Literature DB >> 16266099

The application of the sinusoidal model to lung cancer patient respiratory motion.

R George1, S S Vedam, T D Chung, V Ramakrishnan, P J Keall.   

Abstract

Accurate modeling of the respiratory cycle is important to account for the effect of organ motion on dose calculation for lung cancer patients. The aim of this study is to evaluate the accuracy of a respiratory model for lung cancer patients. Lujan et al. [Med. Phys. 26(5), 715-720 (1999)] proposed a model, which became widely used, to describe organ motion due to respiration. This model assumes that the parameters do not vary between and within breathing cycles. In this study, first, the correlation of respiratory motion traces with the model f(t) as a function of the parameter n (n = 1, 2, 3) was undertaken for each breathing cycle from 331 four-minute respiratory traces acquired from 24 lung cancer patients using three breathing types: free breathing, audio instruction, and audio-visual biofeedback. Because cos2 and cos4 had similar correlation coefficients, and cos2 and cos1 have a trigonometric relationship, for simplicity, the cos1 value was consequently used for further analysis in which the variations in mean position (z0), amplitude of motion (b) and period (tau) with and without biofeedback or instructions were investigated. For all breathing types, the parameter values, mean position (z0), amplitude of motion (b), and period (tau) exhibited significant cycle-to-cycle variations. Audio-visual biofeedback showed the least variations for all three parameters (z0, b, and tau). It was found that mean position (z0) could be approximated with a normal distribution, and the amplitude of motion (b) and period (tau) could be approximated with log normal distributions. The overall probability density function (pdf) of f(t) for each of the three breathing types was fitted with three models: normal, bimodal, and the pdf of a simple harmonic oscillator. It was found that the normal and the bimodal models represented the overall respiratory motion pdfs with correlation values from 0.95 to 0.99, whereas the range of the simple harmonic oscillator pdf correlation values was 0.71 to 0.81. This study demonstrates that the pdfs of mean position (z0), amplitude of motion (b), and period (tau) can be used for sampling to obtain more realistic respiratory traces. The overall standard deviations of respiratory motion were 0.48, 0.57, and 0.55 cm for free breathing, audio instruction, and audio-visual biofeedback, respectively.

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Year:  2005        PMID: 16266099     DOI: 10.1118/1.2001220

Source DB:  PubMed          Journal:  Med Phys        ISSN: 0094-2405            Impact factor:   4.071


  36 in total

1.  A novel technique for markerless, self-sorted 4D-CBCT: feasibility study.

Authors:  Irina Vergalasova; Jing Cai; Fang-Fang Yin
Journal:  Med Phys       Date:  2012-03       Impact factor: 4.071

2.  The impact of audio-visual biofeedback on 4D PET images: results of a phantom study.

Authors:  Jaewon Yang; Tokihiro Yamamoto; Byungchul Cho; Youngho Seo; Paul J Keall
Journal:  Med Phys       Date:  2012-02       Impact factor: 4.071

3.  A phantom model demonstration of tomotherapy dose painting delivery, including managed respiratory motion without motion management.

Authors:  Michael W Kissick; Xiaohu Mo; Keisha C McCall; Leah K Schubert; David C Westerly; Thomas R Mackie
Journal:  Phys Med Biol       Date:  2010-04-30       Impact factor: 3.609

4.  Breathing-synchronized delivery: a potential four-dimensional tomotherapy treatment technique.

Authors:  Tiezhi Zhang; Weiguo Lu; Gustavo H Olivera; Harry Keller; Robert Jeraj; Rafael Manon; Minesh Mehta; Thomas R Mackie; Bhudatt Paliwal
Journal:  Int J Radiat Oncol Biol Phys       Date:  2007-06-14       Impact factor: 7.038

5.  Use of MRI to assess the prediction of heart motion with gross body motion in myocardial perfusion imaging by stereotracking of markers on the body surface.

Authors:  Michael A King; Joyoni Dey; Karen Johnson; Paul Dasari; Joyeeta M Mukherjee; Joseph E McNamara; Arda Konik; Cliff Lindsay; Shaokuan Zheng; Dennis Coughlin
Journal:  Med Phys       Date:  2013-11       Impact factor: 4.071

6.  Target tracking using DMLC for volumetric modulated arc therapy: a simulation study.

Authors:  Baozhou Sun; Dharanipathy Rangaraj; Lech Papiez; Swetha Oddiraju; Deshan Yang; H Harold Li
Journal:  Med Phys       Date:  2010-12       Impact factor: 4.071

7.  Respiratory triggered 4D cone-beam computed tomography: a novel method to reduce imaging dose.

Authors:  Benjamin J Cooper; Ricky T O'Brien; Salim Balik; Geoffrey D Hugo; Paul J Keall
Journal:  Med Phys       Date:  2013-04       Impact factor: 4.071

8.  rConverse: Moment by Moment Conversation Detection Using a Mobile Respiration Sensor.

Authors:  Rummana Bari; Roy J Adams; Mahbubur Rahman; Megan Battles Parsons; Eugene H Buder; Santosh Kumar
Journal:  Proc ACM Interact Mob Wearable Ubiquitous Technol       Date:  2018-03

9.  Management of three-dimensional intrafraction motion through real-time DMLC tracking.

Authors:  Amit Sawant; Raghu Venkat; Vikram Srivastava; David Carlson; Sergey Povzner; Herb Cattell; Paul Keall
Journal:  Med Phys       Date:  2008-05       Impact factor: 4.071

10.  From phase-based to displacement-based gating: a software tool to facilitate respiration-gated radiation treatment.

Authors:  Joseph P Santoro; Ellen Yorke; Karyn A Goodman; Gig S Mageras
Journal:  J Appl Clin Med Phys       Date:  2009-10-07       Impact factor: 2.102

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