Learning-Based Reconstruction of Under-Sampled MRI Data Using End-to-End Deep Learning in Comparison to CS

Adnan Khalid, Husnain Shahid, Hatem Rashwan, Domenec Puig

2025

Abstract

Magnetic Resonance Imaging (MRI) reconstruction, particularly restoration and denoising, remains challenging due to its ill-posed nature and high computational demands. In response to this, Compressed Sensing (CS) has recently gained prominence for enabling image reconstruction from limited measurements and consequently reducing computational costs. However, CS often struggles to maintain diagnostic image quality and strictly relies on sparsity and incoherence conditions that are somewhat challenging to meet with experimental data or particularly real-world medical data. To address these limitations, this paper proposes a novel framework that integrates CS with a convolutional neural network (CNN), effectively relaxing the CS constraints and enhancing the diagnostic quality of MRI reconstructions. In essence, this method applies CS to generate a measurement vector during initial step and then refined the output by CNN to improve image quality. Extensive evaluations on the MRI knee dataset demonstrate the efficacy of this dual step approach, achieving significant quality improvements with measurements (SSIM = 0.876, PSNR = 27.56 dB). A deep comparative analysis also perform to identify the superior performance over multiple existing CNN architectures

Paper Citation

in Harvard Style

Khalid A., Shahid H., Rashwan H. and Puig D. (2025). Learning-Based Reconstruction of Under-Sampled MRI Data Using End-to-End Deep Learning in Comparison to CS. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 373-380. DOI: 10.5220/0013141400003912

in Bibtex Style

@conference{visapp25,
author={Adnan Khalid and Husnain Shahid and Hatem Rashwan and Domenec Puig},
title={Learning-Based Reconstruction of Under-Sampled MRI Data Using End-to-End Deep Learning in Comparison to CS},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={373-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013141400003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Learning-Based Reconstruction of Under-Sampled MRI Data Using End-to-End Deep Learning in Comparison to CS
SN - 978-989-758-728-3
AU - Khalid A.
AU - Shahid H.
AU - Rashwan H.
AU - Puig D.
PY - 2025
SP - 373
EP - 380
DO - 10.5220/0013141400003912
PB - SciTePress