Abstract: Systems and methods for determining an optimal position of one or more ablation electrodes are provided. A current state of an environment is defined based on a mask of one or more anatomical objects and one or more current positions of one or more ablation electrodes. The one or more anatomical objects comprise one or more tumors. For each particular AI (artificial intelligence) agent of one or more AI agents, one or more actions for updating the one or more current positions of a respective ablation electrode of the one or more ablation electrodes in the environment are determined based on the current state using the particular AI agent. A next state of the environment is defined based on the mask and the one or more updated positions of the respective ablation electrode.

Abstract: The disclosure relates to techniques for automatically characterizing liver tissue of a patient, comprising receiving morphological magnetic resonance image data set and at least one magnetic resonance parameter map of an imaging region comprising at least partially the liver of the patient, each acquired by a magnetic resonance imaging device, via a first interface. The techniques further include applying a trained function comprising a neural network to input data comprising at least the image data set and the parameter map. At least one tissue score describing the liver tissue is generated as output data, which is provided using a second interface.

Abstract: Systems and methods are provided for enhancing a medical image. An initial medical image having an initial field of view is received. An augmented medical image having an expanded field of view is generated using a trained machine learning model. The expanded field of view comprises the initial field of view and an augmentation region. The augmented medical image is output.

Abstract: For quantification of blood flow by an ultrasound system, B-mode images generated with a multi-transmit, coherent image formation produce swirling or other speckle patterns in the blood regions. These patterns, as represented in specially formed B-mode images, are tracked over time to indicate two or three-dimensional velocity vectors of the blood at a B-mode resolution. Various visualizations may be provided at the same resolution, including the velocity flow field, flow direction, vorticity, vortex size, vortex shape, and/or divergence.

Abstract: A generative adversarial network (GAN) (21, 24), or any other generative modeling technique, is used to learn (12) how to generate (68) an optimal robotic system given performance, operation, safety, or any other specifications. For instance, the specifications may be modeled (65) relative to anatomy to confirm satisfaction of anatomy-based or another task specific constraint. A machine-learning system, for instance neural network, is trained (12) to translate given specifications to a robotic configuration. The network may convert task-specific specifications into one or more configurations of robot modules into a robotic system. The user may enter (67) changes to performance in order for the network to estimate (62) appropriate configurations. The configurations may be converted (64) to estimated performance by another machine-learning system, for instance neural network, allowing modeling (65) of operation relative to the anatomy, such as anatomy based on medical imaging.

Abstract: For robotically operating a catheter, translation and/or rotation manipulation is provided along the shaft or away from the handle, such as near a point of access to the patient. A worm drive arrangement may allow for both translation and rotation of the shaft. Some control may be provided by robotic manipulation of the handle, while other control (e.g., fine adjustments) are made by robotic manipulation of the shaft.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: A computer-implemented method for planning a thermal ablation of a target object within a biological body includes acquiring an object image within the body, determining an object position within the body from the image, determining external body surface position relative to the object position from the image, acquiring, for an initial set of ablation needles those of types for the ablation, and for each type, a set of characterizing features common to all needles of a same type, including a fixed and/or variable parameter. A neural ordinary differential equation algorithm receives a characterizing feature, external surface position, object position, algorithm for outputting an ablation plan, including a final set of needles for ablating the object, and for each needle of the final set, type, trajectory from the external surface, and optionally, a variable parameter value. The plan is provided through an interface to guide a clinician for object ablation.

Abstract: For quantification of blood flow by an ultrasound system, B-mode images generated with a multi-transmit, coherent image formation produce swirling or other speckle patterns in the blood regions. These patterns, as represented in specially formed B-mode images, are tracked over time to indicate two or three-dimensional velocity vectors of the blood at a B-mode resolution. Various visualizations may be provided at the same resolution, including the velocity flow field, flow direction, vorticity, vortex size, vortex shape, and/or divergence.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: For robotically operating a catheter, translation and/or rotation manipulation is provided along the shaft or away from the handle, such as near a point of access to the patient. A worm drive arrangement may allow for both translation and rotation of the shaft. Some control may be provided by robotic manipulation of the handle, while other control (e.g., fine adjustments) are made by robotic manipulation of the shaft.

