Lee Philip K, Zhou Xuetong, Hargreaves Brian A
Radiology, Stanford University, Stanford, California, USA.
Bioengineering, Stanford University, Stanford, California, USA.
Magn Reson Med. 2025 Jun;93(6):2456-2472. doi: 10.1002/mrm.30484. Epub 2025 Mar 4.
Diffusion-prepared imaging is a flexible alternative to conventional spin-echo diffusion-weighted EPI that allows selection of different imaging readouts and k-space traversals, and permits control of image contrast or image artifacts. We investigate a new signal model and reconstruction for diffusion-prepared imaging that addresses signal variations caused by motion-sensitizing diffusion gradients.
A signal model, sampling theory, and reconstruction framework were developed assuming that motion-induced phases and the measured signals are random variables. The reconstruction incorporates real-valued amplitude weights estimated from low-resolution images into a linear system. A diffusion-prepared sequence was applied in phantom and in vivo acquisitions using different methods for managing phase errors from eddy currents or motion. This acquisition was performed with a high number of NEX and retrospectively undersampled to analyze errors in ADC estimation, and compared to spin-echo diffusion-weighted EPI, as well as conventional diffusion-prepared methods. The B sensitivity of the sequence was investigated using simulation and phantom experiments.
Images reconstructed using the proposed method had similar image structures when compared to conventional spin-echo diffusion-weighted EPI, and demonstrated improved SNR efficiency compared to previous diffusion-prepared approaches. ADC errors followed a trend consistent with the derived signal model, sampling theory, and expected B sensitivity. The sampling requirement was shown to depend on the magnitude of motion-induced phases, as well as phases from residual eddy currents.
Employing amplitude weights in the reconstruction of a diffusion-prepared sequence can improve SNR efficiency at the cost of a greater minimum sampling time and increased sensitivity to B inhomogeneity.
扩散准备成像（Diffusion-prepared imaging）是传统自旋回波扩散加权EPI的一种灵活替代方法，它允许选择不同的成像读出方式和k空间遍历方式，并能控制图像对比度或图像伪影。我们研究了一种用于扩散准备成像的新信号模型和重建方法，该方法可解决由运动敏感扩散梯度引起的信号变化问题。
在假设运动诱导相位和测量信号为随机变量的情况下，开发了一种信号模型、采样理论和重建框架。该重建方法将从低分辨率图像估计的实值幅度权重纳入线性系统。使用不同方法处理涡流或运动引起的相位误差，在体模和体内采集过程中应用扩散准备序列。采集时采用大量的NEX并进行回顾性欠采样，以分析ADC估计中的误差，并与自旋回波扩散加权EPI以及传统的扩散准备方法进行比较。使用模拟和体模实验研究了该序列的B敏感性。
与传统自旋回波扩散加权EPI相比，使用所提出方法重建的图像具有相似的图像结构，并且与先前的扩散准备方法相比，其SNR效率有所提高。ADC误差遵循与推导的信号模型、采样理论以及预期的B敏感性一致的趋势。结果表明，采样要求取决于运动诱导相位的大小以及残余涡流的相位。
在扩散准备序列的重建中采用幅度权重可以提高SNR效率，但代价是最小采样时间增加以及对B不均匀性的敏感性提高。
