Akademik Veri Yönetim Sistemi
Biomedical Physics and Engineering Express, cilt.12, sa.3, 2026 (ESCI, Scopus)
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.