计算机科学
磁共振弥散成像
人工智能
深度学习
图像处理
神经影像学
人工神经网络
峰度
数据处理
磁共振成像
机器学习
模式识别(心理学)
医学
放射科
数学
图像(数学)
精神科
操作系统
统计
作者
Vladimir Golkov,Alexey Dosovitskiy,Jonathan I. Sperl,Marion I. Menzel,Michael Czisch,Philipp G. Sämann,Thomas Brox,Daniel Cremers
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2016-04-06
卷期号:35 (5): 1344-1351
被引量:203
标识
DOI:10.1109/tmi.2016.2551324
摘要
Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines. An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive microstructure assessment method with a prominent application in neuroimaging. Advanced diffusion models providing accurate microstructural characterization so far have required long acquisition times and thus have been inapplicable for children and adults who are uncooperative, uncomfortable, or unwell. We show that the long scan time requirements are mainly due to disadvantages of classical data processing. We demonstrate how deep learning, a group of algorithms based on recent advances in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This modification allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models. We set a new state of the art by estimating diffusion kurtosis measures from only 12 data points and neurite orientation dispersion and density measures from only 8 data points. This allows unprecedentedly fast and robust protocols facilitating clinical routine and demonstrates how classical data processing can be streamlined by means of deep learning.
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