A method includes, in a processor, receiving example representations of geometrical shapes of a given type of organ. In a training phase, a neural network model is trained using the example representations. In a modeling phase, the trained neural network model is applied to a set of location measurements acquired in an organ of the given type, to produce a three-dimensional model of the organ.

The invention provides methods and systems for imaging.

An imaging steering apparatus includes sensors and an imaging processor configured for: acquiring, via multiple ones of the sensors and from a current position (322), and current orientation (324), an image of an object of interest; based on a model, segmenting the acquired image; and determining, based on a result of the segmenting, a target position (318), and target orientation (320), with the target position and/or target orientation differing correspondingly from the current position and/or current orientation. An electronic steering parameter effective toward improving the current field of view may be computed, and a user may be provided instructional feedback (144) in navigating an imaging probe toward the improving. A robot can be configured for, automatically and without need for user intervention, imparting force (142) to the probe to move it responsive to the determination.

Embodiments include methods of identifying a personalized cardiovascular device based on patient-specific geometrical information, the method comprising acquiring a geometric model of at least a portion of a patient's vascular system; obtaining one or more geometric quantities of one or more blood vessels of the geometric model of the patient's vascular system; determining the presence or absence of a pathology characteristic at a location in the geometric model of the patient's vascular system; generating an objective function defined by a plurality of device variables and a plurality of hemodynamic and solid mechanics characteristics; and optimizing the objective function using computational fluid dynamics and structural mechanics analysis to identify a plurality of device variables that result in desired hemodynamic and solid mechanics characteristics.

Embodiments discussed herein facilitate generation of a prognosis for recurrence or non-recurrence of atrial fibrillation (AF) after pulmonary vein isolation (PVI). A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prognosis for AF after PVI based on radiographic images, alone or in combination with clinical features. A second set of embodiments discussed herein relates to determination of a prognosis for a patient for AF after PVI based on radiographic images, alone or in combination with clinical features.

A method for determining hemodynamic parameters may be provided. The method may include obtaining image data of a subject. The method may include generating a first vascular model and a second vascular model based on the image data and coupling the first vascular model with the second vascular model using an intermediate model to form a coupled vascular model. The method may also include setting at least one of a first boundary condition of the first vascular model or a second boundary condition of the second vascular model and determining a flow field distribution of the coupled vascular model based on the at least one of the first boundary condition or the second boundary condition. The method may further include determining hemodynamic parameters based on the flow field distribution.

Provided herein is a catheter assembly including an imaging device for identifying an anatomical structure. The catheter assembly includes a patient cannula configured to be drawn along a catheter or guide wire; a transseptal puncture catheter at least partially enclosed within the patient cannula; and an imaging catheter. The imaging catheter includes a transducer configured to emit an energy beam capable of reflecting from an anatomical structure and to detect energy reflected from the structure. The catheter assembly also includes a transmitter for conveying a signal representative of the detected energy from the transducer to a signal processor for obtaining information about the structure. An imagining system and a method for identifying a predetermined transseptal puncture location on an atrial septum are also provided herein.

Methods and systems for identifying optimized ablation targets for treating and preventing arrhythmias sustained by reentrant circuits are described. The methods comprise receiving at least one mesh generated from one or more images of a patient's heart, receiving activation data generated from one or more simulations of electrical-signal propagation over the at least one mesh, generating at least one flow graph based on the activation data and the at least one mesh, and applying a max-flow min-cut algorithm to the at least one flow graph to determine at least one of a number, one or more dimensions, and one or more locations of one or more ablation targets. Non-transitory computer-readable media storing a set of instructions for treating and preventing arrhythmias sustained by reentrant circuits are also described.

Various embodiments include methods and diagnostic systems implementing the methods for determining a prognostic prediction of a likelihood of success or a likelihood of complications of an electrophysiology procedure at the identified area of electrophysiological interest. Various embodiments may include generating a patient-specific three-dimensional (3D) cardiac activation and arrythmia localization model identifying an area of electrophysiological interest for performing an electrophysiology procedure to treat the arrythmia, using the 3D heart model to identify heart structures near the identified area of electrophysiological interest, determining a prognostic indication of an electrophysiology procedure performed at the identified area of electrophysiological interest based at least in part on one or more heart structures near the area of electrophysiological interest, and generating an output providing a prognostic indication of an electrophysiology procedure at the identified area of electrophysiological interest based at least in part on the determined likelihood of success.

A method for delineating a ventricle from MRI data relating to the heart of a patient, the method comprising: a) providing a contrast-enhanced cine MRI dataset; b) providing one or more additional MRI datasets; c) segmenting one or more features on the additional MRI dataset or datasets; d) mapping the segmented features to the contrast-enhanced cine MRI dataset; and e) using the segmented features as mapped in step d) to assist segmentation of the ventricle on the contrast-enhanced cine MRI dataset.
A corresponding device and computer program are also disclosed.