CSIRO, The Australian e-Health Research Centre, Herston, Queensland 4029, Australia.
Calvary Mater Newcastle Hospital, Newcastle, New South Wales 2298, Australia; University of Newcastle, Newcastle, New South Wales 2308, Australia.
Med Image Anal. 2015 Jul;23(1):56-69. doi: 10.1016/j.media.2015.04.014. Epub 2015 Apr 24.
CT-MR registration is a critical component of many radiation oncology protocols. In prostate external beam radiation therapy, it allows the propagation of MR-derived contours to reference CT images at the planning stage, and it enables dose mapping during dosimetry studies. The use of carefully registered CT-MR atlases allows the estimation of patient specific electron density maps from MRI scans, enabling MRI-alone radiation therapy planning and treatment adaptation. In all cases, the precision and accuracy achieved by registration influences the quality of the entire process.
Most current registration algorithms do not robustly generalize and lack inverse-consistency, increasing the risk of human error and acting as a source of bias in studies where information is propagated in a particular direction, e.g. CT to MR or vice versa. In MRI-based treatment planning where both CT and MR scans serve as spatial references, inverse-consistency is critical, if under-acknowledged.
A robust, inverse-consistent, rigid/affine registration algorithm that is well suited to CT-MR alignment in prostate radiation therapy is presented.
The presented method is based on a robust block-matching optimization process that utilises a half-way space definition to maintain inverse-consistency. Inverse-consistency substantially reduces the influence of the order of input images, simplifying analysis, and increasing robustness. An open source implementation is available online at http://aehrc.github.io/Mirorr/.
Experimental results on a challenging 35 CT-MR pelvis dataset demonstrate that the proposed method is more accurate than other popular registration packages and is at least as accurate as the state of the art, while being more robust and having an order of magnitude higher inverse-consistency than competing approaches.
The presented results demonstrate that the proposed registration algorithm is readily applicable to prostate radiation therapy planning.
CT-MR 配准是许多放射肿瘤学方案的关键组成部分。在前列腺外束放射治疗中，它允许在规划阶段将 MR 衍生的轮廓传播到参考 CT 图像，并在剂量学研究期间进行剂量映射。使用精心注册的 CT-MR 图谱，可以从 MRI 扫描中估计患者特定的电子密度图，从而实现仅使用 MRI 的放射治疗计划和治疗适应。在所有情况下，配准的精度和准确性都会影响整个过程的质量。
大多数当前的配准算法不能稳健地推广，缺乏反向一致性，增加了人为错误的风险，并成为在特定方向传播信息的研究中的一个偏差源，例如从 CT 到 MR 或反之亦然。在基于 MRI 的治疗计划中，CT 和 MR 扫描都作为空间参考，反向一致性至关重要，如果没有得到充分的认识。
提出了一种稳健、反向一致、刚性/仿射配准算法，非常适合前列腺放射治疗中的 CT-MR 配准。
所提出的方法基于稳健的块匹配优化过程，利用半空间定义来保持反向一致性。反向一致性大大降低了输入图像顺序的影响，简化了分析，并提高了鲁棒性。一个开源实现可在 http://aehrc.github.io/Mirorr/ 上获得。
在具有挑战性的 35 个 CT-MR 骨盆数据集上的实验结果表明，所提出的方法比其他流行的配准包更准确，至少与最先进的方法一样准确，同时具有更高的鲁棒性和比竞争方法高一个数量级的反向一致性。
所提出的结果表明，所提出的配准算法易于应用于前列腺放射治疗计划。
Zotero 插件