# TRD3 - Image Reconstruction

> **NIH NIH P41** · UNIVERSITY OF MINNESOTA · 2020 · $221,908

## Abstract

Project Summary/Abstract
Image reconstruction from raw measurements is an inverse problem of fundamental importance in MRI. The
basic formulation for such reconstructions involve a k-space sampled uniformly on a Cartesian grid at greater
than the Nyquist rate, which is Fourier transformed to generate the desired image. However, this acquisition-
reconstruction strategy is often difficult to perform in practical research and clinical settings, as it leads to long
scan times, necessitating trade-offs in spatial and temporal resolutions. This observation has led to the
development of multiple reconstruction strategies over the last few decades, including partial Fourier imaging,
parallel imaging, non-Cartesian acquisitions and compressed sensing, where the reconstruction goes beyond a
simple Fourier transform, and often involves careful mathematical modeling of the MR system and images. The
aforementioned developments aim to address a continuous need for faster imaging, improved resolutions and
robustness, both in clinical and research settings. However, as the existing methods reach the limits of
resolution and acceleration achievable in the presence of system and physiological limitations, new
reconstruction strategies are needed to improve image quality for various acquisition strategies.
In this TRD, we seek to develop new image reconstruction techniques for enabling fast high-resolution
acquisitions, improving noise resilience, allowing for different encoding strategies, while increasing robustness
to underlying physiological and system variations. Our developments for fast high-resolution imaging include
improved strategies for k-space interpolation reconstruction in Cartesian imaging, as well as new self-
calibrated techniques for three-dimensional non-Cartesian imaging. For the former, we extend the liner shift-
invariant convolutional interpolation approaches for reconstructing multi-coil data in two ways: i) Scan-specific
deep learning without training databases for non-linear estimation of missing k-space data, in simultaneous
multi-slice, parallel and partial Fourier imaging, ii) Region-specific shift-variant linear kernels for highly-
accelerated volumetric parallel imaging. For non-Cartesian acquisitions, our self-calibration is used to estimate
radius- and rotation-specific interpolation kernels, without additional ACS data. We also tackle the problem of
improving non-Fourier encoded acquisitions, such as spatiotemporal encoding, and devise fast matrix
sparsifying approaches to enable regularized reconstructions without high computational burden. To further
improve reconstruction fidelity in multi-dimensional acquisitions, we propose the local use of high-order tensor
models, along with an information theoretic approach for parameter-free regularization. Finally, we consider
imaging in the presence of physiological and system variations, such as motion and B0 inhomogeneities, which
are especially pronounced at ultrahigh field strength...

## Key facts

- **NIH application ID:** 9850583
- **Project number:** 5P41EB027061-02
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Mehmet Akcakaya
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $221,908
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9850583

## Citation

> US National Institutes of Health, RePORTER application 9850583, TRD3 - Image Reconstruction (5P41EB027061-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9850583. Licensed CC0.

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