Mechanical Engineering Department, Imperial College London, London, UK.
MSK Lab, Department of Surgery and Cancer, Imperial College London, UK.
Comput Med Imaging Graph. 2020 Dec;86:101796. doi: 10.1016/j.compmedimag.2020.101796. Epub 2020 Oct 9.
In tissues containing significant amounts of organised collagen, such as tendons, ligaments, menisci and articular cartilage, MR imaging exhibits a strong signal intensity variation caused by the angle between the collagen fibres and the magnetic field. By obtaining scans at different field orientations it is possible to determine the unknown fibre orientations and to deduce the underlying tissue microstructure. Our previous work demonstrated how this method can detect ligament injuries and maturity-related changes in collagen fibre structures. Practical application in human diagnostics will demand minimisation of scanning time and likely use of open low-field scanners that can allow re-orienting of the main field. This paper analyses the performance of collage fibre estimation for various image SNR values, and in relation to key parameters including number of scanning directions and parameters of the reconstruction algorithm. The analysis involved Monte Carlo simulation studies which provided benchmark performance measures, and studies using MR images of caprine knee samples with increasing levels of synthetic added noise. Tractography plots in the form of streamlines were performed, and an Alignment Index (AI) was employed as a measure of the detected orientation distribution. The results are highly encouraging, showing high accuracy and robustness even for low image SNR values.
在含有大量组织胶原的组织中，如肌腱、韧带、半月板和关节软骨，MR 成像表现出由于胶原纤维与磁场之间的角度而导致的强烈信号强度变化。通过在不同的场方向获取扫描，可以确定未知的纤维方向，并推断出潜在的组织微观结构。我们之前的工作表明，这种方法如何检测韧带损伤和胶原纤维结构的成熟相关变化。在人类诊断中的实际应用将需要最小化扫描时间，并可能使用可以重新定向主磁场的开放式低场扫描仪。本文分析了各种图像 SNR 值下胶原纤维估计的性能，以及与关键参数的关系，包括扫描方向的数量和重建算法的参数。该分析涉及提供基准性能指标的蒙特卡罗模拟研究，以及使用具有递增水平的合成添加噪声的山羊膝关节样本的 MR 图像进行的研究。以流线图的形式进行了轨迹分析，并采用了对齐指数 (AI) 作为检测方向分布的度量。结果非常令人鼓舞，即使对于低图像 SNR 值，也显示出了很高的准确性和鲁棒性。
