The present invention provides an image processing method to assess quantitative myocardial blood flow and/or myocardial flow reserve, comprising the steps of: (a) pre-processing of images comprises: (i) reconstructing dynamic cine 3D tomographic myocardial perfusion imaging (MPI) data, (ii) optionally, denoising to improve the quality of image, (iii) extracting blood input function from a region of interest (ROI) of the left ventricle blood cavity, (iv) estimating the distribution volume (DV), given by the ratio of uptake and washout rates (K.sub.1/k.sub.2) to stabilize and improve estimation of K.sub.1, k.sub.2 and total blood volume (TBV) and subsequent myocardial blood flow measures, and (v) data normalization by dividing by the maximum of the blood input function; (b) assessing the individual signals pre-processed in step (a) in order to generate K.sub.1 and TBV parametric maps using artificial neural network; (c) post-processing of K.sub.1, k.sub.2 and TBV parametric maps; and of rest and stress myocardial blood flow to estimate myocardial flow reserve (MFR) and/or coronary flow reserve (CFR).

Systems and methods are disclosed for assessing organ and/or tissue transplantation by estimating blood flow through a virtual transplant model by receiving a patient-specific anatomical model of the intended transplant recipient; receiving a patient-specific anatomical model of the intended transplant donor, the model including the vasculature of the organ or tissue that is intended to be transplanted to the recipient; constructing a unified model of the connected system post transplantation, the connected system including the transplanted organ or tissue from the intended transplant donor and the vascular system of the intended transplant recipient; receiving one or more blood flow characteristics of the connected system; assessing the suitability for an actual organ or tissue transplantation using the received blood flow characteristics; and outputting the assessment into an electronic storage medium or display.

Systems and methods are disclosed for determining anatomy directly from raw medical acquisitions using a machine learning system. One method includes obtaining raw medical acquisition data from transmission and collection of energy and particles traveling through and originating from bodies of one or more individuals; obtaining a parameterized model associated with anatomy of each of the one or more individuals; determining one or more parameters for the parameterized model, wherein the parameters are associated with the raw medical acquisition data; training a machine learning system to predict one or more values for each of the determined parameters of the parametrized model, based on the raw medical acquisition data; acquiring a medical acquisition for a selected patient; and using the trained machine learning system to determine a parameter value for a patient-specific parameterized model of the patient.

The present invention relates to peripheral perfusion measurement. In order to provide more detailed peripheral perfusion characteristics for better knowledge about a current situation, a device (10) for peripheral perfusion measurement is provided that comprises an image data input (12), a data processor (14) and an output interface (16). The image data input receives at least one perfusion angiographic 2D X-ray image of a region of interest of a subject's foot and a 3D foot-model comprising spatial perfusion-related parameters. The data processor registers the 3D foot-model with the foot in the at least one perfusion angiographic X-ray image. The registering comprises a pose-estimation of the foot in the 2D X-ray image. The information is mapped between the 2D image and the 3D foot-model based on the pose-estimation. Image processing modification instructions are identified based on the mapped information. Further, the at least one image signal is modified based on the image processing modification instructions. The output interface provides the at least one modified image signal. In a first example, a regional perfusion analysis is provided. In a second example, a normalization of the perfusion signal by the fraction of perfused tissue is provided. In third example, reporting in a 3D model is provided.

Disclosed are methods and systems for signal attenuation-compensated projection-resolved (sacPR) optical coherence tomography angiography (OCTA). The sacPR OCTA may be free of segmentation and vascular contrast enhancement. In some embodiments, projection artifacts may be suppressed with signal compensation including flow and large vessel shadow compensation for projection removal and wavelet-based compensation for noise suppression.

A method for blood flow imaging can include receiving, by a processor coupled to a first memory device comprising a first type of media and a second memory device comprising a second type of media, an indication corresponding to initiation of an application and data captured by an imaging device coupled to the processor. The method can include determining characteristics of a workload corresponding to execution of the application to process the data captured by the imaging device for the first memory device and the second memory device and writing the data captured by the imaging device to the first memory device or the second memory device based on determined characteristics for the first memory device and the second memory device in executing the workload. The method can further include executing the workload as part of executing of the application while the data captured by the imaging device is written to the first memory device or the second memory device that exhibits greater than the threshold set of determined characteristics in executing the workload.

Angiographic recordings are to be made more informative. To this end, a method for spatiotemporal fusion of time-resolved angiographic data sets is proposed. Respective 4D reconstructions are obtained from angiographic 3D data sets acquired from contrast agents administered at different sites. In both 4D reconstructions, a common vascular region is identified. For each contrast agent bolus, the corresponding time point or time course in the common vascular region is determined. Finally, the two 4D reconstructions are synchronized and fused.

System and method for measuring dyslipidemia condition of a subject using thermal imaging is disclosed. The disclosed system and method includes thermal sensors for capturing thermal images and/or videos of a body part; and a processing engine to detect a predefined region of the body part in each frame of the captured images and/or videos. The processing engine segments one or more portions from the predefined region in each frame of the captured images and/or videos to identify a ROI comprising arteries in the segmented portions. Based on the identified region of interest, the engine extracts pixel values, representing biosignals, from each frame of the captured images and/or videos to determine parameters associated with a rate of atherosclerotic, levels of lipids and lipoproteins, and hemodynamic factors of the subject. Further a risk score for the dyslipidemia condition based on the determined parameters using computational models is measured.

It is presented a computer-implemented method for estimating narrowings in arteries of a heart based on a sample myocardial perfusion imaging (S-MPI) data set using an artificial neural network (ANN). The method comprises receiving, in a data processing apparatus, the S-MPI data and a request, determining, in the data processing apparatus, an estimate quantitative coronary angiography (E-QCA) data set based on the S-MPI data set using the ANN, wherein the ANN is trained using a reference MPI (R-MPI) data set, a reference quantitative coronary angiography (R-QCA) data set and a reference invasive measurement (R-IM) data set, wherein each record in the R-MPI data set has a corresponding record in the R-QCA data set and a corresponding record in the R-IM data set, respectively, and transmitting, from the data processing apparatus, the E-QCA data set in response to the request.

A computer implemented method for assessing an arterio-venous malformation (AVM) may include, for example, receiving a patient-specific model of a portion of an anatomy of a patient; using a computer processor to analyze the patient-specific model for identifying one or more blood vessels associated with the AVM, in the patient-specific model; and estimating a risk of an undesirable outcome caused by the AVM, by performing computer simulations of blood flow through the one or more blood vessels associated with the AVM in the patient-specific model.