用神经场加速2D动态相位对比MRI，大幅缩短扫描时间。
Neural Fields for Highly Accelerated 2D Cine Phase Contrast MRI
- 用神经场联合建模复杂图像的幅值与相位，连续表示时空数据。
- 在32×至64×下仍保持低误差，16×下低时序数据也表现优异。
- 适合需要快速精准血流成像的临床研究者使用。
2D动态相位对比（CPC）MRI可提供人体血管内血流速度与流量的定量信息，但数据采集耗时，需通过欠采样重建来缩短扫描时间。本文提出将神经场作为复数图像的连续时空参数化方法，联合建模多回波下的幅值与相位，实现速度估计，并利用其归纳偏置进行速度数据重建。为缓解严重欠采样下神经场重建的过度平滑问题，引入简单的体素后处理步骤。方法在笛卡尔与径向k空间下均进行了数值验证，涵盖高、低时间分辨率数据。结果表明，该方法在高加速因子下仍能实现精确重建：高时间分辨率数据在32×和64×欠采样下误差极低，低时间分辨率数据在16×下亦表现良好，且在流速估计与解剖结构呈现上持续优于经典的局部低秩正则化体素方法。
原文摘要 · Abstract (English)
2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time-consuming, motivating the reconstruction of the velocity field from undersampled measurements to reduce scan times. In this work, neural fields are proposed as a continuous spatiotemporal parametrization of complex-valued images, jointly modeling magnitude and phase across multiple echoes to enable velocity estimation, and leveraging their inductive bias for the reconstruction of the velocity data. Additionally, to compensate for the oversmoothing tendency observed in neural-field reconstructions under severe undersampling, a simple voxel-based postprocessing step is introduced. The method is validated numerically in Cartesian and radial k-space with both high and low temporal resolution data. This approach achieves accurate reconstructions at high acceleration factors, with low errors even at 32$\times$ and 64$\times$ undersampling for the high temporal resolution data, and 16$\times$ for the low temporal resolution data, and consistently outperforms classical locally low-rank regularized voxel-based methods in both flow estimates and anatomical depiction.
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