Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi-channel multi-contrast MRI acquisitions.,NMR in Biomedicine

当前位置： X-MOL 学术 › NMR Biomed. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Simultaneous use of individual and joint regularization terms in compressive sensing: Joint reconstruction of multi-channel multi-contrast MRI acquisitions.
NMR in Biomedicine ( IF 2.7 ) Pub Date : 2020-01-23 , DOI: 10.1002/nbm.4247
Emre Kopanoglu _{1,

2} , Alper Güngör _{2,

3,

4} , Toygan Kilic _{3,

4} , Emine Ulku Saritas _{3,

4,

5} , Kader K Oguz _{4,

6} , Tolga Çukur _{3,

4,

5} , H Emre Güven ₂

Affiliation

Multi-contrast images are commonly acquired together to maximize complementary diagnostic information, albeit at the expense of longer scan times. A time-efficient strategy to acquire high-quality multi-contrast images is to accelerate individual sequences and then reconstruct undersampled data with joint regularization terms that leverage common information across contrasts. However, these terms can cause features that are unique to a subset of contrasts to leak into the other contrasts. Such leakage-of-features may appear as artificial tissues, thereby misleading diagnosis. The goal of this study is to develop a compressive sensing method for multi-channel multi-contrast magnetic resonance imaging (MRI) that optimally utilizes shared information while preventing feature leakage. Joint regularization terms group sparsity and colour total variation are used to exploit common features across images while individual sparsity and total variation are also used to prevent leakage of distinct features across contrasts. The multi-channel multi-contrast reconstruction problem is solved via a fast algorithm based on Alternating Direction Method of Multipliers. The proposed method is compared against using only individual and only joint regularization terms in reconstruction. Comparisons were performed on single-channel simulated and multi-channel in-vivo datasets in terms of reconstruction quality and neuroradiologist reader scores. The proposed method demonstrates rapid convergence and improved image quality for both simulated and in-vivo datasets. Furthermore, while reconstructions that solely use joint regularization terms are prone to leakage-of-features, the proposed method reliably avoids leakage via simultaneous use of joint and individual terms, thereby holding great promise for clinical use.

中文翻译：

在压缩感测中同时使用个体和联合正则化项：多通道多对比度MRI采集的联合重建。

尽管以较长的扫描时间为代价，但通常会同时采集多对比度图像以最大化互补的诊断信息。一种获取高质量多对比度图像的省时策略是加速单个序列，然后使用联合正则化项重建欠采样数据，该正则化项利用跨对比的公共信息。但是，这些术语可能导致对比度子集特有的特征泄漏到其他对比度中。这种特征泄漏可能表现为人造组织，从而误导诊断。这项研究的目的是开发一种用于多通道多对比度磁共振成像（MRI）的压缩感测方法，该方法可以最佳地利用共享信息，同时防止特征泄漏。联合正则化术语组稀疏性和颜色总变化被用于利用图像中的共同特征，而个体稀疏性和总变化也被用于防止不同特征在对比度之间的泄漏。通过基于乘法器交替方向法的快速算法解决了多通道多对比度重建问题。将所提出的方法与在重建中仅使用单个和仅联合正则化项进行比较。在重建质量和神经放射科医生阅读者评分方面，对单通道模拟和多通道体内数据集进行了比较。拟议的方法展示了模拟和体内数据集的快速收敛和改进的图像质量。此外，

更新日期：2020-03-09

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11