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 for computing a transformation for correction motion between a first medical image and a second medical image are provided. One or more landmarks are detected in the first medical image and the second medical image. A first tree of the anatomical structure is generated from the first medical image based on the one or more landmarks detected in the first medical image and a second tree of the anatomical structure is generated from the second medical image based on the one or more landmarks detected in the second medical image. The one or more landmarks detected in the first medical image are mapped to the one or more landmarks detected in the second medical image based on the first tree and the second tree. A transformation to align the first medical image and the second medical image is computed based on the mapping.

Abstract: Systems and method for assisted catheter steering are provided. Instructions for steering a catheter within a patient are received. A graph defining paths between a plurality of configurations of a robotic catheter navigation system is constructed based on the received instructions. Each of the plurality of configurations are associated with a respective view of the patient. A path is determined in the graph to a target configuration of the plurality of configurations of the robotic catheter navigation system. The catheter is automatically steered within the patient based on the determined path in the graph to recover the respective view of the patient associated with the target configuration.

Abstract: Digital twin models of a patient, patient organ, or patient organ system from which biomarkers can be derived are used for clinical decision support. The individualization procedure also includes a predictive consideration (16) to improve the sensitivity and specificity of the digital-twin derived biomarker. In particular, during training, the predictive biomarker for which the individualized model is to be used is taken into account (16), which then accounts for the biomarker in application. The fitting (15) of the model for a specific patient accounts (16) for the prediction or model usage, resulting in estimating (14) biomarkers more optimized for the end use rather than just fit to the current baseline of the patient.

Abstract: A robotic catheter navigation system, and a method for operating the robotic catheter navigation system, are provided. The robotic catheter navigation system comprises a catheter handle, a motor, and a torque transfer disk. The catheter handle comprises a set of gears coupled to a first shaft. The motor is for rotating a second shaft. The torque transfer disk is coupled to the first shaft and the second shaft for transferring the rotation of the second shaft to the first shaft to thereby rotate the set of gears for steering a catheter.

Abstract: A computer-implemented method for decoding brain imaging data of individual subjects by using additional imaging data from other subjects includes receiving a plurality of functional Magnetic Resonance Imaging (fMRI) datasets corresponding to a plurality of subjects. Each fMRI dataset corresponds to a distinct subject and comprises brain activation patterns resulting from presentation of a plurality of stimuli to the distinct subject. A group dimensionality reduction (GDR) technique is applied to the example fMRI datasets to yield a low-dimensional space of response variables shared by the plurality of subjects. A model is trained to predict a set of target variables based on the low-dimensional space of response variables shared by all subjects, wherein the set of target variables comprise one or more characteristics of the plurality of stimuli.

Abstract: A holder facility for holding a medical instrument includes a first receiving element, a second receiving element, and at least three diaphragm elements. The at least three diaphragm elements within a diaphragm layer are arranged between the first and second receiving elements about a common rotation axis. The first and second receiving elements each have an opening for receiving the medical instrument. The first and second receiving elements are movable around about the common rotation axis relative to one another. Each of the at least three diaphragm elements is forcibly moved by mechanical coupling. For a movement of the first receiving element relative to the second receiving element about the common rotation axis, there is a forcibly-guided movement of the at least three diaphragm elements such that the at least three diaphragm elements hold a medical instrument arranged in the opening of the first and second receiving elements.

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: For three-dimensional rendering, a machine-learnt model is trained to generate representation vectors for rendered images formed with different rendering parameter settings. The distances between representation vectors of the images to a reference are used to select the rendered image and corresponding rendering parameters that provides a consistency with the reference. In an additional or different embodiment, optimized pseudo-random sequences are used for physically-based rendering. The random number generator seed is selected to improve the convergence speed of the renderer and to provide higher quality images, such as providing images more rapidly for training compared to using non-optimized seed selection.

Abstract: Systems and methods for personalized sudden cardiac death risk prediction that generates fingerprints of imaging features of cardiac structure and function. One or more fingerprints and clinical data may be used to generate a risk score. The output risk score may be used to predict the time of death in order to select high-risk patients for implantable cardioverter-defibrillator treatment.

