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.

The present disclosure relates to an image information generation method, a pulse wave measurement system and an electronic device. The method comprises: with respect to a target part, acquiring a first infrared image sequence, each infrared image in the first infrared image sequence including at least a vein pattern; by registering the vein pattern in each infrared image in the first infrared image sequence, correcting each infrared image in the first infrared image sequence, thereby obtaining the corrected first infrared image sequence; removing at least the vein regions from respective infrared images in the corrected first infrared image sequence, to obtain image information of remaining regions as image information for pulse wave measurement.

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 parameterized 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 application relates to a method for acquiring maximum principal strain or a maximum principal stress status of a vessel wall. The method includes: acquiring first vessel data of a first time phase corresponding to a vessel; acquiring second vessel data of a second time phase corresponding to the vessel; generating, based on the first vessel data, a first vessel model relating to the first time phase, generating a second vessel model relating to the second time phase based on the second vessel data; determining a region of interest in the first vessel model; determining the corresponding region of interest in the second vessel model; determining a reference point in the region of interest of the first vessel model; determining the corresponding reference point in the region of interest of the second vessel model; determining a displacement of the reference point from the first vessel model to the second vessel model; and determining a maximum principal strain or a maximum principal stress at the reference point based on the displacement of the reference point.

Systems and methods for determining modified fractional flow reserve values of vascular lesions are provided. Patient physiologic data, including coronary vascular information, is measured. According to the physiologic data, a coronary vascular model is generated. Lesions of interest within the coronary vascular system of the patient are identified for modified fractional flow reserve value determination. The coronary vascular model is modified to generate modified blood flow information for determining the modified fractional flow reserve value.

A method for determining quantitative parameters for dynamic contrast-enhanced MR data includes acquiring a set of contrast-enhanced MR data for a region of interest using a T1-weighted pulse sequence, generating at least one contrast concentration curve based on the set of contrast-enhanced MR data, accessing a comprehensive dictionary of contrast concentration curves and generating a grouped dictionary that has a plurality of groups based on the comprehensive dictionary. Each group includes a plurality of correlated contrast concentration curves and a group representative signal for the group. The method also includes comparing a contrast concentration curve with the group representative signal of each group to select a group, comparing the contrast concentration curve to the plurality of correlated contrast concentration curves in the selected group to identify a set of quantitative parameters for the concentration curve and generating a report including the set of quantitative parameter.

A fuel injection pump for a diesel engine, including: a control rack an in a rack chamber formed between a pump head and a pump housing, and configured to adjust a fuel injection amount; a transmission shaft rotatably supported by a transmission shaft hole formed in the pump housing; and a lubricating oil passage formed in the pump housing, and configured to pressure-feed lubricating oil between the transmission shaft and the transmission shaft hole. The transmission shaft has an oil passage therein through which passage the lubricating oil pressure-fed to the lubricating oil passage partially passes, a first opening of the oil passage is communicated with the lubricating oil passage, and a second opening of the oil passage is formed on the outer circumferential surface of an upper portion of the transmission shaft, nearby the control rack.

A registration facility and a registration method are provided where a pre-interventionally generated simulation model of an examination object is registered with an intra-interventional live image. The simulation model is adapted to the live image using at least one simulated course line of an anatomical feature and/or an instrument by minimizing a line distance metric, specified as a cost function, for a distance between the simulated course line and an actual intra-interventional course of the instrument that is visible in the live image.

Methods and systems for characterizing fluids from a patient are disclosed. The method includes receiving a time series of images of a conduit receiving fluids from the patient, identifying a conduit image region in each of the images, classifying a flow type through the conduit based on an evaluation of the conduit image region in the time series of images, and estimating at least one of a volume of fluids and a quantity of a blood component that has passed through the conduit within a predetermined period of time, based at least in part on the classification of the flow type.

A system and for determining the presence or absence of myocardial ischemia in a subject, based upon analysis of medical images of at least one region of the heart of a subject of interest, the plurality of medical images being acquired in a consecutive manner by a medical imaging modality and being a plurality of myocardial layers in a direction which is generally perpendicular to the wall of the left ventricular myocardium.