The Australian e-Health Research Centre, CSIRO, Queensland, Australia.
Univ Rennes, INSERM, LTSI-UMR1099, Rennes, France.
PLoS One. 2022 Feb 18;17(2):e0247343. doi: 10.1371/journal.pone.0247343. eCollection 2022.
Magnetic Resonance Imaging (MRI) motion artefacts frequently complicate structural and diffusion MRI analyses. While diffusion imaging is easily 'scrubbed' of motion affected volumes, the same is not true for T1w or T2w 'structural' images. Structural images are critical to most diffusion-imaging pipelines thus their corruption can lead to disproportionate data loss. To enable diffusion-image processing when structural images are missing or have been corrupted, we propose a means by which synthetic structural images can be generated from diffusion MRI. This technique combines multi-tissue constrained spherical deconvolution, which is central to many existing diffusion analyses, with the Bloch equations that allow simulation of MRI intensities for given scanner parameters and magnetic resonance (MR) tissue properties. We applied this technique to 32 scans, including those acquired on different scanners, with different protocols and with pathology present. The resulting synthetic T1w and T2w images were visually convincing and exhibited similar tissue contrast to acquired structural images. These were also of sufficient quality to drive a Freesurfer-based tractographic analysis. In this analysis, probabilistic tractography connecting the thalamus to the primary sensorimotor cortex was delineated with Freesurfer, using either real or synthetic structural images. Tractography for real and synthetic conditions was largely identical in terms of both voxels encountered (Dice 0.88-0.95) and mean fractional anisotropy (intrasubject absolute difference 0.00-0.02). We provide executables for the proposed technique in the hope that these may aid the community in analysing datasets where structural image corruption is common, such as studies of children or cognitively impaired persons.
磁共振成像（MRI）运动伪影经常使结构和弥散 MRI 分析变得复杂。虽然弥散成像很容易“清除”受运动影响的体积，但 T1w 或 T2w“结构”图像则不然。结构图像对大多数弥散成像管道至关重要，因此它们的损坏会导致不成比例的数据丢失。为了在缺少结构图像或结构图像损坏的情况下进行弥散图像处理，我们提出了一种从弥散 MRI 生成合成结构图像的方法。该技术将多组织约束球分解（这是许多现有的弥散分析的核心）与 Bloch 方程相结合，Bloch 方程允许根据给定的扫描仪参数和磁共振（MR）组织特性模拟 MRI 强度。我们将该技术应用于 32 次扫描，包括在不同扫描仪上、具有不同协议和存在病理学的扫描。生成的合成 T1w 和 T2w 图像在视觉上令人信服，并表现出与获取的结构图像相似的组织对比。这些图像的质量也足以驱动基于 Freesurfer 的追踪分析。在该分析中，使用 Freesurfer 对丘脑与初级感觉运动皮层之间的连接进行了基于概率的追踪，使用的是真实的或合成的结构图像。真实和合成条件下的追踪在遇到的体素（Dice 0.88-0.95）和平均各向异性分数（受试者内绝对差异 0.00-0.02）方面基本相同。我们提供了所提出技术的可执行文件，希望这些文件可以帮助社区分析结构图像损坏较为常见的数据集，例如儿童或认知障碍患者的研究。
