# Reliable AI for Medical Image Reconstruction > **NIH NIH DP2** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2023 · $1,434,445 ## Abstract Project Abstract/Summary Deep neural networks have enjoyed wide empirical success in a variety of disciplines ranging from computer vision to natural language processing. However, in medical imaging such as Magnetic Resonance Imaging (MRI), a variety of challenges including lack of high-quality training data, lack of robustness to corruption/outliers and distribution shifts between train and test time as well as documented lack of reliability and trustworthiness impede the wide use and adaptation of AI. This project develops new deep learning-based architectures, algorithms and training mechanisms that that deals with these challenges creating a new toolkit for MRI reconstruction that is robust, reliable, trustworthy yet can be trained collaboratively and privately across multiple hospital systems. Using this new toolkit the project addresses three major MRI reconstruction challenges (1) reducing acquisition time via higher acceleration factors, (2) enabling high quality reconstruction even with low-intensity magnetic elds, and (3) dealing with motion artifacts. In collaboration with Musculoskeletal (MSK) sections of a few major universities, this project also involves gathering, curating and releasing new datasets and open source reconstruction software aimed at addressing these key challenges. This will also help attract further research from the machine learning/AI community to further improve this important medical imaging modality. Healthcare Impact: This project will signi cantly enhance MRI which is an important diagnostic tool. (1) reductions in the acquisition time will simultaneously increases both the accuracy of diagnosis and patient comfort. (2) the reduction of noise/nonlinear artifacts caused by low- eld scanners will lead to a reduction in the size and weight of the MR scanner. This may eventually allow MRI to be used at point of care or for emergency scenarios at the bedside and also open up a plethora of new use cases. (3) reductions in the motion artifacts increases the accuracy of diagnosis for a variety of new diseases and conditions enabling new diagnostic use cases for MRI. Also, (1) allows more patients to receive a scan using the same machine and (2) lowers the cost of the magnet and the space of operation of MR scanners. This can signi cantly reduce patient cost and thus increase the access to this diagnostically important medical imaging modality. Furthermore, the focus on MSK data collection can greatly facilitate accurate diagnosis of pathology such as subtle meniscal tears or chondrites in the knee, labral and rotator cu tears in the shoulder, and ligament out tears in the wrist. The particular focus on brachial plexopathy is also expected to have signi cant healthcare bene ts as the briachial plexus is an intricate anatomic structure with the critical function of providing innervation to the upper extremity, shoulder, and upper chest. The brachial plexus MRI study will enable great detail of this intricate anatomi... ## Key facts - **NIH application ID:** 10687707 - **Project number:** 1DP2LM014564-01 - **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA - **Principal Investigator:** Mahdi Soltanolkotabi - **Activity code:** DP2 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2023 - **Award amount:** $1,434,445 - **Award type:** 1 - **Project period:** 2023-09-20 → 2026-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10687707 ## Citation > US National Institutes of Health, RePORTER application 10687707, Reliable AI for Medical Image Reconstruction (1DP2LM014564-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10687707. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*