期刊
NEUROIMAGE
卷 200, 期 -, 页码 391-404出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2019.06.039
关键词
Diffusion weighted imaging; Rician bias; Random matrix denoising; Optimal shrinkage; Asymptotic risk
资金
- European Research Council under the European Union's Seventh Framework Programme (FP7/20072013/ERC grant) [319456]
- Wellcome/EPSRC Centre for Medical Engineering at King's College London [WT 203148/Z/16/Z]
- Medical Research Council [MR/K006355/1]
- National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust and King's College London
- MRC [MR/K006355/1] Funding Source: UKRI
We propose a patch-based singular value shrinkage method for diffusion magnetic resonance image estimation targeted at low signal to noise ratio and accelerated acquisitions. It operates on the complex data resulting from a sensitivity encoding reconstruction, where asymptotically optimal signal recovery guarantees can be attained by modeling the noise propagation in the reconstruction and subsequently simulating or calculating the limit singular value spectrum. Simple strategies are presented to deal with phase inconsistencies and optimize patch construction. The pertinence of our contributions is quantitatively validated on synthetic data, an in vivo adult example, and challenging neonatal and fetal cohorts. Our methodology is compared with related approaches, which generally operate on magnitude-only data and use data-based noise level estimation and singular value truncation. Visual examples are provided to illustrate effectiveness in generating denoised and debiased diffusion estimates with well preserved spatial and diffusion detail.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据