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Improved Myelin Water Imaging Using B1+ Correction and Data-Driven Global Feature Extraction: Application on People with MS

Imaging Neuroscience(2024)

Department of Biomedical Engineering

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Abstract
The predominant technique for quantifying myelin content in the white matter is multi-compartment analysis of MRI’s T2 relaxation times (mcT2 analysis). The process of resolving the T2 spectrum at each voxel, however, is highly ill-posed and remarkably susceptible to noise and to inhomogeneities of the transmit field (B1+). To address these challenges, we employ a preprocessing stage wherein a spatially global data-driven analysis of the tissue is performed to identify a set of mcT2 configurations (motifs) that best describe the tissue under investigation, followed by using this basis set to analyze the signal in each voxel. This procedure is complemented by a new algorithm for correcting B1+ inhomogeneities, lending the overall fitting process with improved robustness and reproducibility. Successful validations are presented using numerical and physical phantoms vs. ground truth, showcasing superior fitting accuracy and precision compared to conventional (non data-driven) fitting. In vivo application of the technique is presented on 26 healthy subjects and 29 people living with multiple sclerosis (MS), revealing substantial reduction in myelin content within normal-appearing white matter regions of people with MS (i.e., outside obvious lesions), and confirming the potential of data-driven myelin values as a radiologic biomarker for MS.
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要点】:本文提出了一种改进的多组分T2弛豫时间分析技术,通过B1+校正和基于数据驱动的全局特征提取,提高了髓鞘含量测量的准确性和可重复性,并应用于多发性硬化症(MS)患者的髓鞘含量评估。

方法】:采用基于数据驱动的全局特征提取技术,通过分析组织特征来识别最佳描述待测组织的mcT2配置(模式),并结合B1+不均匀性校正算法,提高信号拟合的鲁棒性和可重复性。

实验】:通过数值和物理 phantom与真实值对比验证,展示了该方法在拟合准确性和精度上的优势。在26名健康受试者和29名MS患者中进行体内实验,使用数据驱动的髓鞘含量值,发现MS患者的正常外观白质区域髓鞘含量显著减少,验证了该方法作为MS放射学生物标志物的潜力。数据集包括健康受试者和MS患者的MRI数据。