likelihood expectation maximization (MLEM), model-based iterative reconstruction (MBIR), and so on to reconstruct images of a target volume of the subject 112. The DMPR module 234 receives the initial reconstruction of the image and / or has access to the initial reconstructed image (e.g., via the computing device 216).

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

[0053] At step 408, the user initiates the DMPR task. Initiating the DMPR task may include opening the task, for example, by clicking on or selecting the alert and selecting an orientation from a list of available orientations for which to perform DMPR task(s). Initiating the DMPR task may open a different user interface screen on the display, which allows the user to view and / or manipulate the images. The example user interface may include multiple view ports, such as one for each of the sagittal, coronal, and axial views. An example dashboard is depicted in FIG. 7.

[0054] At step 410, the user may perform an initial alignment by aligning the image axis with an axis of the normal, ideal, or straight axis for the geometry. For example, the user may rotate the axis depicted in the viewport such that the axis is aligned with the center of the image view. Alternatively, the user may rotate the image so an axis overlaid on the image aligns with the 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. Example dashboards for alignment are depicted in FIGS. 8 and 9.

[0055] Next, at step 412, the system automatically straightens the image based on the aligned axis and the initial transformation matrix defined when the user aligned the axis and image. Auto-straightening may be applied to multiple views of the image and / or multiple images simultaneously, thereby reducing the amount of time needed to complete the task. An example dashboard depicting auto-straightening is shown in FIG. 10.

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

[0057] At step 416, the user may define the geometry. Defining the geometry includes defining a bounding box on the image that encompasses the geometry (e.g., region of interest). For example, if the geometry or region of interest is a brain, the bounding box defined by the user will encompass the brain, but may not include all areas of the skull. Similarly, if the geometry is a right lung, the bounding box would encompass the right lung, but may not include the left lung or other parts of the chest. Example dashboards depicting the bounding box defining the geometry are shown in FIGS. 11-13. After defining the geometry, the changes may be applied to the image in step 418. Applying the changes may be done by the user, for example, by selecting an “apply” or “accept” option. The changes, including the transformation matrix, are applied to all images, slices, and views of the volume associated with the orientation, for example, the brain orientation. That is, the changes are applied to the DMPRs that were mapped to the orientation during the protocol management.

[0058] At step 429, it is determined whether additional orientations have DMPR tasks. If additional orientations have DMPR tasks, the method returns to step 408, where the user initiates another DMPR task. If there are no additional orientations that have DMPR tasks, the method 400 is complete.

[0059] FIG. 5 is a flowchart of a method 500 for a Direct Multi-Planar Reformat workflow that may be performed after a scan image is acquired. For example, a DMPR may have been performed at scan time, but a different filter may need to be applied to show a different aspect of the patient scan. Method 500 is a workflow allowing the user to apply the same transformation matrix from the previously competed DMPR tasks for a patient to the new filtered version of the images. At step 502, the user opens a review of a previous scan. That is, the user may open images from a previous scan of a patient that is stored in the database (e.g., a server, a cloud, etc.). The user may access the database from a workstation that is coupled to the imaging system 200, or may access the database from a workstation remote from an imaging system 200.

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

[0061] At step 508, the user retrieves the transformation matrix created from previous DMPRtasks for the patient or imaging session of the patient. At step 510, the use applies the existing or retrieved transformation matrix to the DMPR tasks for the orientations of this session. At step 512, it is determined whether additional orientations have DMPR tasks. If there are additional orientations with DMPR tasks, the method 500 returns to step 504. If there are not additional orientations with DMPR tasks, the method 500 is complete.

[0062] The example methods 300, 400, and 500 may be combined or partially combines. Steps of any of the methods 300, 400, and 500 may be reordered or removed. For example, some steps, including but not limited to refining the alignment, may be optional.

[0063] FIG. 6 depicts an example dashboard 600 which may be displayed to a user as a notice that a DMPR task needs to be completed. FIG. 6 depicts an example main scan screen 602. When using the example process described herein, the main scan 602 screen of the user may display a notice 604 that there is a manual task for orientation. In the illustrated example, the notice is displayed as a banner. However, the notice may be displayed in both the left task list and / or as a banner. Alternatively, the notice may be displayed as a pop-up.

[0064] FIG. 7 represents an example dashboard 700 that may be presented to the technologist or radiologist to select the task to create a DMPR / MPR transformation using the proposed concept. As depicted in FIG. 7, the user can select from an example Brain Orientation 702 task or Neck Orientation 702 task to straighten and correct image reconstruction. Additional or different tasks may also be created based on the user’s needs. The selection of Brain Orientation 702 or Neck Orientation 704 can be done either during a protocol management time or at scan time. At protocol management time, the Brain or Neck Orientations 702, 704 may be selected to make changes. At scan time, the DMPR tasks may be selected by the user to perform the corrections outlined by the protocol management to an image obtained during a scan.

