DCE MR images are obtained from a MR scanner and under a free-breathing protocol is provided. A neural network assigns a perfusion metric to DCE MR images. The neural network includes an input layer configured to receive at least one DCE MR image representative of a first contrast enhancement state and of a first respiratory motion state and at least one further DCE MR image representative of a second contrast enhancement state and of a second respiratory motion state. The neural network further includes an output layer configured to output at least one perfusion metric based on the at least one DCE MR image and the at least one further DCE MR image. The neural network with interconnections between the input layer and the output layer is trained by a plurality of datasets, each of the datasets having an instance of the at least one DCE MR image and of the at least one further DCE MR image for the input layer and the at least one perfusion metric for the output layer.

Systems and methods for super-resolution ultrasound imaging of microvessels in a subject are described. Ultrasound data are acquired from a region-of-interest in a subject who has been administered a microbubble contrast agent. The ultrasound data are acquired while the microbubbles are moving through, or otherwise present in, the region-of-interest. The region-of-interest may include, for instance, microvessels or other microvascuiature in the subject. By isolating, localizing, tracking, and accumulating the microbubbles in the ultrasound data, super-resolution images of the microvessels can be generated.

An apparatus and a method for contrast-enhanced ultrasound (CEUS) including use of a fluid dynamics model for the analysis of dynamic contrast-enhanced ultrasound (DCEUS).

A method and system for non-invasive assessment and therapy planning for coronary artery disease from medical image data of a patient is disclosed. Geometric features representing at least a portion of a coronary artery tree of the patient are extracted from medical image data. Lesions are detected in coronary artery tree of the patient and a hemodynamic quantity of interest is computed at a plurality of points along the coronary artery tree including multiple points within the lesions based on the extracted geometric features using a machine learning model, resulting in an estimated pullback curve for the hemodynamic quantity of interest. Post-treatment values for the hemodynamic quantity of interest are predicted at the plurality of points along the coronary artery tree including the multiple points within the lesions for each of one or more candidate treatment options for the patient, resulting in a respective predicted post-treatment pullback curve for the hemodynamic quantity of interest for each of the one or more candidate treatment options. A visualization of a treatment prediction for at least one of the candidate treatment options is displayed.

An image-capturing device includes: an illumination light source configured to emit illumination light to illuminate an object; a laser light source configured to emit laser light with a peak wavelength in a range of wavelengths absorbed or reflected by at least one region of the object; an imaging device configured to take an image of the object; a speckle variable device configured to change a speckle pattern in an image acquired by the imaging device over time; and an image processing device configured to process the image acquired by the imaging device, which includes: measuring a change over time in a intensity signal from each pixel constituting the image, and dividing an imaged region of the object into a plurality of portions based on a waveform of the change in the intensity signal over time.

The disclosure discloses a method, a device, a system and a computer storage medium for obtaining the CT perfusion imaging parameter maps of brain. The method includes: obtaining CT perfusion images, pre-processing the CT perfusion images, and obtaining discrete contrast agent concentration curves C(n) of each pixel point in the brain tissue; reading the acquisition time information of the CT perfusion images to obtain the acquisition time arrays T(n); intercepting the acquisition time arrays T(n) to obtain the relative acquisition time arrays t(n); combining the discrete contrast agent concentration curves C(n) with the corresponding relative acquisition time arrays t(n) to obtain the discrete time-concentration curves C(t.sub.n) of each pixel point in the brain tissue; after fitting or interpolating the discrete time-concentration curves C(t.sub.n), re-discretizing at the same time interval, and obtaining the discrete time-concentration curves C(n)′ of each pixel point in brain tissue.

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.

Embodiments include a system for determining cardiovascular information for a patient. The system may include at least one computer system configured to receive patient-specific data regarding a geometry of the patient's heart, and create a three-dimensional model representing at least a portion of the patient's heart based on the patient-specific data. The at least one computer system may be further configured to create a physics-based model relating to a blood flow characteristic of the patient's heart and determine a fractional flow reserve within the patient's heart based on the three-dimensional model and the physics-based model.

Methods and systems are provided for extracting cardiac frequency angiographic phenomena for an unconstrained vascular object from an angiographic study. In one example, a computer may obtain a series of angiographic image frames obtained at a rate faster than cardiac frequency. Each image frame may comprise a plurality of pixels, and each pixel may have a corresponding intensity. The computer may apply an optical flow technique to the angiographic image frames to generate a plurality of paths corresponding to a displacement of respective pixels from image frame to image frame. The computer may further generate a spatiotemporal reconstruction of cardiac frequency angiographic phenomena based on the plurality of paths and the corresponding intensities associated with respective pixels of the paths, and output for display the spatiotemporal reconstruction of cardiac frequency angiographic phenomena in one or more images.

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.