Benchmarking 3D multi-coil NC-PDNet MRI reconstruction
Abstract
Motivation: Deep Neural Networks (DNNs) show promise in reconstructing undersampled MRI data but face challenges with non-Cartesian 3D multi-coil acquisitions due to high memory demands and training complexity. Goal(s): To address the research gap in validating DNNs performances for non-Cartesian data in 3D multi-coil settings and assess undersampling patterns’ impact on reconstruction quality. Approach: We extend the Non-Cartesian Primal-Dual Network (NC-PDNet) for multi-coil 3D reconstruction, evaluating different training configurations and benchmarking four non-Cartesian undersampling patterns. Results: NC-PDNet with GoLF-SPARKLING trajectory achieved gains of +2.43/+0.01 in PSNR/SSIM for 32-channel data, reconstructing a 1mm isotropic volume in 4.95 seconds using 5.49 GB GPU memory. Impact: Achieving fast, high-quality reconstructions with reasonable GPU memory usage demonstrates our approach’s viability for clinical research. Benchmarking the GoLF-SPARKLING trajectory against established non-Cartesian baselines in 3D multi-coil settings validates its future application in more challenging experiments, notably at higher resolutions.
Details
- Type of work: Conference paper
- Venue: 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)
- Published: 16 September 2025
- Pages: 1—5
- DOI: 10.1109/isbi60581.2025.10980654
- Authors: Asma Tanabene, Chaithya Giliyar Radhakrishna, Aurélien Massire, Mariappan Nadar, and Philippe Ciuciu
- Cited by: 2 times (OpenAlex)
- Data sources: ref.bib, OpenAlex
Links
BibTeX
@inproceedings{tanabene2025benchmarking,
title={Benchmarking 3D multi-coil NC-PDNet MRI reconstruction},
author={Tanabene, Asma and Chaithya, GR and Massire, Aur{\'e}lien and Nadar, Mariappan and Ciuciu, Philippe},
booktitle={2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)},
pages={1--5},
year={2025},
organization={IEEE}
}