Motion Correction in Low SNR MRI Using an Approximate Rician Log-Likelihood
Motion Correction in Low SNR MRI Using an Approximate Rician Log-Likelihood
复制标题
使用近似莱斯对数似然在低 SNR MRI 中进行运动校正
DOI：
10.1007/978-3-031-11203-4_16
复制
发表时间：
2022
影响因子：
3.9
通讯作者：
M. Cercignani
中科院分区：
文献类型：
--
作者：
Ivor J. A. Simpson;Balázs Örzsik;N. Harrison;I. Asllani;M. Cercignani
某些MRI采集（如钠成像）产生的数据信噪比（SNR）非常低，有意义的分析可能需要对多个图像进行平均。由于数据包含大量噪声，使用标准配准工具的运动校正可能无效。本文采用了一个简单的生成模型的数据，其中的误差被描述为以下的Rician分布，更准确地表征了图像噪声。最大后验推理是通过对Rician对数似然函数的可微近似来实现的。我们发现，这种方法大大优于高斯对数似然基线的合成数据，已被破坏的不同程度的莱斯噪声。我们展示了我们的方法对真实的钠MRI数据的结果，并证明我们可以减少大量运动的影响。
Some MRI acquisitions, such as Sodium imaging, produce data with very low signal-to-noise ratio (SNR) and meaningful analysis may require several images to be averaged. As the data contains substantial noise, motion correction using standard registration tools may not be effective. This paper employs a simple generative model for the data, where the error is described as following a Rician distribution, which more accurately characterised the image noise. Maximum a posteriori inference is enabled by a differentiable approximation to the Rician log-likelihood function. We find that this approach substantially outperforms a Gaussian log-likelihood baseline on synthetic data that has been corrupted by Rician noise of varying degrees. We show results of our approach on real Sodium MRI data, and demonstrate that we can reduces the effects of substantial motion.