Abstract: A method for reconstructing dynamic contrast-enhanced (DCE) MR images includes receiving a plurality of continuous free-breathing DCE images, the plurality of images obtained with a contrast, sorting the images by identifying a respiratory phase associated with each of the continuous free-breathing DCE images, reconstructing the plurality continuous free-breathing DCE images into a 4D respiratory motion-resolved image, obtaining 3D deformable motion vector fields (MVFs), and utilizing a deep learning based motion transformation integrated forward-Fourier (DL-MOTIF) model and the 3D deformable MVFs to reconstruct the DCE MR images.

Abstract: A system for image reconstruction includes an input for receiving image data, a processor, and a memory. The memory stores instructions that cause the processor to reconstruct an image from the image data using a self-supervised deep learning model.

Abstract: A method for reconstructing dynamic contrast-enhanced (DCE) MR images includes receiving a plurality of continuous free-breathing DCE images, the plurality of images obtained with a contrast, sorting the images by identifying a respiratory phase associated with each of the continuous free-breathing DCE images, reconstructing the plurality continuous free-breathing DCE images into a 4D respiratory motion-resolved image, obtaining 3D deformable motion vector fields (MVFs), and utilizing a deep learning based motion transformation integrated forward-Fourier (DL-MOTIF) model and the 3D deformable MVFs to reconstruct the DCE MR images.

Abstract: A computer-implemented method of reconstructing magnetic resonance (MR) images of a subject is provided. The method includes executing a neural network model for analyzing MR images, wherein the neural network model is trained with a first subset of training MR images as inputs and a second subset of the training MR images as outputs, wherein each image in the first subset is acquired during a neighboring respiratory phase of at least one of the images in the second subset. The method further includes receiving MR signals, reconstructing crude MR images based on the MR signals, analyzing the crude MR images using the neural network model, deriving clear MR images based on the analysis, wherein the clear MR images include reduced artifacts, compared to the crude MR images, and outputting the clear MR images.

Abstract: A system for image reconstruction includes an input for receiving image data, a processor, and a memory. The memory stores instructions that cause the processor to reconstruct an image from the image data using a self-supervised deep learning model.

Abstract: Systems and methods for image reconstruction for parallel MR imaging are disclosed that receive a k-space single-coil measurement dataset that includes at least two k-space single-coil measurement sets, transforming the k-space single-coil measurement dataset to an estimated CSM using a coil sensitivity estimation module, and transforming the k-space single-coil measurement dataset and the estimated CSM into a final MR image using an MRI reconstruction module. In some aspects, the coil sensitivity estimation module and MRI reconstruction module include deep learning neural networks trained without the use of ground truth data.

Abstract: A computer-implemented method of reconstructing magnetic resonance (MR) images of a subject is provided. The method includes executing a neural network model for analyzing MR images, wherein the neural network model is trained with a first subset of training MR images as inputs and a second subset of the training MR images as outputs, wherein each image in the first subset is acquired during a neighboring respiratory phase of at least one of the images in the second subset. The method further includes receiving MR signals, reconstructing crude MR images based on the MR signals, analyzing the crude MR images using the neural network model, deriving clear MR images based on the analysis, wherein the clear MR images include reduced artifacts, compared to the crude MR images, and outputting the clear MR images.