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本文引用的文献

1
On-the-fly motion-compensated cone-beam CT using an a priori model of the respiratory motion.使用呼吸运动的先验模型进行实时运动补偿锥束CT
Med Phys. 2009 Jun;36(6):2283-96. doi: 10.1118/1.3115691.
2
Enhanced 4D cone-beam CT with inter-phase motion model.具有相间运动模型的增强型4D锥形束CT
Med Phys. 2007 Sep;34(9):3688-95. doi: 10.1118/1.2767144.
3
Four-dimensional cone beam CT with adaptive gantry rotation and adaptive data sampling.具有自适应机架旋转和自适应数据采样的四维锥形束CT
Med Phys. 2007 Sep;34(9):3520-9. doi: 10.1118/1.2767145.
4
Optimizing 4D cone-beam CT acquisition protocol for external beam radiotherapy.优化适形调强放疗的四维锥形束CT采集协议。
Int J Radiat Oncol Biol Phys. 2007 Mar 15;67(4):1211-9. doi: 10.1016/j.ijrobp.2006.10.024. Epub 2006 Dec 29.
5
Four-dimensional cone-beam computed tomography using an on-board imager.使用机载成像仪的四维锥形束计算机断层扫描
Med Phys. 2006 Oct;33(10):3825-33. doi: 10.1118/1.2349692.
6
Integrating respiratory gating into a megavoltage cone-beam CT system.将呼吸门控集成到兆伏级锥形束CT系统中。
Med Phys. 2006 Jul;33(7):2354-61. doi: 10.1118/1.2207136.
7
Towards cardiac C-arm computed tomography.迈向心脏C型臂计算机断层扫描
IEEE Trans Med Imaging. 2006 Jul;25(7):922-34. doi: 10.1109/tmi.2006.876166.
8
Linac-integrated 4D cone beam CT: first experimental results.直线加速器集成式4D锥形束CT:首次实验结果。
Phys Med Biol. 2006 Jun 7;51(11):2939-52. doi: 10.1088/0031-9155/51/11/017. Epub 2006 May 24.
9
Motion correction for improved target localization with on-board cone-beam computed tomography.采用机载锥形束计算机断层扫描进行运动校正以改善目标定位
Phys Med Biol. 2006 Jan 21;51(2):253-67. doi: 10.1088/0031-9155/51/2/005. Epub 2005 Dec 21.
10
Respiratory correlated cone beam CT.呼吸相关锥形束CT
Med Phys. 2005 Apr;32(4):1176-86. doi: 10.1118/1.1869074.

基于自适相关相位重建(AAPC)的 4D CBCT 重建。

Autoadaptive phase-correlated (AAPC) reconstruction for 4D CBCT.

机构信息

Institute of Medical Physics, University of Erlangen-Nürnberg, Erlangen D-91052, Germany.

出版信息

Med Phys. 2009 Dec;36(12):5695-706. doi: 10.1118/1.3260919.

DOI:10.1118/1.3260919
PMID:20095282
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3966083/
Abstract

PURPOSE

Kilovoltage cone-beam computed tomography (CBCT) is widely used in image-guided radiation therapy for exact patient positioning prior to the treatment. However, producing time series of volumetric images (4D CBCT) of moving anatomical structures remains challenging. The presented work introduces a novel method, combining high temporal resolution inside anatomical regions with strong motion and image quality improvement in regions with little motion.

METHODS

In the proposed method, the projections are divided into regions that are subject to motion and regions at rest. The latter ones will be shared among phase bins, leading thus to an overall reduction in artifacts and noise. An algorithm based on the concept of optical flow was developed to analyze motion-induced changes between projections. The technique was optimized to distinguish patient motion and motion deriving from gantry rotation. The effectiveness of the method is shown in numerical simulations and patient data.

RESULTS

The images reconstructed from the presented method yield an almost the same temporal resolution in the moving volume segments as a conventional phase-correlated reconstruction, while reducing the noise in the motionless regions down to the level of a standard reconstruction without phase correlation. The proposed simple motion segmentation scheme is yet limited to rotation speeds of less than 3 degrees/s.

CONCLUSIONS

The method reduces the noise in the reconstruction and increases the image quality. More data are introduced for each phase-correlated reconstruction, and therefore the applied dose is used more efficiently.

摘要

目的

千伏锥形束计算机断层扫描(CBCT)广泛应用于图像引导放射治疗中,用于在治疗前对患者进行精确的定位。然而,对于运动的解剖结构,生成时间序列的容积图像(4D CBCT)仍然具有挑战性。本研究提出了一种新方法,该方法结合了在解剖区域内具有高时间分辨率和在运动较小的区域内具有强运动和图像质量改善的能力。

方法

在所提出的方法中,将投影分为受运动影响的区域和静止区域。后者将在相区间共享,从而总体上减少伪影和噪声。开发了一种基于光流概念的算法来分析投影之间运动引起的变化。该技术经过优化,可区分患者运动和来自旋转机架的运动。该方法的有效性在数值模拟和患者数据中得到了证明。

结果

从所提出的方法重建的图像在运动体积段中具有几乎相同的时间分辨率,与传统的相位相关重建相比,同时将静止区域的噪声降低到没有相位相关的标准重建的水平。所提出的简单运动分割方案仍然仅限于转速小于 3 度/秒。

结论

该方法降低了重建中的噪声并提高了图像质量。对于每个相位相关重建,引入了更多的数据,因此更有效地利用了所应用的剂量。