Tracking tumor boundary using point correspondence for adaptive radio therapy. (October 2018)

• Record Type: Journal Article
• Authors: Tahmasebi, Nazanin; Boulanger, Pierre; Yun, Jihyun; Fallone, B. Gino; Punithakumar, Kumaradevan
• Is Part Of: Computer methods and programs in biomedicine. Volume 165(2018)
• Page Start: 187
• Page End: 195
• Publication Date: 2018-10
• Subjects: Non-rigid image registration -- Image segmentation -- MRI-Guided radiotherapy -- Tumor tracking -- Linac-MR -- Radiation therapy; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.08.002
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This study addresses the problem of delineating mobile lung tumor regions for radiation therapy. We propose two registration approaches to track the tumor regions: 1) First frame approach; and 2) Best frame approach. We evaluated the proposed approaches over a sequence of 600 images acquired from 6 patients. The best frame approach yielded an average Dice score of 0.89 ± 0.04 and Hausdorff distance of 3.38 ± 0.10 mm. This study demonstrates a promising boundary tracking tool for delineating the tumor region for adaptive radiation therapy. Abstract: Background and objective : Tracking mobile tumor regions during the treatment is a crucial part of image-guided radiation therapy because of two main reasons which negatively affect the treatment process: (1) a tiny error will lead to some healthy tissues being irradiated; and (2) some cancerous cells may survive if the beam is not accurately positioned as it may not cover the entire cancerous region. However, tracking or delineation of such a tumor region from magnetic resonance imaging (MRI) is challenging due to photometric similarities of the region of interest and surrounding area as well as the influence of motion in the organs. The purpose of this work is to develop an approach to track the center and boundary of tumor region by auto-contouring the region of interest in moving organs for radiotherapy. Methods : We utilize a nonrigid registration method as well as a publicly available RealTITracker algorithm for MRI to delineate and track tumor regions from a sequence of MRI images. The location and shape of the tumor region in the MRI image sequence varies over time due to breathing. We investigate two approaches: the first one uses manual segmentation of the first frame during the pretreatment stage; and the second one utilizes manual segmentation of all the frames during the pretreatment stage. Results : We evaluated the proposed approaches over a sequence of 600 images acquired from 6 patients. The method that utilizes all the frames in the pretreatment stage with moving mesh based registration yielded the best performance with an average Dice Score of 0.89 ± 0.04 and Hausdorff Distance of 3.38 ± 0.10 mm. Conclusions : This study demonstrates a promising boundary tracking tool for delineating the tumor region that can deal with respiratory movement and the constraints of adaptive radiation therapy.