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通过平均衰减曲线提高肝脏 R2* 映射。

Improved Liver R2* Mapping by Averaging Decay Curves.

机构信息

Guangdong Provincial Key Laborary of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China.

School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China.

出版信息

Sci Rep. 2017 Jul 21;7(1):6158. doi: 10.1038/s41598-017-05683-5.

Abstract

Liver R2* mapping is often degraded by the low signal-to-noise ratio (SNR) especially in the presence of severe iron. This study aims to improve liver R2* mapping at low SNRs by averaging decay curves before the process of curve-fitting. Independently filtering echo images by nonlocal means (NLM) demonstrated improved quality of R2* mapping, but may introduce new errors due to the nonlinear nature of the NLM filter, during which the averaging weights may vary with different image contents at multiple echo times. In addition, the image denoising effect of the NLM may decline when no sufficient similar patches are available. To overcome these drawbacks, we proposed to filter decay curves instead of images. In this novel scheme, decay curves were averaged in a local window, each with a weight assigned according to the curve-similarity measured by the distance between one of the neighboring curves and the targeted one. The proposed method was tested on simulated, phantom and patient data. The results demonstrate that the proposed method can provide more accurate R2* mapping compared with the NLM algorithm, and hence has the potential to improve diagnosis and therapy in patients with liver iron.

摘要

肝脏 R2* 映射通常会受到低信噪比(SNR)的影响,尤其是在存在严重铁沉积的情况下。本研究旨在通过在曲线拟合之前平均衰减曲线来提高低 SNR 下的肝脏 R2* 映射质量。通过非局部均值(NLM)独立滤波回波图像,虽然 R2* 映射的质量得到了改善,但由于 NLM 滤波器的非线性性质,可能会引入新的误差,在这种情况下,平均权重可能会随多个回波时间的不同图像内容而变化。此外,当不存在足够相似的补丁时,NLM 的图像去噪效果可能会下降。为了克服这些缺点,我们提出了一种新的方法,即过滤衰减曲线而不是图像。在这种新方案中,在局部窗口中平均衰减曲线,每个曲线都根据相邻曲线之一与目标曲线之间的距离测量的曲线相似性来分配权重。在模拟、体模和患者数据上进行了实验验证。结果表明,与 NLM 算法相比,所提出的方法可以提供更准确的 R2* 映射,因此有可能改善肝脏铁沉积患者的诊断和治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9f91/5522399/f95dec0a3391/41598_2017_5683_Fig1_HTML.jpg

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