A method includes calculating a first medial-axis tree graph of a volume of an organ of a patient in a first computerized anatomical map of the volume, acquired at a first time. A second medial-axis tree graph is calculated, of a volume of the organ of the patient in a second computerized anatomical map of the volume, acquired at a second time that is different from the first time. A deviation is detected and estimated, between the first and second tree-graphs. Using the estimated deviation, the first and second medial-axis tree graphs are registered with one another. Using the registered first and second tree graphs, the first and second computerized anatomical maps are combined.

The present disclosure provides a system for MRI. The system may obtain a plurality of auxiliary signals and a plurality of imaging signals collected by applying an MRI pulse sequence simultaneously to a plurality of slice locations of a subject. For each of at least one target slice location of the plurality of slice locations, the system may generate at least one target image of the target slice location based on the plurality of auxiliary signals and the plurality of imaging signals. During the application of the MRI pulse sequence, phase modulation may be applied to at least one of the plurality of slice locations so that the plurality of slice locations have different phases during the readout of at least one of the plurality of imaging signals.

A system for identifying vortices in a vector map including multiple vectors, the system includes a processor and an output device. The processor is configured to: (i) define one or more closed loops on the vector map, and (ii) for each closed loop, identify a plurality of the vectors that cross the closed loop, calculate a vector sum of the identified vectors, and decide based on the vector sum whether a vortex is located inside the closed loop. The output device is configured to indicate one or more identified vortices to a user.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

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.

Systems and methods for late gadolinium enhancement (“LGE”) tissue viability imaging in a dynamic (e.g., temporally-resolved) manner using magnetic resonance imaging (“MRI”) are provided. Dynamic LGE images can be generated throughout the entire cardiac cycle at high temporal resolution in a single breath-hold. Dynamic, semi-quantitative longitudinal relaxation maps are acquired and retrospective synthetization of dynamic LGE images is implemented using those semi-quantitative longitudinal relaxation maps.

According to one embodiment, a medical image processing apparatus includes processing circuitry. The processing circuitry estimates a contour of a desired structure based on a medical image, receives a desired correction mode among multiple correction modes for correcting the estimated contour, and corrects the estimated contour according to the desired correction mode.

In an example, a signal segment evaluator can be programmed to evaluate a morphology of at least one electrophysiological signal to identify a signal segment of interest. The morphology of the signal segment of interest can be indicative of an electrophysiological event of a patient during a respective time interval. A reconstruction engine can be programmed to reconstruct electrophysiological signals on a surface of interest within a body of the patient based on the electrophysiological signals measured from an outer surface of the patient and geometry data representing an anatomy of the patient. A map generator can be programmed to generate a map representing the reconstructed electrophysiological signals on the surface of interest for the respective time interval of the signal segment of interest. A target generator can be programmed to identify a target site within the patient's body based on the map for the electrophysiological event.

A computer-implemented method for determining an orientation of at least one diagnostically relevant sectional plane for heart imaging in a three-dimensional magnetic resonance imaging image dataset, comprises: providing the three-dimensional image dataset; applying a trained function to the three-dimensional image dataset to determine a position of at least one landmark; determining the orientation of the at least one diagnostically relevant sectional plane as a function of at least one landmark; and providing the orientation of the at least one diagnostically relevant sectional plane.

A system and method for cardiac function and myocardial strain analysis include techniques and structure for classifying a set of cardiac images according to their views, detecting a heart range and valid short-axis slices in the set of cardiac images, determining heart segment locations, segmenting heart anatomies for each time frame and each slice, calculating volume related parameters, determining key physiological time points, calculating myocardium transmural thickness and deriving a cardiac function measure from the myocardium transmural thickness at the key physiological time points, estimating a dense motion field from the key physiological time points as applied to the set of cardiac images, calculating myocardial strain along different myocardium directions from the dense motion field, and providing the cardiac function measure and myocardial strain calculation to a user through a user interface.