Abstract: Methods and systems are provided for identifying motion in medical images. In one example, a method includes obtaining projection data of an imaging subject, reconstructing a first image of a location of the imaging subject from the projection data using a first reconstruction technique and reconstructing a second image corresponding to the same location of the imaging subject from the of projection data using a second reconstruction technique, different than the first reconstruction technique in terms of temporal sensitivity, calculating an inconsistency metric quantifying temporal inconsistencies between the first image and the second image, and taking an action based on the inconsistency metric.

Abstract: Various methods and systems are provided for an x-ray imaging system. In one example, a method for the system includes, responsive to operation of the x-ray imaging system in an automated mode, generating a difference image from a first image and a second image. The difference image is displayed at a display device to allow motion outside of a region of interest (ROI) to be detected based on analysis of the difference image. Further, the difference image is displayed before a contrast agent reaches the ROI.

Abstract: An imaging system and method for using the imaging system are described. In one example, a method of creating representative position information of a patient during a medical imaging scan is provided. The method includes, prior to performing a medical imaging scan, performing initial LiDAR data acquisition to obtain baseline LiDAR data, performing segmentation of the baseline LiDAR data, wherein the baseline LiDAR data includes a three-dimensional (3D) point cloud model, performing continuous LiDAR data acquisition to obtain continuous LiDAR data, which includes a real-time 3D point cloud model, and comparing the baseline LiDAR data with the continuous LiDAR data to detect motion of a patient.

Abstract: The current disclosure provides methods and systems to reduce an amount of noise in image data. In one example, a method for creating synthetic computed tomography (CT) images for training a model to reduce an amount of noise in acquired CT images is proposed, comprising performing a tissue segmentation of reference images of an anatomical region of a subject to determine a set of different tissue types of the reference images; and generating synthetic CT images of the reference images by assigning CT image values of the synthetic CT images based on the different tissue types.

Abstract: Systems and methods for obtaining images are described. A method may include detecting a patient residing on a table proximate the system and detecting one or more artifact areas within an anatomical scan range of the patient. In some examples, the method can include generating an artifact area indicator for the system to display, wherein the artifact area indicator comprises data indicating a location for each of the one or more artifact areas that reside within the anatomical scan range of the patient.

Abstract: Systems and methods are provided for performing computed tomography (CT) scans using different detector materials within a single CT system. In one embodiment, a CT system comprises a hybrid detector assembly coupled to a rotatable portion of a gantry of the CT system, the hybrid detector assembly including a plurality of detector arrays, the detector arrays separated along a direction parallel to an axis of motion of a table of the CT system, each detector array of the plurality of detector arrays including a different detector material. The hybrid detector assembly may be mounted on a translation mechanism configured to allow a position of the hybrid detector assembly to change within the rotatable portion of the gantry between a first acquisition of projection data and a second acquisition of projection data, to center an x-ray tube on a detector array of the hybrid detector assembly having a desired detector material.

Abstract: Various systems are provided for a head and/or extremity holder for a medical imaging system. In one example, the head and/or extremity holder is a cradle interface pad for use with an imaging system, the cradle interface pad comprising a first wall on a first side and a second wall on a second side, opposite the first side, wherein the first wall and the second wall form a concave recess of the cradle interface pad, at least one slot extending through each of the first wall and the second wall, a curved base coupling the first wall and the second wall, at least one attachment point mounted on each of the first wall and the second wall and extending towards the curved base, and a handle positioned on a top face of each of the first wall and the second wall.

Abstract: Various systems are provided for a support assembly for a medical imaging system. In one example, a system comprising, a support assembly for use with an imaging system, the support assembly comprising, a cradle including a base and opposing sidewalls, and a plurality of pads shaped to seat against the base within an opening formed between the opposing sidewalls, where each pad of the plurality of pads supports a head or extremity of a subject to be imaged at a different angle relative to the base.

Abstract: In one embodiment, a method of obtaining a computed tomography scan of a region of interest includes determining a region of interest. The region of interest is a portion of an object being scanned. The method also includes selectin an appropriate static filter of a pre-patient collimator from a plurality of pre-patient collimators and positioning the selected static filter of the pre-patient collimator in a path of an x-ray beam. The method may also include adjusting a table of the CT system to position the region of interest in the path of the region of interest.

Abstract: Noise preserving models and methods for resolution recovery of x-ray computed tomography (e.g., using a computerized tool) are enabled. For example, a system can comprise: a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a pair generation component that generates a pair of images, the pair of images comprising an input image and a ground truth image, a training component that trains a machine learning based sharpening algorithm by approximately minimizing a loss function that determines an error between a sharpened image and the ground truth image, and a sharpening component that, using the sharpening algorithm, sharpens the input image to generate the sharpened image, wherein the sharpened image comprises a second noise that is similar in intensity to a first noise of the input image.

Abstract: Techniques are described that facilitate generating neural network (NNs) tailored to optimize specific properties of medical images using novel loss functions. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a training component that trains a NN to generate a modified version of computed tomography (CT) data comprising one or more optimized properties relative to the CT data using a loss function tailored to control learning adaptation of the NN based on error attributed to one or more defined components associated with the CT data, resulting in a trained NN, wherein the one or more defined components comprise at least one of a frequency component or a spatial feature component.

Abstract: Methods and systems are provided for identifying motion in medical images. In one example, a method includes obtaining projection data of an imaging subject, reconstructing a first image of a location of the imaging subject from the projection data using a first reconstruction technique and reconstructing a second image corresponding to the same location of the imaging subject from the of projection data using a second reconstruction technique, different than the first reconstruction technique in terms of temporal sensitivity, calculating an inconsistency metric quantifying temporal inconsistencies between the first image and the second image, and taking an action based on the inconsistency metric.

Abstract: Systems and methods for obtaining images are described. A method may include detecting a patient residing on a table proximate the system and detecting one or more artifact areas within an anatomical scan range of the patient. In some examples, the method can include generating an artifact area indicator for the system to display, wherein the artifact area indicator comprises data indicating a location for each of the one or more artifact areas that reside within the anatomical scan range of the patient.

Abstract: A CT system includes a gantry having a rotatable base and having an opening for receiving an object to be scanned, an x-ray source, a CT detector, and a computer programmed to detect a mis-registration at a slab boundary between a first slab and a second slab of a reconstructed image, quantify an amount of mis-registration at the slab boundary, and adjust the reconstructed image at the slab boundary based on the quantification.

Abstract: An imaging system allows for modification of data acquisition parameters, where the parameters affect dose distribution. The system includes a library of exams, and a computer programmed to obtain a set of scanning parameters for image data acquisition and image reconstruction, obtain scan data from the library of exams that is based on a similarity metric for a patient to be scanned, simulate a new scan of the patient using the obtained scan data and using the obtained set of scanning parameters, and generate a new set of scanning parameters based on the simulation.

Abstract: An imaging system allows for modification of data acquisition parameters, where the parameters affect dose distribution. The system includes a library of exams, and a computer programmed to obtain a set of scanning parameters for image data acquisition and image reconstruction, obtain scan data from the library of exams that is based on a similarity metric for a patient to be scanned, simulate a new scan of the patient using the obtained scan data and using the obtained set of scanning parameters, and generate a new set of scanning parameters based on the simulation.