Abstract: Systems and methods for deep learning based magnetic resonance imaging (MRI) examination acceleration are provided. The method of deep learning (DL) based magnetic resonance imaging (MRI) examination acceleration comprises acquiring at least one fully sampled reference k-space data of a subject and acquiring a plurality of partial k-space of the subject. The method further comprises grafting the plurality of partial k-space with the at least one fully sampled reference k-space data to generate a grafted k-space for accelerated examination. The method further comprises training a deep learning (DL) module using the fully sampled reference k-space data and the grafted k-space to remove the grafting artifacts.

Abstract: Systems and methods for workflow management for labeling the subject anatomy are provided. The method comprises obtaining at least one localizer image of a subject anatomy using a low-resolution medical imaging device. The method further comprises labeling at least one anatomical point within the at least one localizer image. The method further comprises extracting using a machine learning module a mask of the at least one localizer image comprising the at least one anatomical point label. The method further comprises using the mask to label at least one anatomical point on a high-resolution image of the subject anatomy based on the at least one anatomical point within the localizer image.

Abstract: Systems and methods for deep learning based magnetic resonance imaging (MRI) examination acceleration are provided. The method of deep learning (DL) based magnetic resonance imaging (MRI) examination acceleration comprises acquiring at least one fully sampled reference k-space data of a subject and acquiring a plurality of partial k-space of the subject. The method further comprises grafting the plurality of partial k-space with the at least one fully sampled reference k-space data to generate a grafted k-space for accelerated examination. The method further comprises training a deep learning (DL) module using the fully sampled reference k-space data and the grafted k-space to remove the grafting artifacts.

Abstract: Various methods and systems are provided for translating magnetic resonance (MR) images to pseudo computed tomography (CT) images. In one embodiment, a method comprises acquiring an MR image, generating, with a multi-task neural network, a pseudo CT image corresponding to the MR image, and outputting the MR image and the pseudo CT image. In this way, the benefits of CT imaging with respect to accurate density information, especially in sparse regions of bone which exhibit with high dynamic range, may be obtained in an MR-only workflow, thereby achieving the benefits of enhanced soft-tissue contrast in MR images while eliminating CT dose exposure for a patient.

Abstract: Techniques are provided for learning loss functions using DL networks and integrating these loss functions into DL based image transformation architectures. In one embodiment, a method is provided that comprising facilitating training, by a system operatively coupled to a processor, a first deep learning network to predict a loss function metric value of a loss function. The method further comprises employing, by the system, the first deep learning network to predict the loss function metric value in association with training a second deep learning network that to perform a defined deep learning task. In various embodiments, the loss function comprises a computationally complex loss function that is not easily implementable in existing deep learning packages, such as a non-differentiable loss function, a feature similarity index match (FSIM) loss function, a system transfer function, a visual information fidelity (VIF) loss function and the like.

Abstract: A system and method for estimating image intensity bias and segmentation tissues is presented. The system and method includes obtaining a first image data set and at least a second image data set, wherein the first and second image data sets are representative of an anatomical region in a subject of interest. Furthermore, the system and method includes generating a baseline bias map by processing the first image data set. The system and method also includes determining a baseline body mask by processing the second image data set. In addition, the system and method includes estimating a bias map corresponding to a sub-region in the anatomical region based on the baseline body mask. Moreover, the system and method includes segmenting one or more tissues in the anatomical region based on the bias map.

Abstract: A system and method for estimating image intensity bias and segmentation tissues is presented. The system and method includes obtaining a first image data set and at least a second image data set, wherein the first and second image data sets are representative of an anatomical region in a subject of interest. Furthermore, the system and method includes generating a baseline bias map by processing the first image data set. The system and method also includes determining a baseline body mask by processing the second image data set. In addition, the system and method includes estimating a bias map corresponding to a sub-region in the anatomical region based on the baseline body mask. Moreover, the system and method includes segmenting one or more tissues in the anatomical region based on the bias map.