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: The disclosure relates to a method of determining a transformation between coordinate frames of sets of image data. The method includes receiving a model of a structure extracted from first source image data, the first source image data being generated according to a first imaging modality and having a first data format, wherein the model has a second data format, different from the first data format. The method also includes determining, using an intelligent agent, a transformation between coordinate frames of the model and first target image data, the first target image data being generated according to a second imaging modality different to the first imaging modality.

Abstract: A correspondence between a source image and a reference image is determined. A generative model corresponds to a prior probability distribution of deformation fields, each deformation field corresponding to a respective coordinate transformation. A conditional model generates a style transfer probability distribution of reference images, given a source image and a deformation field. The first image data is the source image, and the second image data is the reference image. An initial first deformation field is determined. An update process is iteratively performed until convergence to update the first deformation field, to generate a converged deformation field representing the correspondence between the source image and the reference image.

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: 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 3D/3D medical image registration. A digitally reconstructed radiograph (DRR) is rendered from a 3D medical volume based on current transformation parameters. A trained multi-agent deep neural network (DNN) is applied to a plurality of regions of interest (ROIs) in the DRR and a 2D medical image. The trained multi-agent DNN applies a respective agent to each ROI to calculate a respective set of action-values from each ROI. A maximum action-value and a proposed action associated with the maximum action value are determined for each agent. A subset of agents is selected based on the maximum action-values determined for the agents. The proposed actions determined for the selected subset of agents are aggregated to determine an optimal adjustment to the transformation parameters and the transformation parameters are adjusted by the determined optimal adjustment.

Abstract: A system and method for multi-modality fusion for 3D printing of a patient-specific organ model is disclosed. A plurality of medical images of a target organ of a patient from different medical imaging modalities are fused. A holistic mesh model of the target organ is generated by segmenting the target organ in the fused medical images from the different medical imaging modalities. One or more spatially varying physiological parameter is estimated from the fused medical images and the estimated one or more spatially varying physiological parameter is mapped to the holistic mesh model of the target organ. The holistic mesh model of the target organ is 3D printed including a representation of the estimated one or more spatially varying physiological parameter mapped to the holistic mesh model. The estimated one or more spatially varying physiological parameter can be represented in the 3D printed model using a spatially material property (e.g.

Abstract: A method of training a computer system for use in determining a transformation between coordinate frames of image data representing an imaged subject. The method trains a learning agent according to a machine learning algorithm, to determine a transformation between respective coordinate frames of a number of different views of an anatomical structure simulated using a 3D model. The views are images containing labels. The learning agent includes a domain classifier comprising a feature map generated by the learning agent during the training operation. The classifier is configured to generate a classification output indicating whether image data is synthesized or real images data. Training includes using unlabeled real image data to training the computer system to determine a transformation between a coordinate frame of a synthesized view of the imaged structure and a view of the structure within a real image.

Abstract: A method and system for 3D/3D medical image registration. A digitally reconstructed radiograph (DRR) is rendered from a 3D medical volume based on current transformation parameters. A trained multi-agent deep neural network (DNN) is applied to a plurality of regions of interest (ROIs) in the DRR and a 2D medical image. The trained multi-agent DNN applies a respective agent to each ROI to calculate a respective set of action-values from each ROI. A maximum action-value and a proposed action associated with the maximum action value are determined for each agent. A subset of agents is selected based on the maximum action-values determined for the agents. The proposed actions determined for the selected subset of agents are aggregated to determine an optimal adjustment to the transformation parameters and the transformation parameters are adjusted by the determined optimal adjustment.

Abstract: Systems and methods for performing a medical imaging analysis task using a machine learning based motion model are provided. One or more medical images of an anatomical structure are received. One or more feature vectors are determined. The one or more feature vectors are mapped to one or more motion vectors using the machine learning based motion model. One or more deformation fields representing motion of the anatomical structure are determined based on the one or more motion vectors and at least one of the one or more medical images. A medical imaging analysis task is performed using the one or more deformation fields.