Muschelli John
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
Front Neuroinform. 2019 Sep 4;13:61. doi: 10.3389/fninf.2019.00061. eCollection 2019.
Many research applications of neuroimaging use magnetic resonance imaging (MRI). As such, recommendations for image analysis and standardized imaging pipelines exist. Clinical imaging, however, relies heavily on X-ray computed tomography (CT) scans for diagnosis and prognosis. Currently, there is only one image processing pipeline for head CT, which focuses mainly on head CT data with lesions. We present tools and a complete pipeline for processing CT data, focusing on open-source solutions, that focus on head CT but are applicable to most CT analyses. We describe going from raw DICOM data to a spatially normalized brain within CT presenting a full example with code. Overall, we recommend anonymizing data with Clinical Trials Processor, converting DICOM data to NIfTI using dcm2niix, using BET for brain extraction, and registration using a publicly-available CT template for analysis.
神经成像的许多研究应用都使用磁共振成像（MRI）。因此，存在图像分析和标准化成像流程的相关建议。然而，临床成像在很大程度上依赖于X射线计算机断层扫描（CT）进行诊断和预后评估。目前，只有一个用于头部CT的图像处理流程，其主要关注有病变的头部CT数据。我们展示了用于处理CT数据的工具和完整流程，重点是开源解决方案，这些工具和流程主要针对头部CT，但也适用于大多数CT分析。我们描述了从原始DICOM数据到CT内空间归一化大脑的过程，并给出了带有代码的完整示例。总体而言，我们建议使用临床试验处理器对数据进行匿名化处理，使用dcm2niix将DICOM数据转换为NIfTI格式，使用BET进行脑提取，并使用公开可用的CT模板进行配准以进行分析。
