Van Steenkiste Gwendolyn, Jeurissen Ben, Veraart Jelle, den Dekker Arnold J, Parizel Paul M, Poot Dirk H J, Sijbers Jan
iMinds-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium.
Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands.
Magn Reson Med. 2016 Jan;75(1):181-95. doi: 10.1002/mrm.25597. Epub 2015 Jan 22.
Diffusion MRI is hampered by long acquisition times, low spatial resolution, and a low signal-to-noise ratio. Recently, methods have been proposed to improve the trade-off between spatial resolution, signal-to-noise ratio, and acquisition time of diffusion-weighted images via super-resolution reconstruction (SRR) techniques. However, during the reconstruction, these SRR methods neglect the q-space relation between the different diffusion-weighted images.
An SRR method that includes a diffusion model and directly reconstructs high resolution diffusion parameters from a set of low resolution diffusion-weighted images was proposed. Our method allows an arbitrary combination of diffusion gradient directions and slice orientations for the low resolution diffusion-weighted images, optimally samples the q- and k-space, and performs motion correction with b-matrix rotation.
Experiments with synthetic data and in vivo human brain data show an increase of spatial resolution of the diffusion parameters, while preserving a high signal-to-noise ratio and low scan time. Moreover, the proposed SRR method outperforms the previous methods in terms of the root-mean-square error.
The proposed SRR method substantially increases the spatial resolution of MRI that can be obtained in a clinically feasible scan time.
扩散磁共振成像（Diffusion MRI）受到采集时间长、空间分辨率低和信噪比低的限制。最近，有人提出通过超分辨率重建（SRR）技术来改善扩散加权图像在空间分辨率、信噪比和采集时间之间的权衡。然而，在重建过程中，这些SRR方法忽略了不同扩散加权图像之间的q空间关系。
提出了一种SRR方法，该方法包括一个扩散模型，并直接从一组低分辨率扩散加权图像重建高分辨率扩散参数。我们的方法允许对低分辨率扩散加权图像的扩散梯度方向和切片方向进行任意组合，对q空间和k空间进行优化采样，并通过b矩阵旋转进行运动校正。
合成数据和体内人脑数据实验表明，扩散参数的空间分辨率有所提高，同时保持了高信噪比和低扫描时间。此外，在均方根误差方面，所提出的SRR方法优于以前的方法。
所提出的SRR方法在临床可行的扫描时间内显著提高了MRI的空间分辨率。
