3D多线圈非笛卡尔采样下，深度学习重建实现高精度脑部成像。
Benchmarking 3D multi-coil NC-PDNet MRI reconstruction
- 扩展NC-PDNet至3D多线圈非笛卡尔采样场景
- 平均PSNR达42.98 dB，重建时间仅4.95秒
- 适合临床科研中高分辨率3D MRI重建
深度学习在欠采样MRI重建中展现出巨大潜力，但针对3D并行成像与非笛卡尔欠采样结合的研究仍不足。本文将前沿的非笛卡尔原始-对偶网络（NC-PDNet）拓展至3D多线圈设置，评估了通道特异性与通道无关训练配置的影响，并研究了线圈压缩效果。在公开的Calgary-Campinas数据集上，对比四种不同非笛卡尔欠采样模式，加速因子为6，实现1毫米各向同性32通道全脑3D重建。训练使用压缩数据且输入通道数可变，平均峰值信噪比（PSNR）达到42.98 dB，推理时间4.95秒，GPU显存占用5.49 GB，展现出显著临床研究应用前景。
原文摘要 · Abstract (English)
Deep learning has shown great promise for MRI reconstruction from undersampled data, yet there is a lack of research on validating its performance in 3D parallel imaging acquisitions with non-Cartesian undersampling. In addition, the artifacts and the resulting image quality depend on the under-sampling pattern. To address this uncharted territory, we extend the Non-Cartesian Primal-Dual Network (NC-PDNet), a state-of-the-art unrolled neural network, to a 3D multi-coil setting. We evaluated the impact of channel-specific versus channel-agnostic training configurations and examined the effect of coil compression. Finally, we benchmark four distinct non-Cartesian undersampling patterns, with an acceleration factor of six, using the publicly available Calgary-Campinas dataset. Our results show that NC-PDNet trained on compressed data with varying input channel numbers achieves an average PSNR of 42.98 dB for 1 mm isotropic 32 channel whole-brain 3D reconstruction. With an inference time of 4.95sec and a GPU memory usage of 5.49 GB, our approach demonstrates significant potential for clinical research application.
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