Verification of a method to detect glass microspheres via micro-CT.
Verification of a method to detect glass microspheres via micro-CT.
复制标题
验证通过显微 CT 检测玻璃微球的方法。
DOI：
10.1002/mp.13874
复制
发表时间：
2019
期刊：
影响因子：
3.8
通讯作者：
Frey,EricC
中科院分区：
文献类型：
--
作者：
Crookston,NathanR;Pasciak,AlexanderS;Abiola,Godwin;Donahue,Danielle;Weiss,CliffordR;Frey,EricC
引言确定90 Y放射性栓塞中玻璃微球微观分布的能力在治疗后微剂量测定和聚类分析中具有重要应用。目前的方法是时间密集型和劳动密集型的，因此通常只适用于小样本。材料和方法一个高分辨率micro-CT图像与体素尺寸为8.74 µm的体模包含约25 µm直径的玻璃微球嵌入在组织等效材料是光学透明的，这使得真正的微球的位置被确定使用透射光显微镜。开发了一种3阶段算法来估计组织区域中微球的数量和位置。这些阶段是对CT图像进行阈值处理并丢弃具有不足体素的区域，使用检测到的和相邻区域体素的值来估计每个区域中的微球的数量，以及使用前两个阶段的输出来估计每个微球的位置。两种不同的方法来估计在每个区域中的微球的数量，是五种方法定位微球。计算每个阶段的平均值，并将根据微球位置创建的真实和估计剂量图的72 µm体素剂量之间的平均绝对误差（MAE）用作整体算法性能的品质因数。在光学显微照片中识别的微球位置被用作所有阶段度量的金标准。该方法的实用性，然后证明使用标本从人类神经内分泌肿瘤（NET）与glass 90 Y microspheres.ResultsThe阶段检测区域包含微球发现100%的区域内的微球。没有微球的错误检测区域的数量占区域总数的1.5%。在第2阶段，使用这些参数，每个区域中近94%的实际球体数量被正确计数，每个区域中只有5%的估计球体数量是假阳性。真实剂量图与使用具有最佳参数和方法选择的完整算法估计的剂量图之间的MAE为4.2%。共5，713个玻璃微球被确定为在NET标本中不均匀分布，最大肿瘤剂量>2500戈伊，46%的标本接受<20戈伊.ConclusionsThis work developed and evaluated a method to detect and estimate the three‐dimensional locations of glass microspheres in whole tissue samples that requires less manual efforts than traditional methods.该方法可用于获得对微球分布的异质性的重要见解，这将有助于改善放射性栓塞治疗计划。
IntroductionThe ability to determine the microscopic distribution of glass microspheres in90Y radioembolization has important applications in post‐treatment microdosimetry and cluster analysis. Current methods are time‐intensive and labor‐intensive and thus are typically only applied to small samples.Materials and methodsA high‐resolution micro‐CT image with a voxel size of 8.74 µm was acquired of phantoms containing ~25 µm‐diameter glass microspheres embedded in tissue‐equivalent materials that were optically transparent, which allowed true microsphere locations to be determined using transmission light microscopy. A 3‐stage algorithm was developed to estimate the number and locations of microspheres in tissue regions. The stages are thresholding the CT image and discarding regions with insufficient voxels, estimating the number of microspheres in each region using the values of the detected and neighboring region voxels and estimating locations for each microsphere using the outputs of the previous two stages. Two different methods for estimating the number of microspheres in each region were derived, as were five methods for localizing microspheres. Metrics for each stage were computed, and the mean absolute error (MAE) between the dose to 72 µm voxels of the true and estimated dose maps created from the microsphere locations was used as the figure of merit for overall algorithm performance. Microsphere locations identified in the optical micrograph were used as the gold standard for the metrics of all stages. The method’s utility was then demonstrated using a specimen from a human neuroendocrine tumor (NET) treated with glass90Y microspheres.ResultsThe stage detecting regions containing microspheres found 100% of microspheres inside regions. The number of incorrectly detected regions without microspheres was 1.5% of the total number of regions. In stage 2, with these parameters, nearly 94% of the actual number of spheres in each region was correctly counted, and only 5% of the estimated sphere quantities in each region were false positives. The MAE between the true dose maps and dose maps estimated using the full algorithm with optimal parameter and method choices was 4.2%. A total of 5,713 glass microspheres were identified as being distributed heterogeneously in the NET specimen with a maximum tumor dose of >2500 Gy and 46% of the specimen receiving <20 Gy.ConclusionsThis work developed and evaluated a method to detect and estimate the three‐dimensional locations of glass microspheres in whole tissue samples that require less manual effort than traditional methods. This method could be used to gain important insights into the heterogeneity of microsphere distributions that would be useful for improving radioembolization treatment planning.