Unrolled Neural Networks

Latest papers

9 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Prior-Guided Physics-Informed Deep Learning for Fast 4D MRI Reconstruction
Nov 1, 2026Mojtaba Safari, Parag J. Parikh, Shansong Wang +64D Reconstruction Accelerated MRI Reconstruction
Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL, USA · Department of Radiation Oncology, Henry Ford Health, Detroit, Michigan, USA
Scalable Unrolled Chains for MRI Reconstruction
Oct 24, 2026Emiljo Mëhillaj, Ender KonukogluUnrolled Neural Networks Accelerated MRI Reconstruction
Computer Vision Lab, ETH Zurich, Zurich, Switzerland · LOOP Zurich - Medical Research Center, Zurich, Switzerland
OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRI
Sep 26, 2026Zhengyi Lu, Ming Lu, Chongyu Qu +11Unrolled Neural Networks Magnetic Resonance Imaging
Vanderbilt University, Nashville, TN, USA · Vanderbilt University Medical Center, Nashville, TN, USA · Oak Ridge National Laboratory, Oak Ridge, TN, USA
FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction
Aug 26, 2026Tristan Gottwald, Michelle Bruch, Mubashir-Ul Hassan +6Unrolled Neural Networks Accelerated MRI Reconstruction
Institute of Information Processing (tnt), Leibniz University Hannover, Germany · L3S, Leibniz University Hannover, Hannover, Germany · Department of Data Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany +2
Motion-Robust Deep Reconstruction for Free-Breathing Cardiac Cine MRI
May 20, 2026Mahmut Yurt, Kanghyun Ryu, Zhitao Li +8Unrolled Neural Networks Accelerated MRI Reconstruction
Stanford University · KIST · GE Healthcare
Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?
Apr 24, 2026Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier +2Unrolled Neural Networks Domain Generalization
1Dublin City University, Ireland · University College Dublin, Ireland
Tunable Domain Adaptation Using Unfolding
Mar 27, 2026Snehaa Reddy, Jayaprakash Katual, Satish MulletiUnrolled Neural Networks Domain Adaptation
The authors are with the Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, India.
UEPS: Robust and Efficient MRI Reconstruction
Mar 19, 2026Xiang Zhou, Hong Shang, Zijian Zhan +3Unrolled Neural Networks Accelerated MRI Reconstruction
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China · School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, China · University of Chinese Academy of Sciences, China +2
Unrolled Reconstruction with Integrated Super-Resolution for Accelerated 3D LGE MRI
Mar 18, 2026Md Hasibul Husain Hisham, Shireen Elhabian, Ganesh Adluru +5Unrolled Neural Networks Accelerated MRI Reconstruction
Kahlert School of Computing, University of Utah, Salt Lake City, UT, USA · Radiology & Imaging Sciences, University of Utah, Salt Lake City, UT, USA · Cardiology, University of Utah, Salt Lake City, UT, USA

Nov 1, 2026J/K move · Enter open · S save

Prior-Guided Physics-Informed Deep Learning for Fast 4D MRI Reconstruction

Mojtaba Safari, Parag J. Parikh, Shansong Wang, Mingzhe Hu+5

Department of Radiation and Cellular Oncology, The University of Chicago, Chicago, IL, USA · Department of Radiation Oncology, Henry Ford Health, Detroit, Michigan, USA

4D Reconstruction Accelerated MRI Reconstruction Physics-Informed ML Unrolled Neural Networks

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Background and purpose: We propose PGPI4MR (prior-guided, physics-informed 4D MRI reconstruction), a deep learning method that leverages information from other respiratory phases or a setup scan, the first 30 s of the same scan, to improve 4D MRI reconstruction without extra scan time. Materials and methods: PGPI4MR is an unrolled network that reconstructs 16 respiratory phases from 60, 30, or 15 s of data, and single 3D volumes, each from 0.29-0.49 s of data, using a setup-scan image. The networks were trained once, on other volunteers, and were not retrained for new volunteers. We evaluated PGPI4MR on measured k-space from three datasets: free-breathing MR-linac (1.5T) and abdominal (3T) scans, and thigh scans with cuff compression (1.5T). All methods, including comparative models, received identical samples and were scored similarly. Results: In vivo, PGPI4MR had the lowest held-out error for 4D MRI at every scan time, and its 16 phases were ready a median of 7.5-13 s after the scan on the 2 GPUs tested. Coupling the 16 respiratory phases reduced the error at 60 s by 9.1% compared with reconstructing each phase alone. For sub-second MRI, the error at 0.49 s was 15.5% lower than that of the best comparison method, classical reconstruction toward the same prior, and on the faster GPU, every 0.29 s window was acquired and reconstructed within 0.5 s. Without retraining, PGPI4MR matched the best methods on the 3T abdominal scans but not on the thigh scans; after retraining on thigh data, it reduced the held-out error by 6.6% relative to the best comparison method. Conclusion: Prior information allowed PGPI4MR to reconstruct 4D and sub-second MRI more accurately than compressed sensing and a published 4D network, within seconds and without extra scan time. This could support 4D MRI for treatment planning and 3D motion monitoring during MR-guided radiotherapy.