DiCIPHR (Diffusion and Connectomics in Precision Healthcare Research) Lab, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.
Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America.
PLoS One. 2020 May 29;15(5):e0233645. doi: 10.1371/journal.pone.0233645. eCollection 2020.
Characterization of healthy versus pathological tissue in the peritumoral area is confounded by the presence of edema, making free water estimation the key concern in modeling tissue microstructure. Most methods that model tissue microstructure are either based on advanced acquisition schemes not readily available in the clinic or are not designed to address the challenge of edema. This underscores the need for a robust free water elimination (FWE) method that estimates free water in pathological tissue but can be used with clinically prevalent single-shell diffusion tensor imaging data. FWE in single-shell data requires the fitting of a bi-compartment model, which is an ill-posed problem. Its solution requires optimization, which relies on an initialization step. We propose a novel initialization approach for FWE, FERNET, which improves the estimation of free water in edematous and infiltrated peritumoral regions, using single-shell diffusion MRI data. The method has been extensively investigated on simulated data and healthy dataset. Additionally, it has been applied to clinically acquired data from brain tumor patients to characterize the peritumoral region and improve tractography in it.
在肿瘤周围区域中，正常组织与病变组织的特征因水肿的存在而变得复杂，因此自由水估计成为建模组织微观结构的关键关注点。大多数建模组织微观结构的方法要么基于临床不可用的高级采集方案，要么不是专门针对水肿挑战设计的。这凸显了需要一种稳健的自由水消除（FWE）方法，该方法可以估计病变组织中的自由水，但可以与临床常见的单壳扩散张量成像数据一起使用。单壳数据中的 FWE 需要拟合双室模型，这是一个不适定问题。其解决方案需要优化，这依赖于初始化步骤。我们提出了一种新的 FWE 初始化方法 FERNET，它使用单壳扩散 MRI 数据，改善了对水肿和浸润性肿瘤周围区域的自由水估计。该方法已在模拟数据和健康数据集上进行了广泛研究。此外，它已应用于从脑肿瘤患者获得的临床采集数据，以对肿瘤周围区域进行特征描述并改善其中的轨迹。
