Carass Aaron, Greenman Danielle, Dewey Blake E, Calabresi Peter A, Prince Jerry L, Pham Dzung L
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218, USA.
Center for Neuroscience and Regenerative Medicine, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20817, USA.
Neuroimage Rep. 2024 Mar;4(1). doi: 10.1016/j.ynirp.2024.100195. Epub 2024 Feb 2.
Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.
由于扫描仪硬件以及用于获取图像的脉冲序列存在差异，临床磁共振成像（MRI）缺乏标准强度尺度。当MRI用于定量分析时，比如在评估多发性硬化症中的白质病变（WML）时，这种强度标准化的缺失就成为了一个关键问题，影响着疾病的分期、跟踪以及治疗。本文介绍了一项关于WML分割一致性协调的研究，该研究使用一种目标检测分类方案进行评估，该方案结合了原始MRI和协调后MRI的手动描绘。研究了在两个不同成像平台上扫描的十人的队列。一位对图像来源不知情的专家评分者在协调前后分别在两台扫描仪的图像上手动描绘WML。研究发现，协调后在WML的总体积和病变体积以及空间分布方面都有更紧密的一致性，这表明在创建手动描绘之前进行图像协调的重要性。这些结果可能会在自动病变分割算法的开发和评估中产生更好的真值模型。
