Browse > Article

Optimization of Image Tracking Algorithm Used in 4D Radiation Therapy  

Park, Jong-In (Department of Biomedical Engineering, Gachon University of Medicine and Science)
Shin, Eun-Hyuk (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine)
Han, Young-Yih (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine)
Park, Hee-Chul (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine)
Lee, Jai-Ki (Department of Nuclear Engineering, Hanyang University)
Choi, Doo-Ho (Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine)
Publication Information
Progress in Medical Physics / v.23, no.1, 2012 , pp. 8-14 More about this Journal
Abstract
In order to develop a Patient respiratory management system includinga biofeedback function for4-dimentional radiation therapy, this study investigated anoptimal tracking algorithmfor moving target using IR (Infra-red) camera as well as commercial camera. A tracking system was developed by LabVIEW 2010. Motion phantom images were acquired using a camera (IR or commercial). After image process were conducted to convert acquired image to binary image by applying a threshold values, several edge enhance methods such as Sobel, Prewitt, Differentiation, Sigma, Gradient, Roberts, were applied. The targetpattern was defined in the images, and acquired image from a moving targetwas tracked by matching pre-defined tracking pattern. During the matching of imagee, thecoordinateof tracking point was recorded. In order to assess the performance of tracking algorithm, the value of score which represents theaccuracy of pattern matching was defined. To compare the algorithm objectively, we repeat experiments 3 times for 5 minuts for each algorithm. Average valueand standard deviations (SD) of score were automatically calculatedsaved as ASCII format. Score of threshold only was 706, and standard deviation was 84. The value of average and SD for other algorithms which combined edge detection method and thresholdwere 794, 64 in Sobel, 770, 101 in Differentiation, 754, 85 in Gradient, 763, 75 in Prewitt, 777, 93 in Roberts, and 822, 62 in Sigma, respectively. According to score analysis, the most efficient tracking algorithm is the Sigma method. Therefore, 4-dimentional radiation threapy is expected tobemore efficient if threshold and Sigma edge detection method are used together in target tracking.
Keywords
4DRT; RPM signal; Respiratory gatied radiation therapy; Respiration analysis;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Benedict SH, Yenice KM, Followill D, et al: Stereotactic body radiation therapy: The report of AAPM Task Group 101. Med Phys 37:4078-4101 (2010)   DOI   ScienceOn
2 Keall PJ, Mageras GS, Balter JM, et al: The management of respiratory motion in radiation oncology report of AAPM Task Group 76. Med Phys 33:3874-3900 (2006)   DOI   ScienceOn
3 Park HC, Kin SS, Oh DH, Bae HS: Clinical considerations for respiration synchronized high-precision radiotherapy. Korean J Med Physics Supple 1:7 (2005)
4 Timmerman RD: An overview of hypofractionation and introduction to this issue of Seminars in Radiation Oncology. Semin Radiat Oncol 18:215-222 (2008)   DOI   ScienceOn
5 George R, Keall PJ, Kini VR, et al: Quantifying the effect of intrafraction motion during breast IMRT planning and dose delivery. Med Phys 30:552-562 (2003)   DOI   ScienceOn
6 Kubo HD, Wang LL: Introduction of audio gating to further reduce organ motion in breathing synchronized radiotherapy. Med Phys 29:345-350 (2002)   DOI   ScienceOn
7 Mageras GS, Yorke E, Rosenzweig K, et al: Fluoroscopic evaluation of diaphragmatic motion reduction with a respiratory gated radiotherapy system. J Appl Clin Med Phys 2:191-200 (2001)   DOI   ScienceOn
8 Kini VR, Vedam SS, Keall PJ, et al: Patient training in respiratory-gated radiotherapy. Med Dosim 28:7-11 (2003).   DOI   ScienceOn
9 Venkat RB, Keall P, Sawant A, George R: Respiratory training using audio visual bio-feedback Med Phys 34:2370-2370 (2007)
10 Shin EH, Han YI, Ju SG, Shin JS, Ahn YC: Efficacy of a respiration training system on the regularity of breathing. The J of Korean Society for Radiation Oncology 26:8 (2008)
11 Delk KK, Gevirtz R, Hicks DA, Carden F, Rucker R: The effects of biofeedback assisted breathing retraining on lung functions in patients with cystic fibrosis. Chest 105:23-28 (1994)   DOI   ScienceOn
12 Esteve F, Gallego J: The effects of breathing pattern training on ventilator function in patients with COPD. Biofeedback Self Regul 21:11 (1996)
13 Janson-Bjerklie S, Clarke E: The effects of biofeedback training on bronchial diameter in asthma. Heart Lung 11:200-207 (1982)
14 Khan AU: Effectiveness of biofeedback and counter-conditioning in the treatment of bronchial asthma. J Psychosom Res 21:97-104 (1977)   DOI   ScienceOn
15 Khan AU, Staerk M, Bonk C: Role of counter-conditioning in the treatment of asthma. J Psychosom Res 18:89-92 (1974)   DOI   ScienceOn
16 Mass R, Dahme B, Richter R: Clinical evaluation of a respiratory resistance biofeedback training. Biofeedback Self Regul 18:211-223 (1993)   DOI   ScienceOn
17 George R, Chung TD, Vedam SS, et al.: Audio-visual biofeedback for respiratory-gated radiotherapy: impact of audio instruction and audio-visual biofeedback on respiratory-gated radiotherapy. Int J Radiat Oncol Biol Phys 65:924-933 (2006)   DOI   ScienceOn
18 Leszczynski KW, Shalev S, Cosby NS: The enhancement of radiotherapy verification images by an automated edge detection technique. Med Phys 19:611-621 (1992)   DOI   ScienceOn
19 Somkantha K, Theera-Umpon N, Auephanwiriyakul S: Boundary detection in medical images using edge following alogirthm based on intensity gradient and texture gradient features. Ieee Transactions on Biomedical Engineering 58:7 (2011)   DOI
20 Gierga DP, Shap GC: The correlation between internal markers for abdominal tumors: implications for respiratory gating. Int J RadiatOncolBiol Physics 61:8 (2005)