Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy.
Deformable registration for quantifying longitudinal tumor changes during neoadjuvant chemotherapy.
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DOI：
10.1002/mrm.25368
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发表时间：
2015-06
影响因子：
3.3
通讯作者：
Kontos D
中科院分区：
文献类型：
--
作者：
Ou Y;Weinstein SP;Conant EF;Englander S;Da X;Gaonkar B;Hsieh MK;Rosen M;DeMichele A;Davatzikos C;Kontos D
对比其他基于强度的算法，评估基于属性的可变形配准算法DRAMMS在乳腺MRI纵向配准中的应用，并展示其在新辅助化疗过程中量化肿瘤变化的适用性。分析了14例接受新辅助化疗的妇女的乳房磁共振图像。基于两位专家独立标注的2,380个地标，对整个图像体积、不同图像子区域和患者亚组，对DRAMMS与五种基于强度的可变形配准方法的准确性进行了评估。采用标记误差最小的配准方法，通过计算配准形变的雅可比行列式映射来量化肿瘤的变化。标记误差最小（6.05±4.86 mm），其次是基于强度的CC-FFD（8.07±3.86 mm）、NMI-FFD（8.21±3.81 mm）、SSD-FFD（9.46±4.55 mm）、Demons（10.76±6.01 mm）和Diffeomorphic Demons（10.82±6.11 mm）。结果表明，配准精度还取决于肿瘤与正常组织区域和不同的患者亚组。基于属性匹配和相互显著性驱动的DRAMMS形变配准方法，可以比本文几种强度匹配方法具有更高的胸部纵向磁共振图像配准精度。因此，它可以更准确地量化异质肿瘤变化，作为对治疗反应的标志。
To evaluate DRAMMS, an attribute-based deformable registration algorithm, compared to other intensity-based algorithms, for longitudinal breast MRI registration, and to show its applicability in quantifying tumor changes over the course of neoadjuvant chemotherapy. Breast magnetic resonance images from 14 women undergoing neoadjuvant chemotherapy were analyzed. The accuracy of DRAMMS versus five intensity-based deformable registration methods was evaluated based on 2,380 landmarks independently annotated by two experts, for the entire image volume, different image subregions, and patient subgroups. The registration method with the smallest landmark error was used to quantify tumor changes, by calculating the Jacobian determinant maps of the registration deformation. DRAMMS had the smallest landmark errors (6.05 ± 4.86 mm), followed by the intensity-based methods CC-FFD (8.07 ± 3.86 mm), NMI-FFD (8.21 ± 3.81 mm), SSD-FFD (9.46 ± 4.55 mm), Demons (10.76 ± 6.01 mm), and Diffeomorphic Demons (10.82 ± 6.11 mm). Results show that registration accuracy also depends on tumor versus normal tissue regions and different patient subgroups. The DRAMMS deformable registration method, driven by attribute-matching and mutual-saliency, can register longitudinal breast magnetic resonance images with a higher accuracy than several intensity-matching methods included in this article. As such, it could be valuable for more accurately quantifying heterogeneous tumor changes as a marker of response to treatment.