Post-operative cerebellar mutism syndrome (POPCMS) in children is a post- surgical complication which occurs following the resection of tumors within the brain stem and cerebellum. High resolution brain magnetic resonance (MR) images acquired at multiple time points across a patient’s treatment allow the quantification of localized changes caused by the progression of this syndrome. However, MR images are not necessarily acquired at regular intervals throughout treatment and are often not volumetric. This restricts the analysis to 2D space and causes difficulty in intra- and inter-subject comparison. To address these challenges, we have developed an automated image processing and analysis pipeline. Multi-slice 2D MR image slices are interpolated in space and time to produce a 4D volumetric MR image dataset providing a longitudinal representation of the cerebellum and brain stem at specific time points across treatment. The deformations within the brain over time are represented using a novel metric known as the Jacobian of deformations determinant. This metric, together with the changing grey-level intensity of areas within the brain over time, are analyzed using machine learning techniques in order to identify biomarkers that correspond with the development of POPCMS following tumor resection. This study makes use of a fully automated approach which is not hypothesis-driven. As a result, we were able to automatically detect six potential biomarkers that are related to the development of POPCMS following tumor resection in the posterior fossa.

中文翻译：

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使用纵向小儿MRI全自动识别手术后小脑畸形综合症的成像生物标记物。

儿童术后小脑mutism综合征（POPCMS）是一种外科手术后并发症，发生在脑干和小脑内的肿瘤切除术后。在患者治疗的多个时间点获取的高分辨率脑磁共振（MR）图像，可以量化由该综合征的进展引起的局部变化。但是，在整个治疗过程中不一定必须定期获取MR图像，而且通常不是容积图像。这将分析限制在2D空间中，并导致对象内和对象间比较的困难。为了应对这些挑战，我们开发了自动图像处理和分析管道。在空间和时间上对多层2D MR图像切片进行插值，以生成4D体积MR图像数据集，该数据集提供了整个治疗过程中特定时间点的小脑和脑干的纵向表示。大脑内随时间的变形使用称为变形决定因素的雅可比矩阵的新颖度量来表示。使用机器学习技术分析该指标以及大脑内区域随时间变化的灰度强度，以识别与肿瘤切除后POPCMS发生发展相对应的生物标志物。这项研究利用了一种不受假设驱动的全自动方法。结果是，