Department of Biomedical Engineering, Washington University, St. Louis, Missouri, USA.
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA.
Med Phys. 2022 Aug;49(8):5121-5137. doi: 10.1002/mp.15778. Epub 2022 Jun 29.
Quantitative measures of dopamine transporter (DaT) uptake in caudate, putamen, and globus pallidus (GP) derived from dopamine transporter-single-photon emission computed tomography (DaT-SPECT) images have potential as biomarkers for measuring the severity of Parkinson's disease. Reliable quantification of this uptake requires accurate segmentation of the considered regions. However, segmentation of these regions from DaT-SPECT images is challenging, a major reason being partial-volume effects (PVEs) in SPECT. The PVEs arise from two sources, namely the limited system resolution and reconstruction of images over finite-sized voxel grids. The limited system resolution results in blurred boundaries of the different regions. The finite voxel size leads to TFEs, that is, voxels contain a mixture of regions. Thus, there is an important need for methods that can account for the PVEs, including the TFEs, and accurately segment the caudate, putamen, and GP, from DaT-SPECT images.
Design and objectively evaluate a fully automated tissue-fraction estimation-based segmentation method that segments the caudate, putamen, and GP from DaT-SPECT images.
The proposed method estimates the posterior mean of the fractional volumes occupied by the caudate, putamen, and GP within each voxel of a three-dimensional DaT-SPECT image. The estimate is obtained by minimizing a cost function based on the binary cross-entropy loss between the true and estimated fractional volumes over a population of SPECT images, where the distribution of true fractional volumes is obtained from existing populations of clinical magnetic resonance images. The method is implemented using a supervised deep-learning-based approach.
Evaluations using clinically guided highly realistic simulation studies show that the proposed method accurately segmented the caudate, putamen, and GP with high mean Dice similarity coefficients of ∼ 0.80 and significantly outperformed ( ) all other considered segmentation methods. Further, an objective evaluation of the proposed method on the task of quantifying regional uptake shows that the method yielded reliable quantification with low ensemble normalized root mean square error (NRMSE) < 20% for all the considered regions. In particular, the method yielded an even lower ensemble NRMSE of ∼ 10% for the caudate and putamen.
The proposed tissue-fraction estimation-based segmentation method for DaT-SPECT images demonstrated the ability to accurately segment the caudate, putamen, and GP, and reliably quantify the uptake within these regions. The results motivate further evaluation of the method with physical-phantom and patient studies.
纹状体（尾状核、壳核）和苍白球（GP）中的多巴胺转运体（DaT）摄取的定量测量来源于多巴胺转运体单光子发射计算机断层扫描（DaT-SPECT）图像，它们有可能作为测量帕金森病严重程度的生物标志物。这种摄取的可靠定量需要对所考虑的区域进行准确的分割。然而，从 DaT-SPECT 图像中分割这些区域具有挑战性，主要原因是 SPECT 中的部分容积效应（PVE）。这些 PVE 来自两个来源，即系统分辨率有限和在有限大小的体素网格上重建图像。系统分辨率有限导致不同区域的边界模糊。有限的体素大小导致 TFEs，即体素包含区域的混合物。因此，非常需要能够考虑到 PVE（包括 TFEs）并从 DaT-SPECT 图像中准确分割尾状核、壳核和 GP 的方法。
设计并客观评估一种完全自动化的基于组织分数估计的分割方法，用于从 DaT-SPECT 图像中分割尾状核、壳核和 GP。
所提出的方法估计三维 DaT-SPECT 图像中每个体素内由尾状核、壳核和 GP 占据的分数体积的后验均值。该估计是通过最小化基于二进制交叉熵损失的成本函数获得的，该函数在 SPECT 图像的总体中，在真实分数体积和估计分数体积之间进行比较，其中真实分数体积的分布是从现有的临床磁共振图像总体中获得的。该方法使用基于监督深度学习的方法实现。
使用临床指导的高度逼真的模拟研究进行评估表明，所提出的方法能够准确地分割尾状核、壳核和 GP，平均 Dice 相似系数约为 0.80，显著优于（ ）所有其他考虑的分割方法。此外，在量化区域摄取的任务中对所提出的方法进行客观评估表明，该方法产生了可靠的定量结果，所有考虑的区域的整体归一化均方根误差（NRMSE）<20%。特别是，该方法对尾状核和壳核的整体 NRMSE 约为 10%。
所提出的用于 DaT-SPECT 图像的基于组织分数估计的分割方法证明了准确分割尾状核、壳核和 GP 的能力，并能够可靠地量化这些区域内的摄取量。结果激励进一步使用物理体模和患者研究评估该方法。