Abstract: Systems and methods for navigating a catheter in a patient using a robotic navigation system with risk management are provided. An input medical image of a patient is received. A trajectory for navigating a catheter from a current position to a target position in the patient is determined based on the input medical image using a trained segmentation network. One or more actions of a robotic navigation system for navigating the catheter from the current position towards the target position and a confidence level associated with the one or more actions are determined by a trained AI (artificial intelligence) agent and based on the generated trajectory and a current view of the catheter. In response to the confidence level satisfying a threshold, the one or more actions are evaluated based on a view of the catheter when navigated according to the one or more actions.

Abstract: For robotically operating a catheter, a medical catheter is controlled by rotation of the catheter as well as steering in one or more planes of a distal end of the catheter. To robotically rotate the catheter, a handle is rotated. The steering is performed separately using one or more knobs on the handle. The rotation of the handle complicates the robotic control of the knob. A mechanical decoupling is used so that rotation of the handle maintains the position of the knob relative to the handle. Gearing or transmission is used to avoid independent control of the knob and handle rotation. In an alternative or additional approach, the handle may be robotically controlled while also guiding the catheter shaft spaced away from the handle, allowing fine-tuned control of the catheter at the access point to the patient.

Abstract: A method and system for deep learning based cardiac electrophysiological model personalization is disclosed. Electrophysiological measurements of a patient, such as an ECG trace, are received. A computational cardiac electrophysiology model is personalized by calculating patient-specific values for a parameter of the computational cardiac electrophysiology model based at least on the electrophysiological measurements of the patient using a trained deep neural network (DNN). The parameter of the computational cardiac electrophysiology model corresponds to a spatially varying electrical cardiac tissue property.

Abstract: Systems and methods for automatically navigating a catheter in a patient are provided. An image of a current view of a catheter in a patient is received. A set of actions of a robotic navigation system for navigating the catheter from the current view towards a target view is determined using a machine learning based network. The catheter is automatically navigated in the patient from the current view towards the target view using the robotic navigation system based on the set of actions.

Abstract: Systems and methods for predicting a patient specific risk of cardiac events for cardiac arrhythmia are provided. A medical image sequence of a heart of a patient is received. Cardiac function features are extracted from the medical image sequence. Additional features are extracted from patient data of the patient. A patient specific risk of a cardiac event is predicted based on the extracted cardiac function features and the extracted additional features.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.

Abstract: For robotically operating a catheter, a medical catheter is controlled by rotation of the catheter as well as steering in one or more planes of a distal end of the catheter. To robotically rotate the catheter, a handle is rotated. The steering is performed separately using one or more knobs on the handle. The rotation of the handle complicates the robotic control of the knob. A mechanical decoupling is used so that rotation of the handle maintains the position of the knob relative to the handle. Gearing or transmission is used to avoid independent control of the knob and handle rotation. In an alternative or additional approach, the handle may be robotically controlled while also guiding the catheter shaft spaced away from the handle, allowing fine-tuned control of the catheter at the access point to the patient.

Abstract: Systems and methods for generating an ablation map identifying target ablation locations on a heart of a patient are provided. One or more input medical images of a heart of a patient and a voltage map of the heart of the patient are received. An ablation map identifying target ablation locations on the heart is generated using one or more trained machine learning based models based on the one or more input medical images and the voltage map. The ablation map is output.

Abstract: Methods and systems for image registration using an intelligent artificial agent are disclosed. In an intelligent artificial agent based registration method, a current state observation of an artificial agent is determined based on the medical images to be registered and current transformation parameters. Action-values are calculated for a plurality of actions available to the artificial agent based on the current state observation using a machine learning based model, such as a trained deep neural network (DNN). The actions correspond to predetermined adjustments of the transformation parameters. An action having a highest action-value is selected from the plurality of actions and the transformation parameters are adjusted by the predetermined adjustment corresponding to the selected action. The determining, calculating, and selecting steps are repeated for a plurality of iterations, and the medical images are registered using final transformation parameters resulting from the plurality of iterations.