Systems and methods for automatic processing of input medical images are provided. A set of input medical images acquired at a plurality of locations on a patient is received. For each respective location of the plurality of locations, an image quality score is determined for each input medical image of the set of input medical images acquired at the respective location and one of the input medical images acquired at the respective location is selected based on the image quality scores. The selected input medical images are processed to correct for errors. One or more regions of interest are segmented from the processed selected input medical images. One or more hemodynamic measures are calculated from the processed selected input medical images based on the segmented one or more regions of interest. The calculated one or more hemodynamic measures are output.

Provided is an apparatus for generating a biometric image comprising a processor; and a memory comprising one or more sequences of instructions which, when executed by the processor, causes steps to be performed comprising: receiving a first biometric image and a second biometric image paired with the first biometric image; and generating a first reconstruction biometric image from the first biometric image so as to match the first reconstruction biometric image and the second biometric image based on a machine learning model.

A method and system for automated correction of phase error in MRI-based flow evaluation employs a computer processor programmed to execute a trained convolutional neural network (CNN) to receive and process image data comprising flow velocity data in three directions and magnitude data collected from a region of interest over a scan period from magnetic resonance imaging instrumentation. The image data is processed using the trained CNN to generate three output channels with pixelwise inferred corrections for the flow velocity data which are further smoothed using a regression algorithm. The smoothed corrections are added to the original image data to generate corrected flow data, which may be used for flow visualization and quantization.

An automated workflow receives a patient study comprising cardiac biomarker measurements and a plurality of echocardiographic images taken by an ultrasound device of a patient heart. A filter separates the plurality of echocardiogram images by 2D images and Doppler modality images based on analyzing image metadata. The 2D images are classified by view type, and the Doppler modality images are classified by view type. The cardiac chambers are segmented in the 2D images, and the Doppler modality images are segmented to generate waveform traces, producing segmented 2D images and segmented Doppler modality images. Using both the sets of images, measurements of cardiac features for both left and right sides of the heart are calculated. The cardiac biomarker measurements and the calculated measurements are compared with international cardiac guidelines to generate conclusions, and a report is output showing the measurements that fall within or outside of the guidelines.

This image processing apparatus is provided with an image acquisition unit for generating a concentration change image and a control unit for performing control for displaying a blood vessel image and a concentration change image, and the control unit is configured to perform control for accepting a selection of a target region on the blood vessel image displayed on the display unit and for displaying the concentration change image corresponding to the selected target region.

According to a first aspect, the present disclosure relates to a digital holography device (100) for full-field blood flow imaging of ocular vessels of a field of view of a layer (11) of the eye (10). The device comprises an optical source (101) configured for the generation of an illuminating beam (Eobj) and a reference beam (E.sub.LO), and a detector (135) configured to acquire a plurality of interferograms (I(x,y,t)) wherein an interferogram is defined as the signal resulting from the interference between the said reference beam (E.sub.LO) and a part of said illuminating beam (Eobj) that is backscattered from said layer (11). The device further comprises a processing unit (150) configured for processing said plurality of interferograms, (I(x,y,t)), wherein said processing comprises: the calculation (202), for each interferogram, of a hologram (H(x,y,t)), resulting in a first plurality of holograms; the selection (203), in sequential time windows, (tw), of second pluralities of holograms; the calculation (204), for each said second plurality of holograms, of a Doppler power spectrum (S(x,y,f)); the calculation (205), based on said Doppler power spectrum, of at least a first Doppler image thus generating at least a first plurality of Doppler images; the processing of each first Doppler image, wherein said processing comprises the devignetting (206) of said first Doppler image, resulting in a devignetted first Doppler image; the normalization (207) of said devignetted first Doppler image based on a spatial average of an intensity of said first Doppler image, resulting in a normalized first Doppler image; and the subtraction (208), from said normalized first Doppler image, of said spatial average of said intensity of said first Doppler image, resulting in a corrected first Doppler image.

A simplified method, an apparatus and a system for measuring coronary artery vascular evaluation parameters are provided. The measurement method comprises performing coronary angiography for a blood vessel to be measured(S100); measuring a pressure P.sub.d at a distal end of coronary artery stenosis via a pressure guide wire (S200); selecting an angiogram image of a first body position and an angiogram image of a second body position (S300); obtaining a three-dimensional coronary artery vascular model by three-dimensional modeling based on the angiogram image of the first body position and the angiogram image of the second body position (S400); obtaining a time T.sub.1 taken for a contrast agent flowing from an inlet to an outlet of a segment of blood vessel within the angiogram image of the first body position and a time T.sub.2 taken for the contrast agent flowing from an inlet to an outlet of the segment of blood vessel within the angiogram image of the second body position according to the three-dimensional coronary artery vascular model (S500); measuring coronary artery vascular evaluation parameters based on P.sub.d, T.sub.1, and T.sub.2 (S600).

Images of individuals obtained using perfusion-based imaging techniques or diffusion-based imaging techniques can be analyzed to determine regions of the brains of the individuals where the supply of blood has been disrupted. The images can be used to generate alerts indicating the disruption of blood flow to one or more regions of the brains of the individuals. The images can be used to identify vessel segments (eg M1, M2, M3, M4, . . . ) and branches (MCA, ACA, PCA) of the brains of individuals in which abnormalities may be present.

Systems and methods are disclosed for correcting for artificial deformations in anatomical modeling. One method includes obtaining an anatomic model; obtaining information indicating a presence of an artificial deformation of the anatomic model; identifying a portion of the anatomic model associated with the artificial deformation; estimating a non-deformed local area corresponding to the portion of the anatomic model; and modifying the portion of the anatomic model associated with the artificial deformation, based on the estimated non-deformed local area.

A method to visualize, display, analyze and quantify angiography, perfusion, and the change in angiography and perfusion in real time, is provided. This method captures image data sequences from indocyanine green near infra-red fluorescence imaging used in a variety of surgical procedure applications, where angiography and perfusion are critical for intraoperative decisions.