Elliott Colm, Francis Simon J, Arnold Douglas L, Collins D Louis, Arbel Tal
Centre for Intelligent Machines, McGill University, Canada.
Med Image Comput Comput Assist Interv. 2010;13(Pt 2):290-7. doi: 10.1007/978-3-642-15745-5_36.
Accurate and precise identification of multiple sclerosis (MS) lesions in longitudinal MRI is important for monitoring disease progression and for assessing treatment effects. We present a probabilistic framework to automatically detect new, enlarging and resolving lesions in longitudinal scans of MS patients based on multimodal subtraction magnetic resonance (MR) images. Our Bayesian framework overcomes registration artifact by explicitly modeling the variability in the difference images, the tissue transitions, and the neighbourhood classes in the form of likelihoods, and by embedding a classification of a reference scan as a prior. Our method was evaluated on (a) a scan-rescan data set consisting of 3 MS patients and (b) a multicenter clinical data set consisting of 212 scans from 89 RRMS (relapsing-remitting MS) patients. The proposed method is shown to identify MS lesions in longitudinal MRI with a high degree of precision while remaining sensitive to lesion activity.
在纵向磁共振成像（MRI）中准确、精确地识别多发性硬化症（MS）病变对于监测疾病进展和评估治疗效果至关重要。我们提出了一个概率框架，用于基于多模态减法磁共振（MR）图像自动检测MS患者纵向扫描中的新病变、扩大病变和消退病变。我们的贝叶斯框架通过以似然性的形式明确建模差异图像中的变异性、组织转变和邻域类别，并通过将参考扫描的分类作为先验嵌入，克服了配准伪影。我们的方法在（a）由3名MS患者组成的重扫数据集和（b）由89名复发缓解型MS（RRMS）患者的212次扫描组成的多中心临床数据集上进行了评估。结果表明，所提出的方法能够在纵向MRI中高精度地识别MS病变，同时对病变活动保持敏感。
