A computer-implemented method of determining a value of a blood flow parameter for a vessel, is provided. The method includes: calculating from spectral attenuation data, and using a plurality of different techniques, a value of a blood flow parameter for the vessel; and providing the value of the blood flow parameter for the vessel based on the calculated values of the blood flow parameter.

A method and a system for processing a plurality of camera images of a human body surface target area over time includes the steps of obtaining a plurality of images of a human body surface target area of a human body captured by a first camera over time, obtaining physical data of the human body, and generating a 3D model of at least the human body surface target area.

This disclosure describes examples of dual-modality imaging implementations involving both photoacoustic and fast super-resolution ultrasound localization imaging for non-invasive and non-superficial monitoring of both structural information and physiological activities/parameters in tissues. As an example, such dual-modality imaging may be particularly applied to the monitoring of structural information and physiological activities/parameters associated with the blood-brain barrier (BBB) in brain vascular structures that are subject to intervention/modulation (by, e.g., Focused Ultrasound, or FUS) for purposes of drug delivery from a blood flow to brain tissues.

A method for post-processing a sampled time-dependent experimental perfusion signal to generate a pharmacokinetic parameter is implemented by a processing unit of a medical-imaging analysis system, said unit having been trained beforehand in a process allowing the disrupting effect of acquisition of a perfusion sequence on arterial signals and redundancy of information related to an arterial input function shared by a set of at least two tissual signals to be learnt. Such an arterial input function is produced directly in a step by said processing unit thus trained from a first arterial input function and from tissual signals selected beforehand.

A method and system for detecting possible disease and condition indications on a patient, using combinations of two dimensional, infrared, and three-dimensional (such as dot) cameras. In one embodiment, the cameras are located in a smartphone that the patient uses to aim at parts of the body, wherein features or motion potentially corresponding to indications of disease or conditions are extracted and analyzed. A further embodiment conducts triage for conditions detected or otherwise. Further embodiments use augmented reality overlays to assist the patient through the various procedures, guide in the data collection, and identify to the patient necessary and useful information.

A method for performing flow analysis in a target volume of a moving organ having a long axis, such as the heart, from 4D MR Flow volumetric image data set of such organ, wherein such data set comprises structural information and three-directional velocity information of the target volume over time, the devices, program products and methods comprising, under control of one or more computer systems configured with specific executable instructions: a) deriving from the 4D MR Flow volumetric image data set at least one derived image data set related to the long axis of the moving organ, for example, by using a multi planar reconstruction: b) determining at least one feature of interest in the 4D MR Flow volumetric image data set or in said derived image data set. The feature of interest may be determined, for example, by receiving input from a user or by performing automatic detection steps on the 4D MR Flow volumetric image data set; c) tracking the feature of interest within the 4D MR Flow volumetric image data set or in the derived image data set; d) determining the spatial orientation over time of a plane containing the feature of interest in the 4D MR Flow volumetric image data set; c) performing quantitative flow analysis using velocity information on the plane as determined in step d). A corresponding device and computer program are also disclosed.

A computer-implemented method for evaluating image data depicting a vascular structure for the purpose of automatically determining at least one arterial and/or venous access position of the vascular structure. An embolization may be carried out at at least one peripheral position of a pathological change in the region of the vascular structure by introducing a catheter via an access path which runs through the vascular structure and leads from the access position or one of the access positions to the respective peripheral position. The method includes obtaining the image data comprising a plurality of image points, wherein those image points through whose respectively associated position in the vascular structure a contrast agent flowed during the recording of the image data, or of raw images from which the image data is derived, are each assigned at least one captured item of time information relating to a time point at which the contrast agent reached the respective position, the contrast agent being administered during or before this recording and determining the at least one arterial and/or venous access position on the basis of the at least one item of time information.

The present disclosure describes a system and a method for producing patient-specific small diameter vascular grafts (SDVG) for coronary artery bypass graft (CABG) surgery. In some embodiments, the method for producing SDVGs includes non-invasive quantification of patient-specific coronary and vascular physiology by applying computational fluid dynamics (CFD), rapid prototyping, and in vitro techniques to medical images and coupling the quantified patient-specific coronary and vascular physiology from the CFD to computational fluid-structure interactions and SDVG structural factors to design a patient-specific SDVG.

The present disclosure provides apparatus and methods to both infer stent expansion (vessel expansion or compliance) and probability of procedural success (vessel patency) from pretreatment diagnostic imaging at the point-of-care and also train an inference model to generate such an inference, thus allowing a physician to choose with greater certainty an optimal treatment tool and treatment protocol for treating a vessel of a patient.

Various examples are provided related to predicting perfusion images from non-contrast scans. In one example, a method for predicting perfusion images includes generating perfusion maps of an organ of a subject from non-contrast computed tomography (NCCT) slices of the organ; processing the perfusion maps based upon weights determined by a Physicians-in-the-Loop (PILO) module; and generating synthetic computed tomography perfusion (CTP) maps from the processed perfusion maps, the synthetic CTP maps generated by deep learning-based multimodal image translation. In another example, a system includes at least one computing device that can generate prefusion maps of an organ from NCCT slices; process the perfusion maps based upon weights determined by a PILO module; and generate synthetic CTP maps from the processed perfusion maps using deep learning-based multimodal image translation. The CTP maps can be rendered for display to a user.