神经影像学
冠状面
磁共振成像
脑形态计量学
人脑
高分辨率
分割
方向(向量空间)
矢状面
医学
分辨率(逻辑)
神经科学
计算机科学
人工智能
放射科
心理学
遥感
地质学
数学
几何学
作者
Juan Eugenio Iglesias,Benjamin Billot,Yaël Balbastre,Colin Magdamo,Steven E. Arnold,Sudeshna Das,Brian L. Edlow,Daniel C. Alexander,Polina Golland,Bruce Fischl
出处
期刊:Science Advances
[American Association for the Advancement of Science (AAAS)]
日期:2023-02-01
卷期号:9 (5)
被引量:73
标识
DOI:10.1126/sciadv.add3607
摘要
Every year, millions of brain magnetic resonance imaging (MRI) scans are acquired in hospitals across the world. These have the potential to revolutionize our understanding of many neurological diseases, but their morphometric analysis has not yet been possible due to their anisotropic resolution. We present an artificial intelligence technique, "SynthSR," that takes clinical brain MRI scans with any MR contrast (T1, T2, etc.), orientation (axial/coronal/sagittal), and resolution and turns them into high-resolution T1 scans that are usable by virtually all existing human neuroimaging tools. We present results on segmentation, registration, and atlasing of >10,000 scans of controls and patients with brain tumors, strokes, and Alzheimer's disease. SynthSR yields morphometric results that are very highly correlated with what one would have obtained with high-resolution T1 scans. SynthSR allows sample sizes that have the potential to overcome the power limitations of prospective research studies and shed new light on the healthy and diseased human brain.
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