Wong Andy K O, Szabo Eva, Erlandson Marta, Sussman Marshall S, Duggina Sravani, Song Anny, Reitsma Shannon, Gillick Hana, Adachi Jonathan D, Cheung Angela M
Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada; University Health Network, Osteoporosis Program, Toronto General Research Institute, Toronto, Ontario, Canada; McMaster University, Department of Medicine, Faculty of Health Sciences, Hamilton, Ontario, Canada.
Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada.
J Clin Densitom. 2020 Oct-Dec;23(4):611-622. doi: 10.1016/j.jocd.2018.09.007. Epub 2018 Sep 22.
The accumulation of INTERmuscular fat and INTRAmuscular fat (IMF) has been a hallmark of individuals with diabetes, those with mobility impairments such as spinal cord injuries and is known to increase with aging. An elevated amount of IMF has been associated with fractures and frailty, but the imprecision of IMF measurement has so far limited the ability to observe more consistent clinical associations. Magnetic resonance imaging has been recognized as the gold standard for portraying these features, yet reliable methods for quantifying IMF on magnetic resonance imaging is far from standardized. Previous investigators used manual segmentation guided by histogram-based region-growing, but these techniques are subjective and have not demonstrated reliability. Others applied fuzzy classification, machine learning, and atlas-based segmentation methods, but each is limited by the complexity of implementation or by the need for a learning set, which must be established each time a new disease cohort is examined. In this paper, a simple convergent iterative threshold-optimizing algorithm was explored. The goal of the algorithm is to enable IMF quantification from plain fast spin echo (FSE) T1-weighted MR images or from water-saturated images. The algorithm can be programmed into Matlab easily, and is semiautomated, thus minimizing the subjectivity of threshold-selection. In 110 participants from 3 cohort studies, IMF area measurement demonstrated a high degree of reproducibility with errors well within the 5% benchmark for intraobserver, interobserver, and test-retest analyses; in contrast to manual segmentation which already yielded over 20% error for intraobserver analysis. This algorithm showed validity against manual segmentations (r > 0.85). The simplicity of this technique lends itself to be applied to fast spin echo images commonly ordered as part of standard of care and does not require more advanced fat-water separated images.
肌间脂肪和肌内脂肪（IMF）的蓄积一直是糖尿病患者、脊髓损伤等行动不便者的特征，且已知会随着年龄增长而增加。IMF含量升高与骨折和身体虚弱有关，但迄今为止，IMF测量的不精确性限制了观察更一致临床关联的能力。磁共振成像已被公认为描绘这些特征的金标准，但在磁共振成像上定量IMF的可靠方法远未标准化。先前的研究人员使用基于直方图的区域生长引导的手动分割，但这些技术主观性强，且未显示出可靠性。其他人应用了模糊分类、机器学习和基于图谱的分割方法，但每种方法都受到实施复杂性或对学习集需求的限制，每次检查新的疾病队列时都必须建立学习集。在本文中，探索了一种简单的收敛迭代阈值优化算法。该算法的目标是能够从普通快速自旋回波（FSE）T1加权磁共振图像或水饱和图像中定量IMF。该算法可以轻松编程到Matlab中，并且是半自动的，从而最大限度地减少了阈值选择的主观性。在来自3个队列研究的110名参与者中，IMF面积测量显示出高度的可重复性，观察者内、观察者间和重测分析的误差均远低于5%的基准；相比之下，手动分割在观察者内分析中已经产生了超过20%的误差。该算法与手动分割相比显示出有效性（r>0.85）。这项技术的简单性使其适用于作为标准护理一部分通常订购的快速自旋回波图像，并且不需要更先进的脂肪-水分离图像。
