Abstract: Systems and methods for joint reconstruction and segmentation of organs from magnetic resonance imaging (MRI) data are provided. Sparse MRI data is received at a computer system, which jointly processes the MRI data using a plurality of reconstruction and segmentation processes. The MRI data is processed using a joint reconstruction and segmentation process to identify an organ from the MRI data. Additionally, the MRI data is processed using a channel-wise attention network to perform static reconstruction of the organ from the MRI data. Further, the MRI data can is processed using a motion-guided network to perform dynamic reconstruction of the organ from the MRI data. The joint processing allows for rapid static and dynamic reconstruction and segmentation of organs from sparse MRI data, with particular advantage in clinical settings.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating a thickness of an anatomical structure based on a visual representation of the anatomical structure and a machine-learned thickness prediction model. The visual representation may include an image or a segmentation mask of the anatomical structure. The thickness prediction model may be learned based on ground truth information derived by applying a partial differential equation such as Laplace's equation to the visual representation and solving the partial differential equation. When the visual representation includes an image of the anatomical structure, the systems, methods and instrumentalities described herein may also be capable of generating a segmentation mask of the anatomical structure based on the image.

Abstract: Described herein are systems, methods and instrumentalities associated with motion tracking and strain determination. A motion tracking apparatus as described herein may track the motion of an anatomical structure from a source image to a target image and determine corresponding points on one or more surfaces of the anatomical structure in both the source image and the target image. Using these surface points, the motion tracking apparatus may calculate one or more strain parameters associated with the anatomical structure and provide the strain parameters for medical diagnosis and/or treatment.

Abstract: For k-space trajectory infidelity correction, a model is machine trained to correct k-space measurements in k-space. K-space trajectory infidelity correction uses deep learning. Trajectory infidelity is corrected from a k-space point of view. Since the image artifacts arise from k-space acquisition distortion, a machine learning model is trained to correct in k-space, either changing values of k-space measurements or estimating the trajectory shifts in k-space.

Abstract: For k-space trajectory infidelity correction, a model is machine trained to correct k-space measurements in k-space. K-space trajectory infidelity correction uses deep learning. Trajectory infidelity is corrected from a k-space point of view. Since the image artifacts arise from k-space acquisition distortion, a machine learning model is trained to correct in k-space, either changing values of k-space measurements or estimating the trajectory shifts in k-space.

Abstract: Described herein are systems, methods and instrumentalities associated with motion tracking and strain determination. A motion tracking apparatus as described herein may track the motion of an anatomical structure from a source image to a target image and determine corresponding points on one or more surfaces of the anatomical structure in both the source image and the target image. Using these surface points, the motion tracking apparatus may calculate one or more strain parameters associated with the anatomical structure and provide the strain parameters for medical diagnosis and/or treatment.

Abstract: Described herein are neural network-based systems, methods and instrumentalities associated with estimating a thickness of an anatomical structure based on a visual representation of the anatomical structure and a machine-learned thickness prediction model. The visual representation may include an image or a segmentation mask of the anatomical structure. The thickness prediction model may be learned based on ground truth information derived by applying a partial differential equation such as Laplace's equation to the visual representation and solving the partial differential equation. When the visual representation includes an image of the anatomical structure, the systems, methods and instrumentalities described herein may also be capable of generating a segmentation mask of the anatomical structure based on the image.

Abstract: Systems and methods for joint reconstruction and segmentation of organs from magnetic resonance imaging (MRI) data are provided. Sparse MRI data is received at a computer system, which jointly processes the MRI data using a plurality of reconstruction and segmentation processes. The MRI data is processed using a joint reconstruction and segmentation process to identify an organ from the MRI data. Additionally, the MRI data is processed using a channel-wise attention network to perform static reconstruction of the organ from the MRI data. Further, the MRI data can is processed using a motion-guided network to perform dynamic reconstruction of the organ from the MRI data. The joint processing allows for rapid static and dynamic reconstruction and segmentation of organs from sparse MRI data, with particular advantage in clinical settings.

Abstract: Various approaches provide improved segmentation from raw data. Training samples are generated by medical imaging simulation from digital phantoms. These training samples provide raw measurements, which are used to learn to segment. The segmentation task is the focus, so image reconstruction loss is not used. Instead, an attention network is used to focus the training and trained network on segmentation. Recurrent segmentation from the raw measurements is used to refine the segmented output. These approaches may be used alone or in combination, providing for segmentation from raw measurements with less influence of noise or artifacts resulting from a focus on reconstruction.

Abstract: Various approaches provide improved segmentation from raw data. Training samples are generated by medical imaging simulation from digital phantoms. These training samples provide raw measurements, which are used to learn to segment. The segmentation task is the focus, so image reconstruction loss is not used. Instead, an attention network is used to focus the training and trained network on segmentation. Recurrent segmentation from the raw measurements is used to refine the segmented output. These approaches may be used alone or in combination, providing for segmentation from raw measurements with less influence of noise or artifacts resulting from a focus on reconstruction.