# TRD3: Intelligent Physics-Driven Technologies for Inverse Problems in UHF Applications

> **NIH EB P41** · UNIVERSITY OF MINNESOTA · 2026 · $216,417

## Abstract

Project Summary/Abstract
Image reconstruction has been an integral part of MRI technology development over the past decades,
spanning techniques such as parallel imaging, compressed sensing, low-rank matrix models, methods for
novel encoding strategies/k-space trajectories, and more recently deep learning (DL) approaches. Our TRD in
the first phase has been at the forefront of these developments, introducing multiple new technologies for
improved DL reconstruction and training, interpretable image denoising, and fast iterative algorithms, which
were applied to Cartesian, non-Cartesian and novel encoding strategies beyond Fourier encoding. These new
MRI reconstruction methods have also pushed the imaging technologies in other TRDs, CPs and SPs forward
to target higher resolutions and acceleration rates at lower signal-to-noise ratios (SNR), as well as to combine
information across multiple nuclei or even modalities. Consequently, these targets necessitate newer
technologies for image reconstruction and denoising, an interplay with image acquisition, and new approaches
for multi-nuclei and multi-modal computational imaging. Each of these directions correspond to a specific
inverse problem with its own distinct forward operator dictated by the underlying imaging physics.
Our goal in this TRD is to link these inverse problems through the lens of intelligent physics-driven
technologies that synergistically utilize imaging physics and advances in DL methods. For accelerated high-
resolution imaging, which remains a focal point for our TRD, we will develop a new computational imaging
pipeline that explicitly combines interpretable denoising methods and physics-driven DL reconstruction. These
will be complemented by new technologies for improved denoising of MR image series, and for self-supervised
training of physics-driven DL reconstruction from few examples without ground-truth data. On the acquisition
side, we will concentrate on parallel transmit technology for UHF ima

## Key facts

- **NIH application ID:** 11257715
- **Project number:** 5P41EB027061-08
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Mehmet  Akcakaya
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** EB
- **Fiscal year:** 2026
- **Award amount:** $216,417
- **Award type:** 5
- **Project period:** 2019-02-01T00:00:00 → 2029-01-31T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11257715

## Citation

> US National Institutes of Health, RePORTER application 11257715, TRD3: Intelligent Physics-Driven Technologies for Inverse Problems in UHF Applications (5P41EB027061-08). Retrieved via AI Analytics 2026-07-21 from https://api.ai-analytics.org/grant/nih/11257715. Licensed CC0.

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