Academic Journal
Deep learning-enhanced X-space reconstruction for magnetic particle imaging: a physics-consistent approach.
| Τίτλος: | Deep learning-enhanced X-space reconstruction for magnetic particle imaging: a physics-consistent approach. |
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| Συγγραφείς: | Olamat A; Department of Bioengineering, Yildiz Technical University, Istanbul, Türkey.; Department of Medical Engineering, Al-Ahliyya Amman University, Amman, Jordan., Bingolbali A; Department of Bioengineering, Yildiz Technical University, Istanbul, Türkey. |
| Πηγή: | Biomedical physics & engineering express [Biomed Phys Eng Express] 2026 Jun 08; Vol. 12 (3). Date of Electronic Publication: 2026 Jun 08. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Bristol : IOP Publishing Ltd., [2015]- |
| Ιατρικοί όροι (MeSH): | Magnetic Particle Imaging*/methods , Image Processing, Computer-Assisted*/methods , Deep Learning*, Signal-To-Noise Ratio ; Phantoms, Imaging ; Algorithms ; Physics ; Humans |
| Περίληψη: | Magnetic particle imaging (MPI) is an emerging tracer-based imaging modality with high sensitivity and excellent temporal resolution; however, X-space reconstruction suffers from inherent point spread function (PSF) blur that limits spatial resolution. This study aims to improve X-space MPI reconstruction quality while preserving physical consistency. To address this problem, we propose X-Space-PC-Restore, a physics-consistent deep learning framework that combines a hybrid U-Net encoder-decoder architecture with Transformer-based attention and PSF-guided loss functions. The method was evaluated on a synthetic dataset of 1600 samples spanning 4 phantom types (line, circle, ellipse, cross), Lissajous trajectories, and signal-to-noise ratio (SNR) levels from 5 to 15 dB. The proposed model achieved a peak SNR (PSNR) of 15.48 dB and an normalized root mean square error (NRMSE) of 0.175, representing a 23.0% improvement in PSNR and 45.8% reduction in NRMSE compared to the best classical baseline (Richardson-Lucy: PSNR 12.58 dB, NRMSE 0.244). The method demonstrated consistent superiority across all tested SNR levels (5-40 dB), with PSNR gains ranging from 1.4 to 3.0 dB over Richardson-Lucy. Resolution analysis showed that Richardson-Lucy achieved the best full width at half maximum (10.47 ± 0.93 pixels), while the proposed method achieved near-ground-truth resolution. SNR robustness analysis confirmed stable performance of the proposed method across noise conditions, whereas classical methods exhibited greater sensitivity to noise. These results demonstrate that physics-informed deep learning is a promising strategy for reducing blur, improving fidelity, and enhancing the reliability of MPI image reconstruction. (Creative Commons Attribution license.) |
| Contributed Indexing: | Keywords: X-space reconstruction; deep learning; image deconvolution; magnetic particle imaging; physics-informed neural networks; transformer networks |
| Entry Date(s): | Date Created: 20260527 Date Completed: 20260611 Latest Revision: 20260615 |
| Update Code: | 20260615 |
| DOI: | 10.1088/2057-1976/ae737d |
| PMID: | 42202833 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2057-1976 |
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| DOI: | 10.1088/2057-1976/ae737d |