Centre for Medical Imaging, University College London, 250 Euston Road, NW1 2PG London, UK.
Centre for Medical Imaging, University College London, 250 Euston Road, NW1 2PG London, UK.
Med Image Anal. 2014 Feb;18(2):301-13. doi: 10.1016/j.media.2013.10.016. Epub 2013 Nov 18.
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 (AUC60) at early enhancement.
运动校正在动态对比增强磁共振成像（DCE-MRI）中是一项具有挑战性的任务，因为快速的强度变化可能会影响常见的（基于强度的）配准算法。在这项研究中，我们引入了一种基于鲁棒主成分分析（RPCA）的新的配准技术，该技术可以将给定的时间序列分解为低秩分量和稀疏分量。这使得能够稳健地区分可以配准的运动分量，以及保持不变的强度变化。这项鲁棒数据分解配准（RDDR）技术在模拟数据和广泛的临床数据上进行了验证。针对不同类型的运动和采集过程中的呼吸选择，对包括肝脏、小肠和前列腺在内的各种成像器官进行了稳健性测试。对临床相关感兴趣区域的分析表明，组织时间-强度曲线的误差（注册后减少 15-62%）和早期增强时曲线下面积（AUC60）都有所改善。
