Respiratory motion correction in dynamic MRI using robust data decomposition registration - Application to DCE-MRI
Respiratory motion correction in dynamic MRI using robust data decomposition registration - Application to DCE-MRI
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DOI：
10.1016/j.media.2013.10.016
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发表时间：
2014-02-01
影响因子：
10.9
通讯作者：
Atkinson, David
中科院分区：
文献类型：
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作者：
Hamy, Valentin;Dikaios, Nikolaos;Atkinson, David
动态对比增强（DCE-） MRI的运动校正具有挑战性，因为快速的强度变化会危及常用的（基于强度的）配准算法。本文提出了一种新的基于鲁棒主成分分析（RPCA）的配准技术，将给定的时间序列分解为低秩和稀疏分量。这使得可以从保持不变的强度变化中分离出可以注册的运动组件。这种鲁棒数据分解配准（rdr）在模拟和广泛的临床数据上得到了证明。在采集过程中，对不同类型的运动和呼吸选择的稳健性被证明适用于各种成像器官，包括肝脏、小肠和前列腺。临床相关感兴趣区域的分析显示，组织时间-强度曲线的误差降低（注册后减少15-62%），曲线下区域（AUC(60)）在早期增强时得到改善。(C) 2013年作者。这是一篇基于CC by - nc - nd许可的开放获取文章
Motion correction in Dynamic Contrast Enhanced (DCE-) MRI is challenging because rapid intensity changes can compromise common (intensity based) registration algorithms. In this study we introduce a novel registration technique based on robust principal component analysis (RPCA) to decompose a given time-series into a low rank and a sparse component. This allows robust separation of motion components that can be registered, from intensity variations that are left unchanged. This Robust Data Decomposition Registration (RDDR) is demonstrated on both simulated and a wide range of clinical data. Robustness to different types of motion and breathing choices during acquisition is demonstrated for a variety of imaged organs including liver, small bowel and prostate. The analysis of clinically relevant regions of interest showed both a decrease of error (15-62% reduction following registration) in tissue time-intensity curves and improved areas under the curve (AUC(60)) at early enhancement. (C) 2013 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license