Systems and methods are provided for automatic detection and quantification for traumatic bleeding. Image data is acquired using a full body dual energy CT scanner. A machine-learned network detects one or more bleeding areas on a bleeding map from the dual energy CT scan image data. A visualization is generated from the bleeding map. The predicted bleeding areas are quantified, and a risk value is generated. The visualization and risk value are presented to an operator.

A method for determining respiratory induced blood mass change from a four-dimensional computed tomography (4D CT) includes receiving a 4D CT image set which contains a first three-dimensional computed tomographic image (3D CT) and a second 3D CT image. The method includes executing a deformable image registration (DIR) function on the received 4D CT image set, and determining a displacement vector field indicative of the lung motion induced by patient respiration. The method further includes segmenting the received 3D CT images into a first segmented image and a second segmented. The method includes determining the change in blood mass between the first 3D CT image and the second 3D CT image from the DIR solution, the segmented images, and measured CT densities.

Systems and methods of predicting future medical events are based on the processing of medical image. The prediction of premature birth and estimation of gestational age based on ultrasound images are presented as illustrative examples. The new abilities to estimate the probability of future medical events, before they otherwise could be predicted, provides new avenues for the development of preventative treatments.

Apparatus and methods are described for use with an endoluminal device that includes one or more radiopaque portions and that moves through a lumen of a subject. A sequence of radiographic images of a portion of the subject's body, in which the lumen is disposed, is acquired, during movement of the endoluminal device through the lumen. Locations at which the one or more radiopaque portions of the endoluminal device were imaged during the movement of the endoluminal device through the lumen are identified, by analyzing the sequence of radiographic images. A set of locations at which the one or more radiopaque portions were disposed during the movement of the endoluminal device through the lumen is defined, and an endoluminal path of the device through the lumen is estimated based upon the set of locations. Other applications are also described.

A system and method for assessing blood flow include: an ocular lens; a light source; a digital video camera; a biosensor; a trigger; and a computer. The ocular lens is for viewing a fundus of an eye. The light source is for illuminating the fundus. The digital video camera is for imaging the fundus. The biosensor is for sensing a pulse waveform. The computer is configured for: recording input frames and pulse waveform data in response to an input from the trigger; defining a low-pass frequency and a high-pass frequency from the pulse waveform data; stabilizing the input frames; enhancing contrast of the input frames; separating the input frames into sub-channels; conducting eulerian video magnification for color amplification using the inputs of image sampling rate, the low-pass frequency, the high-pass frequency, and an amplification factor; reconstructing the sub-channels into output frames; and combining the output frames with the input frames.

Methods and systems are provided for detecting coronary lesions in 3D cardiac computed tomography and angiography (CCTA) images using deep neural networks. In an exemplary embodiment, a method for detecting coronary lesions in 3D CCTA images comprises, acquiring a 3D CCTA image of a coronary tree, mapping the 3D CCTA image to a multi-label segmentation map with a trained deep neural network, generating a plurality of 1D parametric curves for a branch of the coronary tree using the multi-label segmentation map, determining a location of a lesion in the branch of the coronary tree using the plurality of 1D parametric curves, and determining a severity score for the lesion based on the plurality of 1D parametric curves.

A system and method for generating a parametric map of a subject's brain includes receiving non-contrast computed tomography (NCCT) imaging data and receiving computed tomography perfusion (CTP) data. The method further includes creating a baseline image by utilizing the NCCT data and generating a parametric map using the CTP data and the baseline image.

A system and method for providing clutter suppression in vessels depicted in B-mode ultrasound images is provided. The method includes acquiring series of B-mode frames and periodically acquiring a flow image frame between each series of B-mode frames. The method includes segmenting the flow image frame and a subsequent B-mode frame in a series of B-mode frames acquired immediately after the flow image frame to extract a vessel lumen and analyzing a spatial correlation between the vessel lumen region in the flow image frame and the subsequent B-mode frame. The method includes applying clutter filtering to pixels in the vessel lumen region of the subsequent B-mode frame based on flow characteristics of corresponding pixels in the flow image frame when the spatial correlation between the vessel lumen region in the flow image frame and the subsequent B-mode frame exceeds a threshold. The method includes presenting the clutter suppressed B-mode frame.

Systems and methods for analyzing pathologies utilizing quantitative imaging are presented herein. Advantageously, the systems and methods of the present disclosure utilize a hierarchical analytics framework that identifies and quantify biological properties/analytes from imaging data and then identifies and characterizes one or more pathologies based on the quantified biological properties/analytes. This hierarchical approach of using imaging to examine underlying biology as an intermediary to assessing pathology provides many analytic and processing advantages over systems and methods that are configured to directly determine and characterize pathology from underlying imaging data.

A computer-implemented method for personalized assessment of patients with acute coronary syndrome (ACS) includes extracting (i) patient-specific coronary geometry data from one or more medical images of a patient; (ii) a plurality of features of a patient-specific coronary arterial tree based on the patient-specific coronary geometry data; and (iii) a plurality of ACS-related features from additional patient measurement data. A surrogate model is used to predict patient-specific hemodynamic measures of interest related to ACS based on the plurality of features of the patient-specific coronary arterial tree and the plurality of ACS-related features from the additional patient measurement data.