Magnetic resonance imaging (MRI) is the gold standard imaging modality for numerous diagnostic tasks, yet its usefulness is tempered due to its high cost and infrastructural requirements. Low-cost very-low-field portable scanners offer new opportunities, while enabling imaging outside conventional MRI suites. However, achieving diagnostic-quality images in clinically acceptable scan times remains challenging with these systems. Therefore methods for improving the image quality while reducing the scan duration are highly desirable. Here, we investigate a physics-informed 3D deep unrolled network for the reconstruction of portable MR acquisitions. Our approach includes a novel network architecture that utilizes momentum-based acceleration and leverages complex conjugate symmetry of k-space for improved reconstruction performance. Comprehensive evaluations on emulated datasets as well as 47mT portable MRI acquisitions demonstrate the improved reconstruction quality of the proposed method compared to existing methods.
@article{arxiv.2509.11790,
title = {Physics-Informed Deep Unrolled Network for Portable MR Image Reconstruction},
author = {Efe Ilıcak and Chinmay Rao and Chloé Najac and Beatrice Lena and Baris Imre and Fernando Galve and Joseba Alonso and Andrew Webb and Marius Staring},
journal= {arXiv preprint arXiv:2509.11790},
year = {2025}
}