Abstract: Methods and systems are provided for aiding a subject of a medical imaging exam in performing coached breathing. In one embodiment, a method for a medical imaging system comprises generating a three-dimensional (3-D) virtual representation of a portion of a surface of a chest and/or abdomen of a subject of the medical imaging system, the portion adjustable in size by an operator of the medical imaging system; displaying changes in the 3-D virtual representation to the subject on a display device of the medical imaging system while the subject breathes, in real time; displaying instructions to the subject on the display device to perform coached breathing in accordance with a selected breathing pattern, using the 3-D virtual representation as a guide; and in response to detecting a deviation of a breathing pattern of the subject from the selected breathing pattern, indicating the deviation to the subject.

Abstract: Computer processing techniques are described for computed tomography (CT) value adjustment of CT images. According to an example, determining, by a system comprising a processor, an adjustment to a calibration vector used to reconstruct CT scan data into first CT image data, wherein determining the adjustment is based on difference between a current CT value of a region in the first CT image data and a target CT value for the region. The method further comprises modifying, by the system, the calibration vector based on the adjustment, resulting in a modified calibration vector, and reconstructing, by the system, the CT scan data into second CT image data using the modified calibration vector, resulting in the region in the second CT image data comprising the target CT value.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to image enhancement using a generative adversarial network (GAN). The computer-implemented system can comprise a memory that can store computer-executable components. The computer-implemented system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a training component that can train a discriminator of the GAN to score a texture of a CT image, wherein the texture can be derived from a difference of two conditionally independent estimates produced by a generator of the GAN by respectively processing two independent noisy samples of images.

Abstract: Various methods and systems are provided for artifact reduction with resolution preservation. In one example, a method includes obtaining projection data of an imaging subject, identifying a metal-containing region in the projection data, interpolating the metal-containing region to generate interpolated projection data, extracting high frequency content information from the projection data in the metal-containing region, adding the extracted high frequency content information to the interpolated projection data to generate adjusted projection data, and reconstructing one or more diagnostic images from the adjusted projection data.

Abstract: Various methods and systems are provided for artifact reduction with resolution preservation. In one example, a method includes obtaining projection data of an imaging subject, identifying a metal-containing region in the projection data, interpolating the metal-containing region to generate interpolated projection data, extracting high frequency content information from the projection data in the metal-containing region, adding the extracted high frequency content information to the interpolated projection data to generate adjusted projection data, and reconstructing one or more diagnostic images from the adjusted projection data.

Abstract: A method for reconstructing an image of a subject with a medical imaging system is provided. Image data is acquired with the medical imaging system, typically in an undersampled manner. A prior image constrained compressed sensing (PICCS) image reconstruction method is then implemented to reconstruct images of the subject being imaged. The prior image used in such a method is produced from so-called averaged image data. The averaged image data is produced by effectively averaging signal information associated with a plurality of different slice locations along a direction orthogonal to the plane parallel to the slice locations. Weightings are calculated from images reconstructed in a conventional manner from the acquired image data, and these weightings are employed to produce the averaged image data. By producing a prior image in this manner, a higher signal-to-noise ratio is achievable, allowing trade-offs with factors such as radiation dose in x-ray imaging.

Abstract: A method for reconstructing an image of a subject with a medical imaging system is provided. Image data is acquired with the medical imaging system, typically in an undersampled manner. A prior image constrained compressed sensing (PICCS) image reconstruction method is then implemented to reconstruct images of the subject being imaged. The prior image used in such a method is produced from so-called averaged image data. The averaged image data is produced by effectively averaging signal information associated with a plurality of different slice locations along a direction orthogonal to the plane parallel to the slice locations. Weightings are calculated from images reconstructed in a conventional manner from the acquired image data, and these weightings are employed to produce the averaged image data. By producing a prior image in this manner, a higher signal-to-noise ratio is achievable, allowing trade-offs with factors such as radiation dose in x-ray imaging.