用视觉变压器+残差网络提升磁粒子成像系统矩阵分辨率
High-Resolution Magnetic Particle Imaging System Matrix Recovery Using a Vision Transformer with Residual Feature Network
- 结合注意力机制与残差卷积，同时捕捉全局结构和细节
- 2倍缩放下恢复误差降低88.2%，峰值信噪比提升44.7%
- 适合需要高精度重建的医学成像研究者
本研究提出一种混合深度学习框架VRF-Net，用于磁粒子成像（MPI）中高分辨率系统矩阵的恢复。由于降采样和线圈灵敏度差异，MPI分辨率常受限。VRF-Net通过结合基于Transformer的全局注意力与残差卷积精修，有效恢复大尺度结构与细微特征。系统矩阵采用双阶段降采样策略模拟真实条件。训练使用公开的Open MPI数据集和含可变线圈灵敏度的仿真数据集。在Open MPI数据集上，2倍缩放下nRMSE=0.403，pSNR=39.08 dB，SSIM=0.835；8倍缩放时仍保持良好表现（pSNR=31.06 dB，SSIM=0.717）。在仿真数据集上，2倍缩放下nRMSE=4.44，pSNR=28.52 dB，SSIM=0.771。平均相比插值与CNN方法，nRMSE降低88.2%，pSNR提升44.7%，SSIM提高34.3%。在Open MPI模体图像重建中，2倍缩放下重建误差降至nRMSE=1.79，pSNR=41.58 dB，SSIM=0.960，优于现有方法。结果表明，VRF-Net能实现清晰无伪影的系统矩阵恢复和多尺度鲁棒重建，为未来活体应用提供新方向。
原文摘要 · Abstract (English)
This study presents a hybrid deep learning framework, the Vision Transformer with Residual Feature Network (VRF-Net), for recovering high-resolution system matrices in Magnetic Particle Imaging (MPI). MPI resolution often suffers from downsampling and coil sensitivity variations. VRF-Net addresses these challenges by combining transformer-based global attention with residual convolutional refinement, enabling recovery of both large-scale structures and fine details. To reflect realistic MPI conditions, the system matrix is degraded using a dual-stage downsampling strategy. Training employed paired-image super-resolution on the public Open MPI dataset and a simulated dataset incorporating variable coil sensitivity profiles. For system matrix recovery on the Open MPI dataset, VRF-Net achieved nRMSE = 0.403, pSNR = 39.08 dB, and SSIM = 0.835 at 2x scaling, and maintained strong performance even at challenging scale 8x (pSNR = 31.06 dB, SSIM = 0.717). For the simulated dataset, VRF-Net achieved nRMSE = 4.44, pSNR = 28.52 dB, and SSIM = 0.771 at 2x scaling, with stable performance at higher scales. On average, it reduced nRMSE by 88.2%, increased pSNR by 44.7%, and improved SSIM by 34.3% over interpolation and CNN-based methods. In image reconstruction of Open MPI phantoms, VRF-Net further reduced reconstruction error to nRMSE = 1.79 at 2x scaling, while preserving structural fidelity (pSNR = 41.58 dB, SSIM = 0.960), outperforming existing methods. These findings demonstrate that VRF-Net enables sharper, artifact-free system matrix recovery and robust image reconstruction across multiple scales, offering a promising direction for future in vivo applications.
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