Abstract: An image correction system and method takes an initial volume reconstructed from a number of projections obtained by the imaging system as the sole input to the image correction system. In a first step, the image correction system reconstructs a number of reconstructed or forward projections from the initial volume. In a second and optionally concurrent step, the image correction system forms a corrected volume by applying an artificial intelligence/deep learning/convolutional neural network model to the initial volume. In a third step, the image correction system employs the forward projections and the corrected volume as inputs to an iterative reconstruction process to achieve an optimized volume as an output from the image correction system. The use of the initial volume as the only input to the image correction system simplifies the computational processes of the image processing system while providing an optimized image having improved detail and image quality.

Abstract: An image processing system and method is provided for correcting artefacts within a three-dimensional (3D) volume reconstructed from a plurality of two-dimensional (2D) projection images of an object. The system and method is implemented on an imaging system having a processing unit operable to control the operation of a radiation source and a detector to generate a plurality of 2D projection images. The system also includes a memory connected to the processing unit and storing processor-executable code that when executed by the processing unit operates to reconstruct the 3D volume from the plurality of 2D projection images, the 3D volume defined in a pseudo parallel geometry based on a zero angle from the plurality of 2D projection images, wherein reconstructing the 3D volume comprises reconstructing a 3D virtual object defined in a pseudo parallel geometry based on the zero angle from the plurality of 2D projection images, and correcting the 3D virtual object to form the 3D volume.

Abstract: A magnetic resonance (MR) imaging method performed by an MR imaging system includes acquiring MR data in multiple shots and multiple acquisitions (NEX), separately reconstructing the component magnitude and phase of images corresponding to the multiple shots and multiple NEX, removing the respective phase from each of the images, and combining, after removal of the respective phase, the shot images and the NEX images to produce a combined image. The method further includes using the combined image to calculate the full k-space data for each shot and NEX and replacing unacquired k-space data points with calculated k-space data points. The operations are repeated until the combined image reaches a convergence.

Abstract: A magnetic resonance (MR) imaging method performed by an MR imaging system includes acquiring MR data in multiple shots and multiple acquisitions (NEX), separately reconstructing the component magnitude and phase of images corresponding to the multiple shots and multiple NEX, removing the respective phase from each of the images, and combining, after removal of the respective phase, the shot images and the NEX images to produce a combined image.