University of California

Fabien Scalzo

Corey W Arnold

King Chung Ho

David S Liebeskind

Systems and methods for analyzing perfusion-weighted medical imaging using deep neural networks are provided. In some aspects, a method includes receiving perfusion-weighted imaging data acquired from a subject using a magnetic resonance ('MR') imaging system and modeling at least one voxel associated with the perfusion-weighted imaging data using a four-dimensional ('4D') convolutional neural network. The method also includes extracting spatio-temporal features for each modeled voxel and estimating at least one perfusion parameter for each modeled voxel based on the extracted spatio-temporal features. The method further includes generating a report using the at least one perfusion parameter indicating perfusion in the subject.

Systems and methods for analyzing perfusion-weighted medical imaging using deep neural networks

An apparatus and methodological framework are provided, named perfusion angiography, for the quantitative analysis and visualization of blood flow parameters from DSA images. The parameters, including cerebral blood flow (CBF) and cerebral blood volume (CBV), mean transit time (MTT), time-to-peak (TTP), and T, are computed using a bolus tracking method based on the deconvolution of time-density curves on a pixel-by-pixel basis. Individual contrast concentration curves of overlapping vessels can be delineated with multivariate Gamma fitting. The extracted parameters are each transformed into parametric maps of the target that can be color coded with different colors to represent parameter values within a particular set range. Side by side parametric maps with corresponding DSA images allow expert evaluation and condition diagnosis.

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Oakland, CA, United States of America

Fabien Scalzo