Objective

To investigate whether varied or repeated b-values provide better diffusion MRI data for discriminating cortical areas with a data-driven approach.

Methods

Data were acquired from three volunteers at 1.5T with b-values of 800, 1400, 2000 s/mm² along 64 diffusion-encoding directions. The diffusion signal was sampled from gray matter in seven regions of interest (ROIs). Rotational invariants of the local diffusion profile were extracted as features that characterize local tissue properties. Random forest classification experiments assessed whether classification accuracy improved when data with multiple b-values were used over repeated acquisition of the same (1400 s/mm²) b-value to compare all possible pairs of the seven ROIs. Three data sets from the Human Connectome Project were subjected to similar processing and analysis pipelines in eight ROIs.

Results

Three different b-values showed an average improvement in correct classification rates of 5.6% and 4.6%, respectively, in the local and HCP data over repeated measurements of the same b-value. The improvement in correct classification rate reached as high as 16% for individual binary classification experiments between two ROIs. Often using only two of the available three b-values were adequate to make such an improvement in classification rates.

Conclusion

Acquisitions with varying b-values are more suitable for discriminating cortical areas.

中文翻译：

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多个 b 值提高了使用扩散 MRI 对皮质灰质区域的辨别能力：使用数据驱动方法进行的实验验证

客观的

研究变化或重复的b值是否提供更好的弥散 MRI 数据，以使用数据驱动的方法区分皮层区域。

方法

数据是在 1.5T 下从三个志愿者那里获得的，b值为 800、1400、2000 s/mm ²沿 64 个扩散编码方向。扩散信号是从七个感兴趣区域 (ROI) 的灰质中采样的。局部扩散轮廓的旋转不变量被提取为表征局部组织特性的特征。随机森林分类实验评估了当使用具有多个b值的数据而不是重复采集相同 (1400 s/mm ² ) b值来比较七个 ROI 的所有可能对时，分类准确性是否提高。来自 Human Connectome Project 的三个数据集在八个 ROI 中进行了类似的处理和分析管道。

结果

与重复测量相同的b值相比，在本地和 HCP 数据中，三个不同的b值显示正确分类率的平均改进分别为 5.6% 和 4.6% 。对于两个 ROI 之间的单个二元分类实验，正确分类率的提高高达 16%。通常只使用可用的三个b值中的两个就足以使分类率提高。

结论

具有不同b值的采集更适合区分皮层区域。