Uwaeze Jason, Narayana Ponnada A, Kamali Arash, Braverman Vladimir, Jacobs Michael A, Akhbardeh Alireza
Department of Computer Science, Rice University, Houston, TX 77005, USA.
Department of Diagnostic and Interventional Imaging, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Diagnostics (Basel). 2024 Mar 16;14(6):632. doi: 10.3390/diagnostics14060632.
Identifying active lesions in magnetic resonance imaging (MRI) is crucial for the diagnosis and treatment planning of multiple sclerosis (MS). Active lesions on MRI are identified following the administration of Gadolinium-based contrast agents (GBCAs). However, recent studies have reported that repeated administration of GBCA results in the accumulation of Gd in tissues. In addition, GBCA administration increases health care costs. Thus, reducing or eliminating GBCA administration for active lesion detection is important for improved patient safety and reduced healthcare costs. Current state-of-the-art methods for identifying active lesions in brain MRI without GBCA administration utilize data-intensive deep learning methods.
To implement nonlinear dimensionality reduction (NLDR) methods, locally linear embedding (LLE) and isometric feature mapping (Isomap), which are less data-intensive, for automatically identifying active lesions on brain MRI in MS patients, without the administration of contrast agents.
Fluid-attenuated inversion recovery (FLAIR), T2-weighted, proton density-weighted, and pre- and post-contrast T1-weighted images were included in the multiparametric MRI dataset used in this study. Subtracted pre- and post-contrast T1-weighted images were labeled by experts as active lesions (ground truth). Unsupervised methods, LLE and Isomap, were used to reconstruct multiparametric brain MR images into a single embedded image. Active lesions were identified on the embedded images and compared with ground truth lesions. The performance of NLDR methods was evaluated by calculating the Dice similarity (DS) index between the observed and identified active lesions in embedded images.
LLE and Isomap, were applied to 40 MS patients, achieving median DS scores of 0.74 ± 0.1 and 0.78 ± 0.09, respectively, outperforming current state-of-the-art methods.
NLDR methods, Isomap and LLE, are viable options for the identification of active MS lesions on non-contrast images, and potentially could be used as a clinical decision tool.
在磁共振成像（MRI）中识别活动性病变对于多发性硬化症（MS）的诊断和治疗规划至关重要。MRI上的活动性病变是在给予钆基造影剂（GBCA）后识别出来的。然而，最近的研究报告称，重复给予GBCA会导致钆在组织中蓄积。此外，给予GBCA会增加医疗成本。因此，减少或消除用于检测活动性病变的GBCA给药对于提高患者安全性和降低医疗成本很重要。当前在不给予GBCA的情况下识别脑MRI活动性病变的最先进方法使用数据密集型深度学习方法。
实施非线性降维（NLDR）方法，即局部线性嵌入（LLE）和等距特征映射（Isomap），它们数据密集程度较低，用于在不给予造影剂的情况下自动识别MS患者脑MRI上的活动性病变。
本研究使用的多参数MRI数据集中包括液体衰减反转恢复（FLAIR）、T2加权、质子密度加权以及造影前和造影后的T1加权图像。造影前和造影后的T1加权相减图像由专家标记为活动性病变（真实情况）。使用无监督方法LLE和Isomap将多参数脑MR图像重建为单个嵌入图像。在嵌入图像上识别活动性病变并与真实病变进行比较。通过计算嵌入图像中观察到的和识别出的活动性病变之间的骰子相似性（DS）指数来评估NLDR方法的性能。
将LLE和Isomap应用于40例MS患者，DS得分中位数分别为0.74±0.1和0.78±0.09，优于当前的最先进方法。
NLDR方法Isomap和LLE是在无对比剂图像上识别MS活动性病变的可行选择，并且有可能用作临床决策工具。
