Learning statistical correlation for fast prostate registration in image-guided radiotherapy.
Learning statistical correlation for fast prostate registration in image-guided radiotherapy.
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DOI：
10.1118/1.3641645
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发表时间：
2011-11
期刊：
影响因子：
3.8
通讯作者：
Yonghong Shi;Shu Liao;D. Shen
中科院分区：
文献类型：
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作者：
Yonghong Shi;Shu Liao;D. Shen
目的在前列腺癌的自适应放射治疗中，快速、准确地将患者的计划图像与治疗图像配准是至关重要的。利用作者最近开发的可变形表面模型，可以快速分割每个治疗图像中的前列腺边界，并自动建立它们与计划图像中前列腺边界的对应关系（或相对变形）。然而，对于将在计划图像空间中设计的治疗计划变换到每个治疗图像空间而言特别重要的非边界区域上的密集对应仍然没有解决。本文提出了一种新的方法来学习前列腺边界和非边界区域的变形之间的统计相关性，快速估计的非边界区域的变形时，给定的前列腺边界的变形在一个新的治疗图像。方法该方法的主要贡献在于以下几个方面。首先，将从当前患者和其他训练患者中学习统计变形相关性，并在放射治疗期间自适应地进一步更新。具体地，在从当前患者收集的治疗图像的数量较少的初始治疗阶段，主要从其他训练患者学习统计变形相关性。随着从当前患者收集更多治疗图像，患者特异性信息将在学习患者特异性统计变形相关性以有效地反映治疗期间当前患者的前列腺变形方面发挥更重要的作用。最后，当已经从当前患者采集了足够数量的治疗图像时，仅使用患者特异性统计变形相关性来估计密集对应性。其次，将通过使用多元线性回归（MLR）模型来学习统计变形相关性，即，岭回归（RR）模型，它具有最好的预测精度比其他MLR模型，如典型相关分析（CCA）和主成分回归（PCR）。结果为了证明所提出的方法的性能，我们首先评估其配准精度进行比较的变形场预测我们的方法与变形场估计薄板样条（TPS）基于对应插值方法对306系列前列腺CT图像的24名患者。对于我们的基于RR的相关模型的方法，前列腺边界5 mm周围的体素的平均预测误差为0.38 mm。此外，相应的最大误差为2.89毫米。然后，我们比较了不同方法的变形插值的速度。当考虑较大的感兴趣区域（ROI）（尺寸为512 × 512 × 61）时，我们的方法需要24.41秒来插值密集变形场，而TPS方法需要6.7分钟;当考虑较小的ROI（前列腺周围）（尺寸为112 × 110 × 93）时，我们的方法需要1.80秒，而TPS方法需要25秒。结论实验结果表明，与基于TPS的对应（或变形）插值方法相比，该方法可以获得更快的配准速度，但配准精度相当。
PURPOSE In adaptive radiation therapy of prostate cancer, fast and accurate registration between the planning image and treatment images of the patient is of essential importance. With the authors' recently developed deformable surface model, prostate boundaries in each treatment image can be rapidly segmented and their correspondences (or relative deformations) to the prostate boundaries in the planning image are also established automatically. However, the dense correspondences on the nonboundary regions, which are important especially for transforming the treatment plan designed in the planning image space to each treatment image space, are remained unresolved. This paper presents a novel approach to learn the statistical correlation between deformations of prostate boundary and nonboundary regions, for rapidly estimating deformations of the nonboundary regions when given the deformations of the prostate boundary at a new treatment image. METHODS The main contributions of the proposed method lie in the following aspects. First, the statistical deformation correlation will be learned from both current patient and other training patients, and further updated adaptively during the radiotherapy. Specifically, in the initial treatment stage when the number of treatment images collected from the current patient is small, the statistical deformation correlation is mainly learned from other training patients. As more treatment images are collected from the current patient, the patient-specific information will play a more important role in learning patient-specific statistical deformation correlation to effectively reflect prostate deformation of the current patient during the treatment. Eventually, only the patient-specific statistical deformation correlation is used to estimate dense correspondences when a sufficient number of treatment images have been acquired from the current patient. Second, the statistical deformation correlation will be learned by using a multiple linear regression (MLR) model, i.e., ridge regression (RR) model, which has the best prediction accuracy than other MLR models such as canonical correlation analysis (CCA) and principal component regression (PCR). RESULTS To demonstrate the performance of the proposed method, we first evaluate its registration accuracy by comparing the deformation field predicted by our method with the deformation field estimated by the thin plate spline (TPS) based correspondence interpolation method on 306 serial prostate CT images of 24 patients. The average predictive error on the voxels around 5 mm of prostate boundary is 0.38 mm for our method of RR-based correlation model. Also, the corresponding maximum error is 2.89 mm. We then compare the speed for deformation interpolation by different methods. When considering the larger region of interest (ROI) with the size of 512 × 512 × 61, our method takes 24.41 seconds to interpolate the dense deformation field while TPS method needs 6.7 minutes; when considering a small ROI (surrounding prostate) with size of 112 × 110 × 93, our method takes 1.80 seconds, while TPS method needs 25 seconds. CONCLUSIONS Experimental results show that the proposed method can achieve much faster registration speed yet with comparable registration accuracy, compared to the TPS-based correspondence (or deformation) interpolation approach.