Abstract: A system for optimizing DBS parameters for a subject includes automatically performing actions via a processor. The actions include obtaining functional MRI data of a brain of the subject acquired utilizing an MRI system during DBS of the brain utilizing a first set of DBS parameters. The actions include generating functional MRI response maps from the functional MRI data. The actions include extracting, utilizing an unsupervised autoencoder-based neural network, features from the functional MRI response maps. The actions include determining, utilizing a deep learning-based DBS parameter classification model, whether the first set of DBS parameters are optimal DBS parameters for the subject based on the features. The actions include, when the first set of DBS parameters are not the optimal DBS parameters, predicting, utilizing a deep learning-based DBS parameter prediction model, a second set of DBS parameters that are the optimal DBS parameters for the subject based on the features.

Abstract: A method for generating an image of a subject with a magnetic resonance imaging (MRI) system is presented. The method includes first performing a localizer scan of the subject to acquire localizer scan data. A machine learning (ML) module is then used to detect the presence of metal regions in the localizer scan data based on magnitude and phase information of the localizer scan data. Based on the detected metal regions in the localizer scan data, the MRI workflow is adjusted for diagnostic scan of the subject. The image of the subject is then generated using the adjusted workflow.

Abstract: The present discussion relates to structures and devices to facilitate application of an ultrasound therapy beam to a target anatomic region in a replicable manner. In certain aspects, an alignment controller may be used to analyze images generated by an ultrasound transducer. The alignment controller may then send a communication to indicate the energy application device is positioned to provide therapy to the target region, or if the device needs to be repositioned. The alignment control of the energy application device provides guided repeatable targeting of the target anatomic region, even when in non-clinical settings.

Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.

Abstract: A deep learning-based continuous federated learning network system is provided. The system includes a global site comprising a global model and a plurality of local sites having a respective local model derived from the global model. The plurality of model tuning modules having a processing system are provided at the plurality of local sites for tuning the respective local model. The processing system is programmed to receive incremental data and select one or more layers of the local model for tuning based on the incremental data. Finally, the selected layers are tuned to generate a retrained model.

Abstract: A computer implemented method is provided. The method includes establishing, via multiple processors, a continuous federated learning framework including a global model at a global site and respective local models derived from the global model at respective local sites. The method also includes retraining or retuning, via the multiple processors, the global model and the respective local models without sharing actual datasets between the global site and the respective local sites but instead sharing synthetic datasets generated from the actual datasets.

Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.

Abstract: A medical imaging system includes at least one medical imaging device providing image data of a subject and a processing system programmed to generate a plurality of training images having simulated medical conditions by blending a pathology region from a plurality of template source images to a plurality of target images. The processing system is further programmed to train a deep learning network model using the plurality of training images and input the image data of the subject to the deep learning network model. The processing system is further programmed to generate a medical image of the subject based on the output of the deep learning network model.

Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.

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 computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates.

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.

Abstract: A method for controlling a physical process includes receiving an input dataset corresponding to the physical process. The method further includes determining a data model based on the input dataset. The data model includes a plurality of latent space variables of a machine learning model. The method also includes receiving a plurality of reference models corresponding to a plurality of classes. Each of the plurality of reference models includes a corresponding plurality of latent space variables. The method includes comparing the data model with each of the plurality of reference models to generate a plurality of distance metric values. The method further includes selecting a reference model among the plurality of reference models based on the plurality of distance metric values. The method also includes controlling the physical process based on the selected reference model.

Abstract: The present approach relates to the processing of edge information related to graph topology using a neural network. In one aspect, graph topology information along with edge weights are added as a first hidden layer of a neural network. In this manner, better spatial information is transferred to the neural network.