Tirunagari Santosh, Poh Norman, Wells Kevin, Bober Miroslaw, Gorden Isky, Windridge David
1Department of Computer Science, University of Surrey, Guildford, Surrey GU2 7XH UK.
2Center for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford, Surrey GU2 7XH UK.
Mach Vis Appl. 2017;28(3):393-407. doi: 10.1007/s00138-017-0835-5. Epub 2017 Apr 6.
Images of the kidneys using dynamic contrast-enhanced magnetic resonance renography (DCE-MRR) contains unwanted complex organ motion due to respiration. This gives rise to motion artefacts that hinder the clinical assessment of kidney function. However, due to the rapid change in contrast agent within the DCE-MR image sequence, commonly used intensity-based image registration techniques are likely to fail. While semi-automated approaches involving human experts are a possible alternative, they pose significant drawbacks including inter-observer variability, and the bottleneck introduced through manual inspection of the multiplicity of images produced during a DCE-MRR study. To address this issue, we present a novel automated, registration-free movement correction approach based on windowed and reconstruction variants of dynamic mode decomposition (WR-DMD). Our proposed method is validated on ten different healthy volunteers' kidney DCE-MRI data sets. The results, using block-matching-block evaluation on the image sequence produced by WR-DMD, show the elimination of of mean motion magnitude when compared to the original data sets, thereby demonstrating the viability of automatic movement correction using WR-DMD.
使用动态对比增强磁共振肾造影（DCE-MRR）对肾脏进行成像时，由于呼吸作用会包含不必要的复杂器官运动。这会产生运动伪影，妨碍对肾功能的临床评估。然而，由于DCE-MR图像序列中造影剂的快速变化，常用的基于强度的图像配准技术很可能会失效。虽然涉及人类专家的半自动方法是一种可能的替代方案，但它们存在显著缺点，包括观察者间的变异性，以及通过手动检查DCE-MRR研究期间产生的大量图像所引入的瓶颈。为了解决这个问题，我们提出了一种基于动态模式分解的加窗和重建变体（WR-DMD）的新型自动、无需配准的运动校正方法。我们提出的方法在十个不同健康志愿者的肾脏DCE-MRI数据集上得到了验证。使用WR-DMD产生的图像序列进行块匹配块评估的结果表明，与原始数据集相比，平均运动幅度消除了，从而证明了使用WR-DMD进行自动运动校正具有可行性。
