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This technology includes a fully automated computer aided diagnosis system to quantify myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) pixel maps from the first-pass contrast-enhanced cardiac magnetic resonance (CMR) perfusion images. This system performs automated image registration, motion compensation, segmentation, and modeling to extract quantitative features from different myocardial regions of interest.

The National Cancer Institute (NCI) seeks parties to license software for modeling the targeted delivery of anti-cancer agents in solid tumors.

The software models the permeability and concentration of intravenously administered antibody anti-cancer agent conjugates in solid tumors.  The models can be used to determine optimal dosing regimen of a therapeutic in a particular cancer type.  Thirty factors that affect delivery rates and efficiencies are analyzed as variables in generating the models.

Summary:

The National Eye Institute (NEI) seeks research co-development partners and/or licensees for an automatic deep learning-based algorithm to detect and quantitate ellipsoid zone (EZ) loss in Spectral Domain Optical Coherence Tomography (SD-OCT) images.

This technology includes a fully automatic 3D image processing system to segment the heart as well as other organs from contrast-enhanced cardiac computed tomography (CCT) images. Our method detects four cardiac chambers including left ventricle, right ventricle, left atrium, right atrium, as well as the ascending aorta and left ventricular myocardium. It also classifies noncardiac tissue structures in the CCT images such as lung, chest wall, spine, descending aorta, and liver.

This technology includes a novel method to train deep learning convolution neural network model to improve the signal-noise-ratio for the magnetic resonance (MR) imaging. The novelty lies on the fact that actual MR imaging physics information is used in the deep learning training. The resulting model achieves significant signal-to-noise ratio (SNR) improved for different acceleration factors in MR imaging. The resulting model can be used for many body anatomies (e.g., brain, heart, liver, spine, etc.) to significantly improve the SNR.