[0065] FIG. 8 depicts an example dashboard 800 that depicts what may be displayed to a user after clicking on a task, for example, the Brain Orientation task 702. The open Brain Orientation task 702 includes the name 802, algorithm 804, notes 806, which DMPR / MPRs the transformation or orientation will apply to 808, and the graphical manipulation viewports 810a, 810b, 810c, 81 Od. The notes may include notes to the technician related to the task protocol and / or include notes created by the technician related to the imaging scan or subject. The user can click on a spot or point in each of the viewports 810a, 810b, 810c (e.g., along the axis) and drag to rotate the axis 812a, 812b, 812c to align the axis 812a, 812b, 812c with the imagedepicted in each viewport 810a, 810b, 810c. The lower right viewport 81 Od may only be a depiction and may not be manipulable by the user. In some examples, the same transformations (e.g., transformation matrix) is applied to multiple DMPR tasks. That is, the changes made to the alignment of the axis 812a, 812b, 812c and images in the viewports may be applied to multiple DMPR tasks from other images of the same scan (e.g., other views of a similar geometry or region of interest, such as the brain, neck, etc. listed on the left side of the dashboard.). After the user has aligned the axis, as depicted in FIG. 9, the user may click next to proceed to the next screen. Though the dashboard 800 of FIG. 9 depicts the axis 812a, 812b, 812c has been rotated to align with the image in each viewport 810a, 810b, 810c, in alternative embodiments, the image may be rotated to align with the axis.

[0066] FIG. 10 depicts an example dashboard 1000 that may be presented to a user after the auto-straightening has been completed. As discussed in conjunction with the method 400 of FIG. 4, the example auto-straightening may be applied to multiple views and images simultaneously. This reduces the total time spent by the user manipulating the images to straighten the images. In the illustrated example, the viewport 810d of the dashboard 1000 includes the original orientation of the image, rather than an updated version. Including the original orientation may be an indication to the user what has been changed in the image and may also remind the user what version of the image is currently stored. For example, the changes to the stored version of the image may not be applied until the user completes the task and accepts or applies the changes. Additionally, after the auto-straightening is complete, the user may fine-tune the alignment of the images, if needed, by manually rotating the images by clicking on a spot or point of the image in the viewports 810a, 810b, and 810c and dragging to rotate the images and / or the axis. If the axis is rotated, the system may perform the auto-straightening task again, such that the other views or images related to the DMPR task are also adjusted. If straightening is complete, the user may click next to move on to the next portion of the DMPR task.

[0067] FIG. 11 depicts an example dashboard 1100, which may be presented to a user after the straightening task is complete. FIG. 11 depicts the user selecting or identifying a geometry of the scan image. In the illustrated example, the selected geometry is a brain of the subject. The user highlights the brain geometry on each axial image displayed in the viewports 810a, 810b, 810c. In the illustrated example, selecting the geometry includes using an adjustable bounding box 1102a, 1102b, 1102c to set the coverage and view in each viewport 810a, 810b, 810c. In someexamples, the user may select a geometry and the system may define a preliminary bounding box 1102a, 1102b, 1102c, which then may be adjusted by the user. For example, the bounding box 1102a, 1102b, 1102c may be automatically defined based on landmarks (e.g., a sternal notch, a brain stem, etc.) identified and detected by the system. In such examples, the user verifies the boundaries of the bounding box 1102a, 1102b, 1102c and may manually refine or adjust the boundaries of the bounding box 1102a, 1102b, 1102c. In other examples, the user defines the boundaries of the bounding box 1102a, 1102b, 1102c. The geometry may correspond with the region of interest of the scan.

[0068] FIG. 12 depicts an example dashboard 1200, which may be presented to a user after completing the selection of the geometry. The user may then select which views the alignment, straightening, and selection of geometry are applied to. In the illustrated example, the user has checked boxes 1202a, 1202b, 1202c corresponding to a sagittal brain view, a coronal brain view, and an axial brain view. The user may at this point add additional views and or geometries (e.g., sagittal, coronal, and axial neck views) or may apply the changes to the selected view by selecting the apply button to create the reconstruction. In some examples, the changes made via the DMPR tasks may be defined after a scout scan, and also applied to the images from an imaging scan.

[0069] FIG. 13 depicts an example dashboard 1300, which may be presented to a user after applying the changes. As depicted in FIG. 13, an indicator (e.g., a color) of the DMPR tasks associated with a brain may be changed to indicate to the user that those DMPR tasks are complete (e.g., that the images have been transformed). As shown, the tasks related to the neck need to be completed. The user may select the neck task and repeat the process for aligning, straightening, and selecting the geometry of the images for the neck DMPR tasks.

[0070] The benefits of the proposed workflow include reduced overall processing time for reconstructions, which is particularly important for trauma cases which are traditionally time consuming and time critical, reduced manual work performed by the technologist or radiologist, and more consistent reconstructions by reducing discrepancies caused by human variances.

