Literature DB >> 19131670

A theoretical model for respiratory motion artifacts in free-breathing CT scans.

John H Lewis1, Steve B Jiang.   

Abstract

Successful radiotherapy treatment depends heavily upon the accuracy of patient geometry captured during treatment simulation using computed tomography (CT) scans. Radiotherapy patients are often scanned under free breathing, and respiratory motion can cause severe artifacts in CT scans, including shortening, elongation or splitting of the shapes and shifting of the midpoint positions of the tumor and organs. This paper presents a theoretical model that explains the source of motion artifacts and the relationship between motion artifacts and the motion parameters of the scanner, treatment couch and tumor/organ. It is shown that an understanding of the relationship between the translational table velocity and the maximum tumor/organ velocity might enable one to mitigate certain types of motion artifacts. We show that splitting artifacts can be eliminated if the scanning speed is above the maximum tumor/organ velocity. Slow scanning speeds are shown to be useful for obtaining accurate internal target volumes (ITVs), and fast scanning speeds are shown to be useful for obtaining accurate tumor/organ shapes. In both cases, an upper bound on the maximum possible error is calculated as a function of the scanning speed. A set of special scanning speeds which allow for an accurate representation of tumor/organ length along the craniocaudal direction is obtained, and a relationship between the maximum displacement of a tumor/organ image's midpoint position and the magnitude of its length distortion is derived.

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Year:  2009        PMID: 19131670     DOI: 10.1088/0031-9155/54/3/018

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  8 in total

1.  The potential for undertaking slow CT using a modern CT scanner.

Authors:  C D Chinneck; M McJury; A R Hounsell
Journal:  Br J Radiol       Date:  2010-06-15       Impact factor: 3.039

2.  Estimating the 4D respiratory lung motion by spatiotemporal registration and super-resolution image reconstruction.

Authors:  Guorong Wu; Qian Wang; Jun Lian; Dinggang Shen
Journal:  Med Phys       Date:  2013-03       Impact factor: 4.071

3.  3D Kinect Camera Scheme with Time-Series Deep-Learning Algorithms for Classification and Prediction of Lung Tumor Motility.

Authors:  Utumporn Puangragsa; Jiraporn Setakornnukul; Pittaya Dankulchai; Pattarapong Phasukkit
Journal:  Sensors (Basel)       Date:  2022-04-11       Impact factor: 3.847

4.  Radiomic feature stability across 4D respiratory phases and its impact on lung tumor prognosis prediction.

Authors:  Qian Du; Michael Baine; Kyle Bavitz; Josiah McAllister; Xiaoying Liang; Hongfeng Yu; Jeffrey Ryckman; Lina Yu; Hengle Jiang; Sumin Zhou; Chi Zhang; Dandan Zheng
Journal:  PLoS One       Date:  2019-05-07       Impact factor: 3.240

Review 5.  Understanding Sources of Variation to Improve the Reproducibility of Radiomics.

Authors:  Binsheng Zhao
Journal:  Front Oncol       Date:  2021-03-29       Impact factor: 6.244

6.  Reproducibility of image quality for moving objects using respiratory-gated computed tomography: a study using a phantom model.

Authors:  Nobuyoshi Fukumitsu; Masaya Ishida; Toshiyuki Terunuma; Masashi Mizumoto; Takayuki Hashimoto; Takashi Moritake; Toshiyuki Okumura; Takeji Sakae; Koji Tsuboi; Hideyuki Sakurai
Journal:  J Radiat Res       Date:  2012-09-10       Impact factor: 2.724

7.  Geometrical differences in gross target volumes between 3DCT and 4DCT imaging in radiotherapy for non-small-cell lung cancer.

Authors:  Fengxing Li; Jianbin Li; Yingjie Zhang; Min Xu; Dongping Shang; Tingyong Fan; Tonghai Liu; Qian Shao
Journal:  J Radiat Res       Date:  2013-04-05       Impact factor: 2.724

8.  A respiratory compensating system: design and performance evaluation.

Authors:  Ho-Chiao Chuang; Ding-Yang Huang; Der-Chi Tien; Ren-Hong Wu; Chung-Hsien Hsu
Journal:  J Appl Clin Med Phys       Date:  2014-05-08       Impact factor: 2.102

  8 in total

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