Atlas-Powered Deep Learning (ADL) - Application to Diffusion Weighted MRI

被引:2
|
作者
Karimi, Davood [1 ,2 ]
Gholipour, Ali [1 ,2 ]
机构
[1] Boston Childrens Hosp, Dept Radiol, Computat Radiol Lab, Boston, MA USA
[2] Harvard Med Sch, Boston, MA USA
来源
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION, MICCAI 2022, PT I | 2022年 / 13431卷
基金
欧洲研究理事会; 美国国家卫生研究院;
关键词
Deep learning; Atlas; Estimation; Diffusion MRI; WHITE-MATTER; TENSOR; CONSTRUCTION; OPTIMIZATION;
D O I
10.1007/978-3-031-16431-6_12
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Deep learning has a great potential for estimating biomarkers in diffusion weighted magnetic resonance imaging (dMRI). Atlases, on the other hand, are a unique tool for modeling the spatio-temporal variability of biomarkers. In this paper, we propose the first framework to exploit both deep learning and atlases for biomarker estimation in dMRI. Our framework relies on non-linear diffusion tensor registration to compute biomarker atlases and to estimate atlas reliability maps. We also use non-linear tensor registration to align the atlas to a subject and to estimate the error of this alignment. We use the biomarker atlas, atlas reliability map, and alignment error map, in addition to the dMRI signal, as inputs to a deep learning model for biomarker estimation. We use our framework to estimate fractional anisotropy and neurite orientation dispersion from down-sampled dMRI data on a test cohort of 70 newborn subjects. Results show that our method significantly outperforms standard estimation methods as well as recent deep learning techniques. Our method is also more robust to higher measurement down-sampling factors. Our study shows that the advantages of deep learning and atlases can be synergistically combined to achieve unprecedented biomarker estimation accuracy in dMRI.
引用
收藏
页码:123 / 132
页数:10
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