Few-view cone-beam CT reconstruction with deformed prior image
Few-view cone-beam CT reconstruction with deformed prior image
复制标题
变形先验图像的少视锥束 CT 重建
DOI：
10.1118/1.4901265
复制
发表时间：
2014-12-01
期刊：
影响因子：
3.8
通讯作者：
Wang, Jing
中科院分区：
文献类型：
--
作者：
Zhang, Hua;Ouyang, Luo;Wang, Jing
目的：可以将先前图像并入到图像重建过程中，以改善来自稀疏视图或低剂量投影的后续锥束CT（CBCT）图像的质量。本文提出了一种基于变形先验图像的重建策略，以减小先验图像与目标图像之间的变形。方法：采用基于投影的配准方法获得变形先验图像。具体地，通过迭代地匹配变形的先前图像的前向投影和测量的治疗中投影来估计用于使先前图像变形的变形矢量场。变形后的先验图像作为标准先验图像约束压缩感知（PICCS）算法的先验图像。对XCAT体模和头颈部癌症患者的临床研究进行了模拟研究，以评估所提出的DPIR strategy.Results的性能：变形的先验图像匹配的几何形状上的治疗CBCT更紧密相比，原始的先验图像。因此，DPIR策略从少视图投影的性能相比，标准的PICCS算法，视觉检查和定量措施的基础上提高。在使用20个投影的XCAT体模研究中，平均均方根误差从PICCS的14%降低到DPIR的10%，平均通用质量指数从PICCS的0.88增加到DPIR的0.92。本DPIR方法为先验图像和目标图像之间的失配问题提供了一种实用的解决方案，该方法提高了原始PICCS算法在少视图或低剂量投影CBCT重建中的性能。(C)2014年美国医学物理学家协会。
Purpose: Prior images can be incorporated into the image reconstruction process to improve the quality of subsequent cone-beam CT (CBCT) images from sparse-view or low-dose projections. The purpose of this work is to develop a deformed prior image-based reconstruction (DPIR) strategy to mitigate the deformation between the prior image and the target image.Methods: The deformed prior image is obtained by a projection-based registration approach. Specifically, the deformation vector fields used to deform the prior image are estimated through iteratively matching the forward projection of the deformed prior image and the measured on-treatment projections. The deformed prior image is then used as the prior image in the standard prior image constrained compressed sensing (PICCS) algorithm. A simulation study on an XCAT phantom and a clinical study on a head-and-neck cancer patient were conducted to evaluate the performance of the proposed DPIR strategy.Results: The deformed prior image matches the geometry of the on- treatment CBCT more closely as compared to the original prior image. Consequently, the performance of the DPIR strategy from few-view projections is improved in comparison to the standard PICCS algorithm, based on both visual inspection and quantitative measures. In the XCAT phantom study using 20 projections, the average root mean squared error is reduced from 14% in PICCS to 10% in DPIR, and the average universal quality index increases from 0.88 in PICCS to 0.92 in DPIR.Conclusions: The present DPIR approach provides a practical solution to the mismatch problem between the prior image and target image, which improves the performance of the original PICCS algorithm for CBCT reconstruction from few-view or low-dose projections. (C) 2014 American Association of Physicists in Medicine.