Improving delineation of the corticospinal tract in the monkey brain scanned with conventional DTI by using a compressed sensing based algorithm.

Yuguang Meng, Chun-Xia Li, Xiaodong Zhang
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Abstract

Background: The corticospinal tract (CST) is a major tract for motor function. It can be impaired by stroke. Its degeneration is associated with stroke outcome. Diffusion tensor imaging (DTI) tractography plays an important role in assessing fiber bundle integrity. However, it is limited in detecting crossing fibers in the brain. The crossing fiber angular resolution of intra-voxel structure (CFARI) algorithm shows potential to resolve complex fibers in the brain. The objective of the present study was to improve delineation of CST pathways in monkey brains scanned by conventional DTI.

Methods: Healthy rhesus monkeys were scanned by diffusion MRI with 128 diffusion encoding directions to evaluate the CFARI algorithm. Four monkeys with ischemic occlusion were also scanned with DTI (b = 1000 s/mm2, 30 diffusion directions) at 6, 48, and 96 hours post stroke. CST fibers were reconstructed with DTI and CFARI-based tractography and evaluated. A two-way repeated MANOVA was used to determine significances of changes in DTI indices, tract number, and volumes of the CST between hemispheres or post-stroke time points.

Results: CFARI algorithm revealed substantially more fibers originated from the ventral premotor cortex in healthy and stroke monkey brains than DTI tractography. In addition, CFARI showed better sensitivity in detecting CST abnormality than DTI tractography following stroke.

Conclusion: CFARI significantly improved delineation of the CST in the brain scanned by DTI with 30 gradient directions. It showed better sensitivity in detecting abnormity of the CST following stroke. Preliminary results suggest that CFARI could facilitate prediction of function outcomes after stroke.

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使用基于压缩传感的算法改进用传统 DTI 扫描的猴脑皮质脊髓束的划分。
背景:皮质脊髓束(CST)是运动功能的主要通道。中风会损害它。其退化与中风预后有关。弥散张量成像(DTI)束成像在评估纤维束完整性方面发挥着重要作用。然而,它在检测大脑中的交叉纤维方面存在局限性。交叉纤维象素内结构角分辨率(CFARI)算法显示了解析大脑中复杂纤维的潜力。本研究的目的是改进传统 DTI 扫描猴脑中 CST 通路的划分。在中风后 6、48 和 96 小时,还用 DTI(b = 1000 s/mm2,30 个扩散方向)扫描了四只缺血性闭塞的猴子。用 DTI 和基于 CFARI 的束学重建 CST 纤维并进行评估。采用双向重复 MANOVA 来确定不同半球或卒中后不同时间点之间 CST 的 DTI 指数、束数和体积变化的显著性:结果:与 DTI tractography 相比,CFARI 算法显示健康猴脑和中风猴脑中源自腹侧运动前皮层的纤维更多。此外,与脑卒中后的 DTI tractography 相比,CFARI 在检测 CST 异常方面表现出更高的灵敏度:结论:CFARI 能明显改善 30 个梯度方向的 DTI 扫描大脑中 CST 的划分。结论:CFARI 能明显改善 30 个梯度方向的 DTI 扫描脑部 CST 的划分,在检测脑卒中后 CST 异常方面表现出更高的灵敏度。初步结果表明,CFARI 可以帮助预测脑卒中后的功能结果。
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