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This software invention pertains to Joint Richardson-Lucy (RL) deconvolution methods used to combine multiple images of an object into a single image for improving resolution in modern fluorescence microscopy. RL deconvolution merges images with very different point spread functions, such as in multi-view light-sheet microscopes, while preserving the best resolution information present in each image.

The invention pertains to a system for reconstructing images acquired from MR and CT scanners in a robust Gadgetron based cloud computing system. A hardware interface connects clinical imaging instruments (e.g., MR or CT scanners) with a cloud computing environment that includes image data reconstruction and processing software not limited by the computational constraints typical of static hardware with finite processor power.

Software is has been developed and available for licensing that fully automates image processing for the quantification of myocardial blood flow (MBF) pixel maps from firstpass contrast-enhanced cardiac magnetic resonance (CMR) perfusion images. The system removes the need for laborious manual quantitative CMR perfusion pixel map processing and can process prospective and retrospective studies acquired from various imaging protocols. In full automation, arterial input function (AIF) images are processed for motion correction and myocardial perfusion images are corrected for intensity bias.

One of four deaths in the United States is due to cancer despite an emphasis on prevention, early detection, and treatment that has lowered cancer death rates by 20% in the past two decades. Further improvements in survival rates are likely to come from improving the limits of detection sensitivity at earlier stages of cancer. New approaches that rely heavily on genomic information, however, may change future testing strategies.

Scientists at NCATS have developed a novel Cellular Thermal Shift Assay (CETSA), named “Real-time CETSA” in which temperature-induced aggregation of proteins can be monitored in cells in real time across a range of compound concentrations and simultaneously across a temperature gradient in a high-throughput manner. Real-time CETSA streamlines the thermal shift assay and allows investigators to capture full aggregation profiles for every sample.

This technology includes a novel computational algorithm and software implementation to map and display biological pathways and their relationship on the surface of a globe in a three-dimensional space. Currently, biological pathways and genes are represented as two-dimensional networks, which is not effective for displaying complicated relationships between pathways and genes.

This technology relates to a software package called NIMH DAO Toolbox that uses multithreading and a unique buffer structure to shorten gaps in sample readouts. Data acquisition devices running in continuous sampling mode collect data samples at a given sampling rate. The samples are typically stored in a memory buffer and read out at a regular interval. If the sampling rate is short enough, there can be a gap between the time the first sample is acquired and the time that sample is available to the user. This gap is typically on the order of tens of milliseconds.

This technology relates to a closed-loop controller that is being developed as a phone app for emotional self-regulation in real-time. There is a significant association between emotion dysregulation and symptoms of depression, anxiety, eating pathology, and substance abuse, affecting millions worldwide. Consisting of a closed-loop controller that adjusts reward values in real-time according to individual mood response, the Mood Machine Interface technology compensates for adaptation to stimuli over time allowing it to generate substantial mood changes in the user.

This invention relates to methods and algorithms that incorporate information from multiple imaging modalities to identify, estimate the size, and track the time course of brain lesions. Subjects develop brain lesions over the natural course of a disease. Currently, lesions are measured and tracked by a trained neuroradiologist using slice-by-slice inspection, a slow process that is prone to human error and hard to generalize to large observational studies.

This technology includes an automated and non-invasive assessment system called Automated Identification of subjects at risks of Multiple Sclerosis (AIMS) to assist clinicians in their diagnostic evaluation. A focus of AIMS is the ‘central vein sign’ (and other radiological findings).