[0071] FIGS. 1-13 show example configurations with relative positioning of the various components. If shown directly contacting each other, or directly coupled, then such elements may be referred to as directly contacting or directly coupled, respectively, at least in one example. Similarly, elements shown contiguous or adjacent to one another may be contiguous oradjacent to each other, respectively, at least in one example. As an example, components laying in face-sharing contact with each other may be referred to as in face-sharing contact. As another example, elements positioned apart from each other with only a space there-between and no other components may be referred to as such, in at least one example. As yet another example, elements shown above / below one another, at opposite sides to one another, or to the left / right of one another may be referred to as such, relative to one another. Further, as shown in the figures, a topmost element or point of element may be referred to as a “top” of the component and a bottommost element or point of the element may be referred to as a “bottom” of the component, in at least one example. As used herein, top / bottom, upper / lower, above / below, may be relative to a vertical axis of the figures and used to describe positioning of elements of the figures relative to one another. As such, elements shown above other elements are positioned vertically above the other elements, in one example. As yet another example, shapes of the elements depicted within the figures may be referred to as having those shapes (e.g., such as being circular, straight, planar, curved, rounded, chamfered, angled, or the like). Further, elements shown intersecting one another may be referred to as intersecting elements or intersecting one another, in at least one example. Further still, an element shown within another element or shown outside of another element may be referred as such, in one example. It will be appreciated that one or more components referred to as being “substantially similar and / or identical” differ from one another according to manufacturing tolerances (e.g., within 1-5% deviation). FIGS. 1-13 are shown approximately to scale, however, other dimensions may be used if desired.

[0072] When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The terms “first,” “second,” and the like, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. As the terms “connected to,” “coupled to,” etc. are used herein, one object (e.g., a material, element, structure, member, etc.) can be connected to or coupled to another object regardless of whether the one object is directly connected or coupled to the other object or whether there are one or more intervening objects between the one object and the other object. In addition, it should be understood that references to “one embodiment” or “an embodiment” of the present disclosure are not intended to be interpreted as excluding the existence of additionalembodiments that also incorporate the recited features.

[0073] In addition to any previously indicated modification, numerous other variations and alternative arrangements may be devised by those skilled in the art without departing from the spirit and scope of this description, and appended claims are intended to cover such modifications and arrangements. Thus, while the information has been described above with particularity and detail in connection with what is presently deemed to be the most practical and preferred aspects, it will be apparent to those of ordinary skill in the art that numerous modifications, including, but not limited to, form, function, manner of operation and use may be made without departing from the principles and concepts set forth herein. Also, as used herein, the examples and embodiments, in all respects, are meant to be illustrative only and should not be construed to be limiting in any manner.

Claims

CLAIMS1. A method for performing a Direct Multi -Planar Reformat task for a medical imaging system, the method comprising: acquiring, via the medical imaging system, an image of a subject; automatically straightening, via a Direct Multi-Planar Reformat module, the image based a transformation matrix; and applying, via the Direct Multi-Planar Reformat module, the transformation matrix to a plurality of images having a corresponding orientation of the image.

2. The method of claim 1, further comprising defining the orientation of the image and mapping Direct Multi-Planar Reformat tasks to the defined orientation.

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

4. The method of claim 3, wherein aligning the axis includes rotating the axis overlaid on the image.

5. The method of claim 1, further comprising providing a notification to a user, via a display, that a Direct Multi-Planar Reformat 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 a geometry of the image by defining a bounding box on the image.

8. The method of claim 7, wherein the Direct Multi -Planar Reformat module defines the bounding box based on a 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, via the operator console, the bounding box.

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 to the user an indication that the additional plurality of images has 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 imagining system comprising performing a Direct Multi-Planar Reformat task, the medical imaging system comprising:an X-ray source; a detector, wherein the X-ray source and the detector acquire images of a subject; a computing device, wherein the computing device is in communication with a mass storage to store and retrieve the images of a subject; and a Direct Multi-Planar Reformat module coupled to the computing device, the Direct Multi- Planar Reformat module configured to: retrieve, via the computing, an image of a subject acquired by the X-ray source and detector; automatically straighten the image based a transformation matrix; and apply the transformation matrix to a plurality of images having a corresponding orientation of the image.

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

14. The system of claim 13, wherein the transformation matrix is determined by aligning, using the display, an axis overlaid on the slice of the image.

15. The system of claim 14, wherein aligning the axis includes rotating the axis overlaid on the slice of the image.

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

17. The system of claim 16, wherein the Direct Multi-Planar Reformat defines the bounding box based on a geometry selected by the user, and wherein the user verifies the boundaries of the bounding box using the operator console.

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 to the user, via a display, an indication that the additional plurality of images has been transformed.

19. A method for performing a Direct Multi-Planar Reformat task for a medical imaging system, the method comprising: retrieving, from a mass storage, an image of a subject; retrieving a transformation matrix associated with a Direct Multi-Planar Reformat task associated with the image; and applying, via a Direct Multi-Planar Reformat module, the transformation matrix to a plurality of images having a corresponding orientation of the image.

20. The method of claim 19, further comprising defining the orientation of the image and mapping Direct Multi-Planar Reformat tasks to the defined orientation.

Images