Gerig Guido, Fishbaugh James, Sadeghi Neda
Tandon School of Engineering, Department of Computer Science and Engineering, NYU, 2 MetroTech Center, 10.094, Brooklyn, NY 11201, USA.
Tandon School of Engineering, Department of Computer Science and Engineering, NYU, 2 MetroTech Center, 10.094, Brooklyn, NY 11201, USA.
Med Image Anal. 2016 Oct;33:114-121. doi: 10.1016/j.media.2016.06.014. Epub 2016 Jun 15.
Clinical assessment routinely uses terms such as development, growth trajectory, degeneration, disease progression, recovery or prediction. This terminology inherently carries the aspect of dynamic processes, suggesting that single measurements in time and cross-sectional comparison may not sufficiently describe spatiotemporal changes. In view of medical imaging, such tasks encourage subject-specific longitudinal imaging. Whereas follow-up, monitoring and prediction are natural tasks in clinical diagnosis of disease progression and of assessment of therapeutic intervention, translation of methodologies for calculation of temporal profiles from longitudinal data to clinical routine still requires significant research and development efforts. Rapid advances in image acquisition technology with significantly reduced acquisition times and with increase of patient comfort favor repeated imaging over the observation period. In view of serial imaging ranging over multiple years, image acquisition faces the challenging issue of scanner standardization and calibration which is crucial for successful spatiotemporal analysis. Longitudinal 3D data, represented as 4D images, capture time-varying anatomy and function. Such data benefits from dedicated analysis methods and tools that make use of the inherent correlation and causality of repeated acquisitions of the same subject. Availability of such data spawned progress in the development of advanced 4D image analysis methodologies that carry the notion of linear and nonlinear regression, now applied to complex, high-dimensional data such as images, image-derived shapes and structures, or a combination thereof. This paper provides examples of recently developed analysis methodologies for 4D image data, primarily focusing on progress in areas of core expertise of the authors. These include spatiotemporal shape modeling and growth trajectories of white matter fiber tracts demonstrated with examples from ongoing longitudinal clinical neuroimaging studies such as analysis of early brain growth in subjects at risk for mental illness and neurodegeneration in Huntington's disease (HD). We will discuss broader aspects of current limitations and need for future research in view of data consistency and analysis methodologies.
临床评估通常使用诸如发育、生长轨迹、退化、疾病进展、恢复或预测等术语。这种术语本身就带有动态过程的特征，这表明时间上的单次测量和横断面比较可能不足以描述时空变化。从医学成像的角度来看，此类任务需要针对个体的纵向成像。虽然随访、监测和预测是疾病进展临床诊断和治疗干预评估中的自然任务，但将从纵向数据计算时间剖面的方法转化为临床常规仍需要大量的研究和开发工作。图像采集技术的快速发展显著缩短了采集时间并提高了患者舒适度，这有利于在观察期内进行重复成像。鉴于长达数年的序列成像，图像采集面临着扫描仪标准化和校准这一具有挑战性的问题，这对于成功的时空分析至关重要。以4D图像形式呈现的纵向3D数据可捕捉随时间变化的解剖结构和功能。此类数据受益于专门的分析方法和工具，这些方法和工具利用了对同一受试者重复采集数据所固有的相关性和因果关系。此类数据的可用性推动了先进4D图像分析方法的发展，这些方法包含线性和非线性回归的概念，现在已应用于复杂的高维数据，如图像、源自图像的形状和结构或它们的组合。本文提供了一些最近开发的4D图像数据分析方法的示例，主要关注作者核心专业领域的进展。这些示例包括时空形状建模以及白质纤维束的生长轨迹，通过正在进行的纵向临床神经成像研究中的实例进行展示，例如对有精神疾病风险的受试者早期大脑发育以及亨廷顿舞蹈症（HD）神经退行性变的分析。我们将从数据一致性和分析方法的角度讨论当前局限性的更广泛方面以及未来研究的需求。
