Multi-band- and in-plane-accelerated diffusion MRI enabled by model-based deep learning in q-space and its extension to learning in the spherical harmonic domain.
Multi-band- and in-plane-accelerated diffusion MRI enabled by model-based deep learning in q-space and its extension to learning in the spherical harmonic domain.
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DOI：
10.1002/mrm.29095
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发表时间：
2022-04
影响因子：
3.3
通讯作者：
Jacob M
中科院分区：
文献类型：
--
作者：
Mani M;Yang B;Bathla G;Magnotta V;Jacob M
提出一种新的面内和多波段(MB)加速弥散联合数据恢复方法。将MB加速与面内加速相结合对于提高高(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因子和面内加速度的增加，由于严重的混叠，重建变得更加困难。新的重构在约束恢复之前利用了一个额外的Q空间，该Q空间是从先前提出的qModeL框架中推导出来的。具体地，qModeL先验提供测量数据所属的扩散信号空间的预学习表示。我们证明了预先学习的Q空间先验结合基于模型的迭代重建能够有效地重建面内和MB加速的数据。通过在k-q域中使用非相干欠采样模式来最大限度地利用联合重建的功率。我们在高/低角分辨率、高/低空间分辨率、健康/异常组织和3T/7T场强的单壳和多壳数据上测试了该方法。此外，将学习扩展到球谐和基，以提供旋转不变的学习框架。在所研究的所有情况下，qModeL联合重建被证明能够以合理的精度同时去混叠和联合恢复弥散加权像。对18倍加速的多壳数据集的重建误差为<；3%。从加速采集得到的微结构图对健康和异常组织也显示出相当的准确性。支持深度学习的重建结果与使用传统方法得到的重建结果相当。QModeL利用深度学习实现面内和MB加速的组合dMRI的联合恢复。
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. Further, 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 micro-structural 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 deep learning.
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用扩散MRI量化脑微观结构：理论和参数估计。
影响因子：
2.9
作者：
Novikov DS;Fieremans E;Jespersen SN;Kiselev VG
通讯作者：
Kiselev VG
DOI：
10.1006/jmrb.1994.1037
发表时间：
1994-03-01
期刊：
JOURNAL OF MAGNETIC RESONANCE SERIES B
影响因子：
--
作者：
BASSER, PJ;MATTIELLO, J;LEBIHAN, D
通讯作者：
LEBIHAN, D
影响因子：
2.2
作者：
CONOLLY, S;NISHIMURA, D;GLOVER, G
通讯作者：
GLOVER, G
影响因子：
10.6
作者：
Christiaens, Daan;Cordero-Grande, Lucilio;Tournier, J-Donald
通讯作者：
Tournier, J-Donald
影响因子：
3.3
作者：
Barth M;Breuer F;Koopmans PJ;Norris DG;Poser BA
通讯作者：
Poser BA