Suppr超能文献

通过时空图像配准从单次自由呼吸三维计算机断层扫描(3D-CT)重建四维计算机断层扫描(4D-CT)。

Reconstruction of 4D-CT from a single free-breathing 3D-CT by spatial-temporal image registration.

作者信息

Wu Guorong, Wang Qian, Lian Jun, Shen Dinggang

机构信息

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.

出版信息

Inf Process Med Imaging. 2011;22:686-98. doi: 10.1007/978-3-642-22092-0_56.

Abstract

In the radiation therapy of lung cancer, a free-breathing 3D-CT image is usually acquired in the treatment day for image-guided patient setup, by registering with the free-breathing 3D-CT image acquired in the planning day. In this way, the optimal dose plan computed in the planning day can be transferred onto the treatment day for cancer radiotherapy. However, patient setup based on the simple registration of the free-breathing 3D-CT images of the planning and the treatment days may mislead the radiotherapy, since the free-breathing 3D-CT is actually the mixed-phase image, with different slices often acquired from different respiratory phases. Moreover, a 4D-CT that is generally acquired in the planning day for improvement of dose planning is often ignored for guiding patient setup in the treatment day. To overcome these limitations, we present a novel two-step method to reconstruct the 4D-CT from a single free-breathing 3D-CT of the treatment day, by utilizing the 4D-CT model built in the planning day. Specifically, in the first step, we proposed a new spatial-temporal registration algorithm to align all phase images of the 4D-CT acquired in the planning day, for building a 4D-CT model with temporal correspondences established among all respiratory phases. In the second step, we first determine the optimal phase for each slice of the free-breathing (mixed-phase) 3D-CT of the treatment day by comparing with the 4D-CT of the planning day and thus obtain a sequence of partial 3D-CT images for the treatment day, each with only the incomplete image information in certain slices; and then we reconstruct a complete 4D-CT for the treatment day by warping the 4D-CT of the planning day (with complete information) to the sequence of partial 3D-CT images of the treatment day, under the guidance of the 4D-CT model built in the planning day. We have comprehensively evaluated our 4D-CT model building algorithm on a public lung image database, achieving the best registration accuracy over all other state-of-the-art methods. Also, we have validated our proposed 4D-CT reconstruction algorithm on the simulated free-breathing data, obtaining very promising 4D-CT reconstruction results.

摘要

在肺癌放射治疗中,通常在治疗当天获取自由呼吸的三维计算机断层扫描(3D-CT)图像用于图像引导的患者摆位,通过将其与计划当天获取的自由呼吸3D-CT图像进行配准。通过这种方式,在计划当天计算出的最佳剂量计划可以转移到治疗当天用于癌症放射治疗。然而,基于计划和治疗当天自由呼吸3D-CT图像的简单配准进行患者摆位可能会误导放射治疗,因为自由呼吸3D-CT实际上是混合相位图像,不同的切片通常是在不同的呼吸相位获取的。此外,通常在计划当天获取的用于改进剂量计划的四维计算机断层扫描(4D-CT)在治疗当天用于引导患者摆位时常常被忽视。为了克服这些限制,我们提出了一种新颖的两步法,通过利用计划当天建立的4D-CT模型,从治疗当天的单个自由呼吸3D-CT重建4D-CT。具体而言,第一步,我们提出了一种新的时空配准算法,用于对齐计划当天获取的4D-CT的所有相位图像,以建立一个在所有呼吸相位之间建立了时间对应关系的4D-CT模型。第二步,我们首先通过与计划当天的4D-CT进行比较,确定治疗当天自由呼吸(混合相位)3D-CT的每个切片的最佳相位,从而获得治疗当天的一系列部分3D-CT图像,每个图像在某些切片中仅具有不完整的图像信息;然后,在计划当天建立的4D-CT模型的指导下,通过将计划当天的4D-CT(具有完整信息)扭曲到治疗当天的部分3D-CT图像序列,重建治疗当天的完整4D-CT。我们在一个公共肺部图像数据库上全面评估了我们的4D-CT模型构建算法,在所有其他现有最先进方法中实现了最佳的配准精度。此外,我们在模拟的自由呼吸数据上验证了我们提出的4D-CT重建算法,获得了非常有前景的4D-CT重建结果。

