Ye Xinyu, Miller Karla L, Wu Wenchuan
Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Magn Reson Med. 2025 Aug;94(2):694-712. doi: 10.1002/mrm.30518. Epub 2025 Apr 6.
This work aims to propose a robust reconstruction method exploiting shared information across shells to increase the acquisition speed of multi-shell diffusion-weighted MRI (dMRI), enabling rapid tissue microstructure mapping.
Local q-space points share similar information. Gaussian Process can exploit the q-space smoothness in a data-driven way and provide q-space signal estimation based on the signals from a q-space neighborhood. The Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER) method uses the signal estimation from Gaussian process as a prior in a joint k-q reconstruction and improves image quality under high acceleration factors compared to conventional (k-only) reconstruction. In this work, we extend the DAGER method by introducing a multi-shell covariance function and correcting for Rician noise distribution in magnitude data when fitting the Gaussian process model. The method was evaluated with both simulation and in vivo data.
Simulated and in-vivo results demonstrate that the proposed method can significantly improve the image quality of reconstructed dMRI data with high acceleration both in-plane and slice-wise, achieving a total acceleration factor of 12. The improvement of image quality allows more robust diffusion model fitting compared to conventional reconstruction methods, enabling advanced multi-shell diffusion analysis within much shorter scan time.
The proposed method enables highly accelerated dMRI which can shorten the scan time of multi-shell dMRI without sacrificing quality compared to conventional practice. This may facilitate a wider application of advanced dMRI models in basic and clinical neuroscience.
本研究旨在提出一种稳健的重建方法，利用不同回波链间的共享信息来提高多回波链扩散加权磁共振成像（dMRI）的采集速度，从而实现快速的组织微结构成像。
局部q空间点共享相似信息。高斯过程可以以数据驱动的方式利用q空间的平滑性，并基于q空间邻域的信号提供q空间信号估计。高斯过程估计重建的扩散加速（DAGER）方法在联合k-q重建中使用高斯过程的信号估计作为先验，与传统（仅k空间）重建相比，在高加速因子下提高了图像质量。在本研究中，我们通过引入多回波链协方差函数并在拟合高斯过程模型时校正幅度数据中的莱斯噪声分布来扩展DAGER方法。该方法通过模拟数据和体内数据进行评估。
模拟和体内结果表明，所提出的方法可以显著提高重建dMRI数据在平面内和层面上的高加速图像质量，实现12的总加速因子。与传统重建方法相比，图像质量的提高允许更稳健的扩散模型拟合，从而在更短的扫描时间内实现先进的多回波链扩散分析。
所提出的方法能够实现高度加速的dMRI，与传统方法相比，在不牺牲质量的情况下缩短了多回波链dMRI的扫描时间。这可能有助于先进的dMRI模型在基础和临床神经科学中得到更广泛的应用。
