Automated vessel exclusion technique for quantitative assessment of hepatic iron overload by R2*-MRI.
Automated vessel exclusion technique for quantitative assessment of hepatic iron overload by R2*-MRI.
复制标题
通过 R2*-MRI 定量评估肝铁过载的自动血管排除技术。
DOI：
10.1002/jmri.25880
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Hillenbrand,ClaudiaM
中科院分区：
文献类型：
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作者：
Tipirneni-Sajja,Aaryani;Song,Ruitian;McCarville,MBeth;Loeffler,RalfB;Hankins,JaneS;Hillenbrand,ClaudiaM
背景肝实质提取是评估肝脏铁含量（HIC）的重要步骤。传统上，这是由放射科医生通过全肝轮廓和阈值化来排除肝血管。然而，血管排除过程是迭代的，耗时的，并且易受审稿人间变异性的影响。目的实施和评估自动肝血管排除和实质提取技术，以准确评估基于HIC的HIC。研究类型临床数据的回顾性分析。受试者分析了对257名患者进行的511次MRI检查的数据。场强/序列所有患者均在1.5T扫描仪上使用多回波梯度回波序列进行扫描，用于临床监测HIC。评估一种基于多尺度血管增强滤波器的自动化方法被研究用于三种输入数据类型-对比度优化的合成图像， map和 映射到分割血管并提取肝脏组织以进行基于HIC的评估。分割和 统计检验Dice相似系数用于比较提取的实质瘤之间的分割结果，线性回归和Bland-Altman分析用于比较 结果，获得与自动化和参考技术。结果平均肝脏 所有三种基于滤波器的方法的估计值与参考方法显示出极好的一致性（斜率1.04-1.05，R2> 0.99，P < 0.001）。使用参考方法和自动化方法提取的薄壁组织面积的平均重叠面积为87- 88%。阈值技术包括血管/组织边界处的小血管和像素作为实质区域，可能导致小偏倚（<5%）， 值相比，自动化的方法。Data ConclusionThe优秀的协议之间的参考和自动肝血管分割方法证实了所提出的方法的准确性和鲁棒性。这种自动化的方法可能会改善放射科医生的工作流程，减少解释时间和操作员的依赖性，以评估HIC，一个重要的临床参数，指导铁过载management.Level的证据：3技术有效性：阶段2 J。Magn. Reson.成像2018;47：1542-1551。
BackgroundExtraction of liver parenchyma is an important step in the evaluation of ‐based hepatic iron content (HIC). Traditionally, this is performed by radiologists via whole‐liver contouring and ‐thresholding to exclude hepatic vessels. However, the vessel exclusion process is iterative, time‐consuming, and susceptible to interreviewer variability.PurposeTo implement and evaluate an automatic hepatic vessel exclusion and parenchyma extraction technique for accurate assessment of ‐based HIC.Study TypeRetrospective analysis of clinical data.SubjectsData from 511 MRI exams performed on 257 patients were analyzed.Field Strength/SequenceAll patients were scanned on a 1.5T scanner using a multiecho gradient echo sequence for clinical monitoring of HIC.AssessmentAn automated method based on a multiscale vessel enhancement filter was investigated for three input data types—contrast‐optimized composite image, map, and map—to segment blood vessels and extract liver tissue for ‐based HIC assessment. Segmentation and results obtained using this automated technique were compared with those from a reference ‐thresholding technique performed by a radiologist.Statistical TestsThe Dice similarity coefficient was used to compare the segmentation results between the extracted parenchymas, and linear regression and Bland‐Altman analyses were performed to compare the results, obtained with the automated and reference techniques.ResultsMean liver values estimated from all three filter‐based methods showed excellent agreement with the reference method (slopes 1.04–1.05, R2> 0.99,P< 0.001). Parenchyma areas extracted using the reference and automated methods had an average overlap area of 87–88%. The ‐thresholding technique included small vessels and pixels at the vessel/tissue boundaries as parenchymal area, potentially causing a small bias (<5%) in values compared to the automated method.Data ConclusionThe excellent agreement between reference and automated hepatic vessel segmentation methods confirms the accuracy and robustness of the proposed method. This automated approach might improve the radiologist's workflow by reducing the interpretation time and operator dependence for assessing HIC, an important clinical parameter that guides iron overload management.Level of Evidence:3Technical Efficacy:Stage 2J. Magn. Reson. Imaging 2018;47:1542–1551.