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: A system and method for identifying a patient-specific neurosurgery target location is provided. The system receives brain imaging data for a patient that includes tracts and networks in the patient brain, accesses a quantitative connectome atlas comprising population-based, disease-specific structural and functional connectivity maps comprising a pattern of tracts and networks associated with an optimal target area (OTA) identified from a population of patients, and defines the patient-specific neurosurgery target location based on a comparison between a pattern of the tracts and networks from the brain imaging data for the patient and the pattern of tracts and networks associated with the OTA identified from the population of patients in the quantitative connectome atlas.

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: A system and method for optimizing parameters of a DBS pulse signal for treatment of a patient is provided. In predicting optimal DBS parameters, functional brain data is input into a predictor system, the functional brain data acquired responsive to a sweeping across a multi-dimensional parameter space of one or more DBS parameters. Statistical metrics of brain response are extracted from the functional brain data for one or more ROIs or voxels of the brain via the predictor system, and a DBS functional atlas is accessed, via the predictor system, that comprises disease-specific brain response maps derived from DBS treatment at optimal DBS parameter settings for a plurality of diseases or neurological conditions. One or more optimal DBS parameters are predicted for the patient based on the statistical metrics of brain response and the DBS functional atlas via the predictor system.

Abstract: A system and method for optimizing parameters of a DBS pulse signal for treatment of a patient is provided. In predicting optimal DBS parameters, functional brain data is input into a predictor system, the functional brain data acquired responsive to a sweeping across a multi-dimensional parameter space of one or more DBS parameters. Statistical metrics of brain response are extracted from the functional brain data for one or more ROIs or voxels of the brain via the predictor system, and a DBS functional atlas is accessed, via the predictor system, that comprises disease-specific brain response maps derived from DBS treatment at optimal DBS parameter settings for a plurality of diseases or neurological conditions. One or more optimal DBS parameters are predicted for the patient based on the statistical metrics of brain response and the DBS functional atlas via the predictor system.

Abstract: A system and method for optimizing parameters of a DBS pulse signal for treatment of a patient is provided. In predicting optimal DBS parameters, functional brain data is input into a predictor system, the functional brain data acquired responsive to a sweeping across a multi-dimensional parameter space of one or more DBS parameters. Statistical metrics of brain response are extracted from the functional brain data for one or more ROIs or voxels of the brain via the predictor system, and a DBS functional atlas is accessed, via the predictor system, that comprises disease-specific brain response maps derived from DBS treatment at optimal DBS parameter settings for a plurality of diseases or neurological conditions. One or more optimal DBS parameters are predicted for the patient based on the statistical metrics of brain response and the DBS functional atlas via the predictor system.

Abstract: A system and method for identifying a patient-specific neurosurgery target location is provided. The system receives brain imaging data for a patient that includes tracts and networks in the patient brain, accesses a quantitative connectome atlas comprising population-based, disease-specific structural and functional connectivity maps comprising a pattern of tracts and networks associated with an optimal target area (OTA) identified from a population of patients, and defines the patient-specific neurosurgery target location based on a comparison between a pattern of the tracts and networks from the brain imaging data for the patient and the pattern of tracts and networks associated with the OTA identified from the population of patients in the quantitative connectome atlas.

Abstract: A system and method for optimizing parameters of a DBS pulse signal for treatment of a patient is provided. In predicting optimal DBS parameters, functional brain data is input into a predictor system, the functional brain data acquired responsive to a sweeping across a multi-dimensional parameter space of one or more DBS parameters. Statistical metrics of brain response are extracted from the functional brain data for one or more ROIs or voxels of the brain via the predictor system, and a DBS functional atlas is accessed, via the predictor system, that comprises disease-specific brain response maps derived from DBS treatment at optimal DBS parameter settings for a plurality of diseases or neurological conditions. One or more optimal DBS parameters are predicted for the patient based on the statistical metrics of brain response and the DBS functional atlas via the predictor system.