Center for QUantitative Imaging in Medicine (CQUIM), Department of Medical Research, Taichung Veterans General Hospital, Taichung, Taiwan.
Department of Biomedical Engineering, Hungkuang University, Taichung, Taiwan.
Biomed Res Int. 2021 Mar 7;2021:9820145. doi: 10.1155/2021/9820145. eCollection 2021.
Accurate quantification of brain tissue is a fundamental and challenging task in neuroimaging. Over the past two decades, statistical parametric mapping (SPM) and FMRIB's Automated Segmentation Tool (FAST) have been widely used to estimate gray matter (GM) and white matter (WM) volumes. However, they cannot reliably estimate cerebrospinal fluid (CSF) volumes. To address this problem, we developed the TRIO algorithm (TRIOA), a new magnetic resonance (MR) multispectral classification method. SPM8, SPM12, FAST, and the TRIOA were evaluated using the BrainWeb database and real magnetic resonance imaging (MRI) data. In this paper, the MR brain images of 140 healthy volunteers (51.5 ± 15.8 y/o) were obtained using a whole-body 1.5 T MRI system (Aera, Siemens, Erlangen, Germany). Before classification, several preprocessing steps were performed, including skull stripping and motion and inhomogeneity correction. After extensive experimentation, the TRIOA was shown to be more effective than SPM and FAST. For real data, all test methods revealed that the participants aged 20-83 years exhibited an age-associated decline in GM and WM volume fractions. However, for CSF volume estimation, SPM8-s and SPM12-m both produced different results, which were also different compared with those obtained by FAST and the TRIOA. Furthermore, the TRIOA performed consistently better than both SPM and FAST for GM, WM, and CSF volume estimation. Compared with SPM and FAST, the proposed TRIOA showed more advantages by providing more accurate MR brain tissue classification and volume measurements, specifically in CSF volume estimation.
准确量化脑组织是神经影像学中的一项基本且具有挑战性的任务。在过去的二十年中，统计参数映射（SPM）和 FMRIB 的自动分割工具（FAST）已被广泛用于估计灰质（GM）和白质（WM）体积。然而，它们不能可靠地估计脑脊髓液（CSF）体积。为了解决这个问题，我们开发了 TRIO 算法（TRIOA），这是一种新的磁共振（MR）多谱分类方法。使用 BrainWeb 数据库和真实磁共振成像（MRI）数据评估了 SPM8、SPM12、FAST 和 TRIOA。在本文中，使用全身 1.5T MRI 系统（Aera，Siemens，Erlangen，德国）获得了 140 名健康志愿者（51.5±15.8 岁）的 MR 脑图像。在分类之前，进行了几个预处理步骤，包括颅骨剥离和运动和不均匀性校正。经过广泛的实验，TRIOA 被证明比 SPM 和 FAST 更有效。对于真实数据，所有测试方法都表明，年龄在 20-83 岁的参与者的 GM 和 WM 体积分数随年龄增长而下降。然而，对于 CSF 体积估计，SPM8-s 和 SPM12-m 都产生了不同的结果，与 FAST 和 TRIOA 的结果也不同。此外，TRIOA 在 GM、WM 和 CSF 体积估计方面的表现均优于 SPM 和 FAST。与 SPM 和 FAST 相比，所提出的 TRIOA 通过提供更准确的 MR 脑组织分类和体积测量，特别是在 CSF 体积估计方面，具有更多优势。