相似文献

1
Reconstruction of 4D-CT from a single free-breathing 3D-CT by spatial-temporal image registration.
Inf Process Med Imaging. 2011;22:686-98. doi: 10.1007/978-3-642-22092-0_56.
3
Lung deformation estimation and four-dimensional CT lung reconstruction.
Acad Radiol. 2006 Sep;13(9):1082-92. doi: 10.1016/j.acra.2006.05.004.
4
Digital reconstruction of high-quality daily 4D cone-beam CT images using prior knowledge of anatomy and respiratory motion.
Comput Med Imaging Graph. 2015 Mar;40:30-8. doi: 10.1016/j.compmedimag.2014.10.007. Epub 2014 Oct 29.
5
Clinical implementation of target tracking by breathing synchronized delivery.
Med Phys. 2006 Nov;33(11):4330-6. doi: 10.1118/1.2359228.
6
Respiratory motion sampling in 4DCT reconstruction for radiotherapy.
Med Phys. 2012 Apr;39(4):1696-703. doi: 10.1118/1.3691174.
7
Helical mode lung 4D-CT reconstruction using Bayesian model.
Med Image Comput Comput Assist Interv. 2013;16(Pt 3):33-40. doi: 10.1007/978-3-642-40760-4_5.
10
Evaluation of a 4D cone-beam CT reconstruction approach using a simulation framework.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:5729-32. doi: 10.1109/IEMBS.2009.5333125.

引用本文的文献

1
Automatic large quantity landmark pairs detection in 4DCT lung images.
Med Phys. 2019 Oct;46(10):4490-4501. doi: 10.1002/mp.13726. Epub 2019 Aug 7.
3
4D-CT Lung registration using anatomy-based multi-level multi-resolution optical flow analysis and thin-plate splines.
Int J Comput Assist Radiol Surg. 2014 Sep;9(5):875-89. doi: 10.1007/s11548-013-0975-7. Epub 2014 Jan 14.
4
Lung deposition analyses of inhaled toxic aerosols in conventional and less harmful cigarette smoke: a review.
Int J Environ Res Public Health. 2013 Sep 23;10(9):4454-85. doi: 10.3390/ijerph10094454.

本文引用的文献

1
Nonrigid registration of dynamic medical imaging data using nD + t B-splines and a groupwise optimization approach.
Med Image Anal. 2011 Apr;15(2):238-49. doi: 10.1016/j.media.2010.10.003. Epub 2010 Oct 28.
2
A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets.
Phys Med Biol. 2009 Apr 7;54(7):1849-70. doi: 10.1088/0031-9155/54/7/001. Epub 2009 Mar 5.
3
Diffeomorphic demons: efficient non-parametric image registration.
Neuroimage. 2009 Mar;45(1 Suppl):S61-72. doi: 10.1016/j.neuroimage.2008.10.040. Epub 2008 Nov 7.
5
A comparison of algorithms for inference and learning in probabilistic graphical models.
IEEE Trans Pattern Anal Mach Intell. 2005 Sep;27(9):1392-416. doi: 10.1109/TPAMI.2005.169.
6
Unsupervised learning of an atlas from unlabeled point-sets.
IEEE Trans Pattern Anal Mach Intell. 2004 Feb;26(2):160-72. doi: 10.1109/TPAMI.2004.1262178.
7
Automated lung segmentation for thoracic CT impact on computer-aided diagnosis.
Acad Radiol. 2004 Sep;11(9):1011-21. doi: 10.1016/j.acra.2004.06.005.
8
Artifacts in computed tomography scanning of moving objects.
Semin Radiat Oncol. 2004 Jan;14(1):19-26. doi: 10.1053/j.semradonc.2003.10.004.
9
Acquiring a four-dimensional computed tomography dataset using an external respiratory signal.
Phys Med Biol. 2003 Jan 7;48(1):45-62. doi: 10.1088/0031-9155/48/1/304.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验