A method to visualize, display, analyze and quantify angiography, perfusion, and the change in angiography and perfusion in real time, is provided. This method captures image data sequences from indocyanine green near infra-red fluorescence imaging used in a variety of surgical procedure applications, where angiography and perfusion are critical for intraoperative decisions.

Systems and methods are disclosed for providing a cardiovascular score for a patient. A method includes receiving, using at least one computer system, patient-specific data regarding a geometry of multiple coronary arteries of the patient; and creating, using at least one computer system, a three-dimensional model representing at least portions of the multiple coronary arteries based on the patient-specific data. The method also includes evaluating, using at least one computer system, multiple characteristics of at least some of the coronary arteries represented by the model; and generating, using at least one computer system, the cardiovascular score based on the evaluation of the multiple characteristics. Another method includes generating the cardiovascular score based on evaluated multiple characteristics for portions of the coronary arteries having fractional flow reserve values of at least a predetermined threshold value.

The present disclosure generally relates to systems and methods and systems of a noninvasive technique for characterizing cardiac chamber size and cardiac mechanical function. A mathematical analysis of three-dimensional (3D) high resolution data may be used to estimate chamber size and cardiac mechanical function. For example, high-resolution mammalian signals are analyzed across multiple leads, as 3D orthogonal (X,Y,Z), or 10-channel data, for 30 to 800 seconds, to derive estimates of cardiac chamber size and cardiac mechanical function. Multiple mathematical approaches may be used to analyze the dynamical and geometrical properties of the data.

A system and method is provided for generating a finite element (FE) model of an anatomical structure based on a fitted model (340) of the anatomical structure and association data. A segmentation model (310) may be provided for segmenting the anatomical structure. Association data may be obtained which associates a segmentation model part (315) of the segmentation model (310) with a mesh property, the segmentation model part (315) representing a pre-determined anatomical region of interest. The segmentation model may be applied to a medical image (320) of a subject, thereby obtaining a fitted model (340) providing a segmentation of the anatomical structure (330). The finite element model (350) may then be generated based on the fitted model (340) and the association data, said generating comprising meshing a finite element model part of the finite element model in accordance with the mesh property, the finite element model part corresponding with the pre-determined anatomical region of interest. Advantageously, this may result in an efficient generation of the FE model needing fewer manual iterations and/or alterations in the model or in the mesh.

An intravascular sensor device can be used to guide treatment of a diseased blood vessel in the body of a patient. In some examples, the intravascular sensor device includes a pressure sensor and an ultrasound transducer. The intravascular sensor device is used to measure a pressure within the diseased blood vessel and acquire an ultrasound image of the diseased blood vessel. The pressure may be measured during hyperemic blood flow that is caused by a pharmacologic vasodilator drug. The measured pressure can be used to calculate a fractional flow reserve value. The ultrasound image can be used to determine a physical dimension of the blood vessel, such as cross-sectional area. The fractional flow reserve value and physical dimensions of the blood vessel can be used to optimize patient treatment.

Disclosed are devices, systems, and methods for determining the dipole densities on heart walls. In particular, a triangularization of the heart wall is performed in which the dipole density of each of multiple regions correlate to the potential measured at various located within the associated chamber of the heart. To create a database of dipole densities, mapping information recorded by multiple electrodes located on one or more catheters and anatomical information is used. In addition, skin electrodes may be implemented. Additionally, one or more ultrasound elements are provided, such as on a clamp assembly or integral to a mapping electrode, to produce real time images of device components and surrounding structures.

Embodiments of the present disclosure are related to providing a method and device processing a first set of volumetric image data comprising cross-sectional images of a myocardium and displaying a second set of volumetric image data of the myocardium. A curved plane to rectangular plane transformation of cross-sectional images of myocardium of human heart is proposed. After the transformation, a combined and reconstructed set of myocardium images are superimposed with a modified Bull's Eye View (BEV) map and corresponding parameters indicating extent of fibrosis to obtain a second set of volumetric image data of myocardium. In addition to quantifying and displaying the extent of fibrosis, the proposed solution preserves neighborhood and adjacency criteria of abnormal tissues of myocardium walls of human heart.

Systems and methods of valve quantification are disclosed. In one embodiment, a method of mitral valve quantification is provided. The method includes generating a 3-D heart model, defining a 3-D mitral valve annulus, fitting a plane through the 3-D mitral valve annulus, measuring the distance between at least two papillary muscle heads, defining an average diameter of at least one cross section around the micro valve annulus, and determining a size of an implant to be implanted.

An automatic recognition and classification method for electrocardiogram heartbeat based on artificial intelligence, comprising: processing a received original electrocardiogram digital signal to obtain heartbeat time sequence data and lead heartbeat data; cutting the lead heartbeat data according to the heartbeat time sequence data to generate lead heartbeat analysis data; performing data combination on the lead heartbeat analysis data to obtain a one-dimensional heartbeat analysis array; performing data dimension amplification and conversion according to the one-dimensional heartbeat analysis array to obtain four-dimensional tensor data; and inputting the four-dimensional tensor data to a trained LepuEcgCatNet heartbeat classification model, to obtain heartbeat classification information. The method overcomes the defect that the conventional method only depends on single lead independent analysis for result summary statistics and thus classification errors are more easily obtained, and the accuracy of the electrocardiogram heartbeat classification is greatly improved.

A computing system (118) includes a computer readable storage medium (122) with computer executable instructions (124), including a biophysical simulator (126) and an electrocardiogram signal analyzer (128). The computing system further includes a processor (120) configured to execute the electrocardiogram signal analyzer determine myocardial infarction characteristics from an input electrocardiogram and to execute the biophysical simulator to simulate a fractional flow reserve or an instant wave-free ratio index from input cardiac image data and the determined myocardial infarction characteristics.