Barnes Josephine, Ourselin Sebastien, Fox Nick C
Dementia Research Centre, UCL Institute of Neurology, London, United Kingdom.
Hippocampus. 2009 Jun;19(6):510-6. doi: 10.1002/hipo.20617.
Hippocampal atrophy is a characteristic and early feature of Alzheimer's disease. Volumetry of the hippocampus using T1-weighted magnetic resonance imaging (MRI) has been used not only to assess hippocampal involvement in different neurodegenerative diseases as a potential diagnostic biomarker, but also to understand the natural history of diseases, and to track changes in volume over time. Assessing change in structure circumvents issues surrounding interindividual variability and allows assessment of disease progression. Disease-modifying effects of putative therapies are important to assess in clinical trials and are difficult using clinical scales. As a result, there is increasing use of serial MRI in trials to detect potential slowing of atrophy rates as an outcome measure. Automated and yet reliable methods of quantifying such change in the hippocampus would therefore be very valuable. Algorithms capable of measuring such changes automatically have been developed and may be applicable to predict decline to a diagnosis of dementia in the future. This article details the progress in using MRI to understand hippocampal changes in the degenerative dementias and also describes attempts to automate hippocampal segmentation in these diseases.
海马萎缩是阿尔茨海默病的一个典型早期特征。利用T1加权磁共振成像（MRI）对海马进行体积测量，不仅用于评估海马在不同神经退行性疾病中的受累情况，作为一种潜在的诊断生物标志物，还用于了解疾病的自然史，以及追踪体积随时间的变化。评估结构变化可规避个体间变异性问题，并有助于评估疾病进展。在临床试验中，评估假定疗法的疾病修饰作用很重要，但使用临床量表却很难做到。因此，在试验中越来越多地使用序列MRI来检测萎缩率的潜在减缓，作为一项结局指标。因此，能够自动且可靠地量化海马这种变化的方法将非常有价值。已经开发出能够自动测量此类变化的算法，这些算法可能适用于预测未来发展为痴呆症的情况。本文详细介绍了利用MRI了解退行性痴呆中海马变化的进展情况，还描述了在这些疾病中实现海马分割自动化的尝试。
