Project Grant R01EB036013

Award Date 4/8/25
Completion Date 2/28/29
Dollars Obligated $459K
Federal Grant Program
93.286
Assistance Type
Project Grant
Place of Performance
Maryland, USA
Similar Awards
The federal Project Grant award of $306,203 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) supports the development of an advanced multiband shimming system for improved neuroimaging with magnetic resonance imaging (MRI) scanners. The project aims to design and prototype a digitally-controlled, real-time shim coil insert that can provide distinct B0...
This $1,434,445.00 Project Grant award from the National Institutes of Health's Trans-NIH Research Support program (CFDA 93.310) supports the University of Southern California (USC) in developing new deep learning-based architectures, algorithms and training mechanisms to address key challenges in magnetic resonance imaging (MRI) reconstruction. The project aims to create a robust, reliable and trustworthy toolkit for reducing MRI acquisition time, enabling high-quality reconstruction with...
This federal Project Grant award, valued at $111,119.00 and provided by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), aims to develop an innovative imaging method to accelerate advanced diffusion magnetic resonance imaging (DMRI) while maintaining image quality and microstructure sensitivity. The key research objectives are to: 1) Optimize a method to...
The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded a $412,801 Project Grant under the CFDA 93.286 "Discovery and Applied Research for Technological Innovations to Improve Human Health" program to Brigham & Women's Hospital Inc. The project aims to develop a time-abbreviated, comprehensive brain MRI exam protocol that can capture a variety of quantitative and qualitative MRI contrasts within an 8-minute timeframe. This involves using a new 3D...
This $588,880 Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the "Discovery and Applied Research for Technological Innovations to Improve Human Health" (CFDA 93.286) program aims to develop advanced methods for reconstructing coherent 3D fetal brain volumes from 2D MRI scans. The project will leverage deep learning strategies to rapidly and accurately reconstruct 3D fetal brain volumes and automatically generate key brain...
This federal Project Grant award of $499,291 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the "Discovery and Applied Research for Technological Innovations to Improve Human Health" program (CFDA 93.286) will fund the development of breakthrough technology to enable future compact, mid-field (0.7 Tesla) magnetic resonance imaging (MRI) systems. The project aims to advance MRI technology to improve accessibility and affordability of high-quality MRI,...
The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded The General Hospital Corporation, doing business as Massachusetts General Hospital (MGH), a $185,625 Project Grant under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286). The funding supports the development of a novel "Multiphoton Parallel Transmit for MRI" technology to improve magnetic resonance imaging (MRI) quality and resolution at 7...
This $417,150 Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health (CFDA 93.286) program, aims to develop and validate a novel motion correction technique for 2D T2-weighted structural MRI of the brain. The key objectives are to: 1) Develop a fast MR-based "spin history" navigator to measure through-plane translational and rotational velocities,...
This $536,954 federal Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the "Discovery and Applied Research for Technological Innovations to Improve Human Health" program (CFDA 93.286) supports the development of an ultra-high spatial resolution photon-counting CT system with multiple focal spots. The key goals are to: Develop system models and reconstruction algorithms to enable data acquisition and processing with multiple focal...
The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded a $390,616 Project Grant under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) to the Regents of the University of Michigan for the development of layer-specific functional magnetic resonance imaging (fMRI) techniques for clinical 3 Tesla scanners. The goal is to improve signal-to-noise ratio and reduce scan time for layer-specific fMRI, which currently...

This federal Project Grant award of $459,264.00 from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) supports the development and evaluation of the Resolution Enhancement and Contrast Harmonization (REACH) algorithm. The REACH algorithm aims to super-resolve multi-slice 2D Magnetic Resonance Imaging (MRI) and adjust the contrast of both multi-slice 2D and 3D MRI for use in downstream processing and clinical diagnosis. Key objectives include: 1) developing an interpretable deep learning algorithm for harmonization, restoration, and imputation; 2) developing a fast super-resolution method incorporating high-resolution reference images; and 3) evaluating the REACH algorithm's performance, including through a radiologist observer study. This research project, with a period of performance from April 2025 to February 2029, seeks to address challenges in standardizing the appearance of MRI neuroimages and enable the use of modern AI algorithms on clinical-quality MRI data.

Generated 4/29/25, 4:26 AM