Department of Radiology, University of Iowa, Iowa City, Iowa, USA.
Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA.
Magn Reson Med. 2022 Apr;87(4):1799-1815. doi: 10.1002/mrm.29095. Epub 2021 Nov 26.
To propose a new method for the recovery of combined in-plane- and multi-band (MB)-accelerated diffusion MRI data.
Combining MB acceleration with in-plane acceleration is crucial to improve the time efficiency of high (angular and spatial) resolution diffusion scans. However, as the MB factor and in-plane acceleration increase, the reconstruction becomes challenging due to the heavy aliasing. The new reconstruction utilizes an additional q-space prior to constrain the recovery, which is derived from the previously proposed qModeL framework. Specifically, the qModeL prior provides a pre-learned representation of the diffusion signal space to which the measured data belongs. We show that the pre-learned q-space prior along with a model-based iterative reconstruction that accommodate multi-band unaliasing, can efficiently reconstruct the in-plane- and MB-accelerated data. The power of joint reconstruction is maximally utilized by using an incoherent under-sampling pattern in the k-q domain. We tested the proposed method on single- and multi-shell data, with high/low angular resolution, high/low spatial resolution, healthy/abnormal tissues, and 3T/7T field strengths. Furthermore, the learning is extended to the spherical harmonic basis, to provide a rotational invariant learning framework.
The qModeL joint reconstruction is shown to simultaneously unalias and jointly recover DWIs with reasonable accuracy in all the cases studied. The reconstruction error from 18-fold accelerated multi-shell datasets was <3%. The microstructural maps derived from the accelerated acquisitions also exhibit reasonable accuracy for both healthy and abnormal tissues. The deep learning (DL)-enabled reconstructions are comparable to those derived using traditional methods.
qModeL enables the joint recovery of combined in-plane- and MB-accelerated dMRI utilizing DL.
提出一种新的方法用于恢复联合平面内和多频带（MB）加速扩散 MRI 数据。
结合 MB 加速与平面内加速对于提高高（角度和空间）分辨率扩散扫描的时间效率至关重要。然而，随着 MB 因子和平面内加速的增加，由于严重的混叠，重建变得具有挑战性。新的重建利用了一个附加的 q 空间先验来约束恢复，该先验来自先前提出的 qModeL 框架。具体来说，qModeL 先验提供了扩散信号空间的预先学习表示，测量数据属于该空间。我们表明，预先学习的 q 空间先验与适应多频带去混叠的基于模型的迭代重建相结合，可以有效地重建平面内和 MB 加速的数据。通过在 k-q 域中使用非相干欠采样模式，最大程度地利用联合重建的优势。我们在单壳和多壳数据、高/低角度分辨率、高/低空间分辨率、健康/异常组织以及 3T/7T 场强下测试了所提出的方法。此外，学习扩展到了球谐函数基，以提供旋转不变的学习框架。
qModeL 联合重建被证明能够在所有研究的情况下同时去混叠并联合恢复 DWIs，具有合理的准确性。18 倍加速的多壳数据集的重建误差<3%。来自加速采集的微观结构图也表现出健康和异常组织的合理准确性。基于深度学习（DL）的重建与传统方法得出的重建相当。
qModeL 能够利用 DL 联合恢复联合平面内和 MB 加速的 dMRI。
