Abstract: Workflows for automatic measurement of Doppler is provided. In various embodiments, a plurality of frames of a medical video are read. A mode label indicative of a mode of each of the plurality of frames is determined. At least one of the plurality of frames is provided to a trained feature generator. The at least one of the plurality of frames have the same mode label. At least one feature vector is obtained from the trained feature generator corresponding to the at least one of the plurality of frames. At least one feature vector is provided to a trained classifier. A valve label indicative of a valve is obtained from the trained classifier corresponding to the at least one of the plurality of frames. One or more measurement is extracted indicative of a disease condition from those of the at least one of the plurality of frames matching a predetermined valve label.

Abstract: Workflows for automatic measurement of Doppler is provided. In various embodiments, a plurality of frames of a medical video are read. A mode label indicative of a mode of each of the plurality of frames is determined. At least one of the plurality of frames is provided to a trained feature generator. The at least one of the plurality of frames have the same mode label. At least one feature vector is obtained from the trained feature generator corresponding to the at least one of the plurality of frames. At least one feature vector is provided to a trained classifier. A valve label indicative of a valve is obtained from the trained classifier corresponding to the at least one of the plurality of frames. One or more measurement is extracted indicative of a disease condition from those of the at least one of the plurality of frames matching a predetermined valve label.

Abstract: Workflows for automatic measurement of Doppler is provided. In various embodiments, a plurality of frames of a medical video are read. A mode label indicative of a mode of each of the plurality of frames is determined. At least one of the plurality of frames is provided to a trained feature generator. The at least one of the plurality of frames have the same mode label. At least one feature vector is obtained from the trained feature generator corresponding to the at least one of the plurality of frames. At least one feature vector is provided to a trained classifier. A valve label indicative of a valve is obtained from the trained classifier corresponding to the at least one of the plurality of frames. One or more measurement is extracted indicative of a disease condition from those of the at least one of the plurality of frames matching a predetermined valve label.

Abstract: A system for estimating fractional flow reserve (FFR) includes a front end application to receive image frames from an imaging system to develop a model of a vasculature system based on an observed concentration time profile at locations within the model using contrast dye in the vasculature system and movement of the vasculature system. A graphics processing unit is configured to represent a computed concentration time profile in the vasculature system using a Lattice-Boltzmann Method (LBM) to generate a representation of the vasculature system. A dynamic controller tunes a velocity field based on a mismatch between the observed concentration time profile and the computed concentration time profile at the locations within the model to obtain a best estimate of the velocity field to perform a FFR measurement.

Abstract: A system for estimating fractional flow reserve (FFR) includes a front end application to receive image frames from an imaging system to develop a model of a vasculature system based on an observed concentration time profile at locations within the model using contrast dye in the vasculature system and movement of the vasculature system. A graphics processing unit is configured to represent a computed concentration time profile in the vasculature system using a Lattice-Boltzmann Method (LBM) to generate a representation of the vasculature system. A dynamic controller tunes a velocity field based on a mismatch between the observed concentration time profile and the computed concentration time profile at the locations within the model to obtain a best estimate of the velocity field to perform a FFR measurement.

Abstract: Multi-step vessel segmentation and analysis is provided. In various embodiments, a branch detection algorithm is applied to a medical image to determine a plurality of branches of a blood vessel appearing in the medical image. A plurality of segments of the blood vessel appearing in the medical image is determined. Each of the plurality of segments is bounded by one of the plurality of branches. An edge detection algorithm is applied piecewise to each of the plurality of segments. A composite blood vessel outline is formed from the piecewise edge detection.

Abstract: Workflows for automatic measurement of Doppler is provided. In various embodiments, a plurality of frames of a medical video are read. A mode label indicative of a mode of each of the plurality of frames is determined. At least one of the plurality of frames is provided to a trained feature generator. The at least one of the plurality of frames have the same mode label. At least one feature vector is obtained from the trained feature generator corresponding to the at least one of the plurality of frames. At least one feature vector is provided to a trained classifier. A valve label indicative of a valve is obtained from the trained classifier corresponding to the at least one of the plurality of frames. One or more measurement is extracted indicative of a disease condition from those of the at least one of the plurality of frames matching a predetermined valve label.

Abstract: Multi-step vessel segmentation and analysis is provided. In various embodiments, a branch detection algorithm is applied to a medical image to determine a plurality of branches of a blood vessel appearing in the medical image. A plurality of segments of the blood vessel appearing in the medical image is determined. Each of the plurality of segments is bounded by one of the plurality of branches. An edge detection algorithm is applied piecewise to each of the plurality of segments. A composite blood vessel outline is formed from the piecewise edge detection.

Abstract: A system for estimating fractional flow reserve (FFR) includes a front end application to receive image frames from an imaging system to develop a model of a vasculature system based on an observed concentration time profile at locations within the model using contrast dye in the vasculature system and movement of the vasculature system. A graphics processing unit is configured to represent a computed concentration time profile in the vasculature system using a Lattice-Boltzmann Method (LBM) to generate a representation of the vasculature system. A dynamic controller tunes a velocity field based on a mismatch between the observed concentration time profile and the computed concentration time profile at the locations within the model to obtain a best estimate of the velocity field to perform a FFR measurement.