CathWorks Ltd.

Methods and systems for manually assisted definition of vascular features are described. In some embodiments, a method provides for editing of vascular paths by enabling a user to drag an erroneously segmented region of a selected vascular path into alignment with a more correctly segmented position that is depicted as a blood vessel in a vascular image. The method may use an energy function, defined as a function of position along the segmentation of the selected blood vessel, to determine how a vascular path is to be moved based on dragging motions provided by the user. In some instances, non-zero regions of the energy function are set based on the position of the selected region.

Systems and methods for displaying flow index values on a user interface. An example method may include receiving medical images imaging a portion of a vasculature of a subject, with the portion of the vasculature including vessels; producing, by automatic processing of the medical images, a three-dimensional vascular model of the portion of the vasculature comprising the one or more vessels based on the medical images; calculating flow index values quantifying vascular function along each of the one or more vessels based on the three-dimensional vascular model; displaying a representation of the three-dimensional vascular model comprising the one or more vessels; and for a designated vessel of the one or more vessels, simultaneously displaying the flow value index for a designated location of the designated vessel along with the flow value index for a predetermined distal location along a length of the designated vessel.

A three-dimensional (3D) sizing tool for cardiac assessment is disclosed. An example system may display a representation of a three-dimensional vascular model including a three-dimensional (3D) sizing tool that surrounds a portion of the three-dimensional vascular model, wherein the portion comprises a volume of the three-dimensional vascular model for which to determine a mapping of geometrical information to a length along the portion; display an interface for adjusting the 3D sizing tool, wherein an area along the interface corresponds to a length of the 3D sizing tool; receive input to adjust the length of the 3D sizing tool via the area along the interface; and adjust the length of the 3D sizing tool according to the input.

An apparatus for performing a vascular assessment is disclosed. The apparatus creates a three-dimensional model that is representative of a coronary vessel tree of a patient based on at least two angiographic images. The apparatus estimates first blood flow resistance values for points along at least some vascular segments of the coronary vessel tree using vascular geometrical dimensions of the three-dimensional model. The apparatus also estimates second blood flow resistance values for the points along the at the least some vascular segments of the coronary vessel tree using a volume of a crown of the vascular segment downstream from the respective point. The apparatus determines fractional flow reserve (“FFR”) by calculating a ration of the first blood flow resistance values and the second blood flow resistance values at each of the points along the at least some vascular segments of the coronary vessel tree.

Systems and methods for enhanced user interface and crosstalk analysis for cardiac index determination. An example method includes accessing a cardiac model of a portion of a patient's heart, the portion including one or more vessels of the patient's heart, and the cardiac model indicating a plurality of lesions along a length of at least one of the vessels; obtaining, based on the cardiac model for the lesions, respective positions along the length for which the lesions are associated with index drops, wherein the index drops are with respect to an index indicative of vascular function; and causing presentation of a user interface, wherein the user interface: presents a graph mapping the length to the index indicative of vascular function, presents individual toggles enabling nulling of individual lesions, and updates the graph in response to received user input to one or more of the toggles, wherein the user input nulls effects of one or more lesions.

An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

Methods for automated identification of vascular features are described. In some embodiments, one or more machine learning (ML)-based vascular classifiers are used, with their results being combined to with results of at least one other vascular classifier in order to produce the final results. Potentially advantages of this approach include the ability to combine certain strengths of ML classifiers with segmentation approaches based on more classical (“formula-based”) methods. These strengths may include particularly the identification of anatomically identified targets mixed within an image also showing similar looking but anatomically distinct targets.

Methods and systems for manually assisted definition of vascular features are described. In some embodiments, a method provides for editing of vascular paths by enabling a user to drag an erroneously segmented region of a selected vascular path into alignment with a more correctly segmented position that is depicted as a blood vessel in a vascular image. The method may use an energy function, defined as a function of position along the segmentation of the selected blood vessel, to determine how a vascular path is to be moved based on dragging motions provided by the user. In some instances, non-zero regions of the energy function are set based on the position of the selected region.

Methods for automated identification of vascular features are described. In some embodiments, one or more machine learning (ML)-based vascular classifiers are used, with their results being combined to with results of at least one other vascular classifier in order to produce the final results. Potentially advantages of this approach include the ability to combine certain strengths of ML classifiers with segmentation approaches based on more classical (“formula-based”) methods. These strengths may include particularly the identification of anatomically identified targets mixed within an image also showing similar looking but anatomically distinct targets.

Systems and methods are described for the compositing together of model-linked vascular data from a plurality of sources, including at least one 2-D angiography image, for display in a frame of reference of the at least one angiography image. In some embodiments, a linking model comprises a data structure configured to link locations of angiographic images to corresponding elements of non-image vascular parameter data. The linking data structure is traversed to obtain a mapping to the frame of reference of one or more of the angiographic images.