Literature DB >> 23039671

Methodology for registration of distended rectums in pelvic CT studies.

B Rodriguez-Vila1, F Garcia-Vicente, E J Gomez.   

Abstract

PURPOSE: Accurate delineation of the rectum is of high importance in off-line adaptive radiation therapy since it is a major dose-limiting organ in prostate cancer radiotherapy. The intensity-based deformable image registration (DIR) methods cannot create a correct spatial transformation if there is no correspondence between the template and the target images. The variation of rectal filling, gas, or feces, creates a non correspondence in image intensities that becomes a great obstacle for intensity-based DIR.
METHODS: In this study the authors have designed and implemented a semiautomatic method to create a rectum mask in pelvic computed tomography (CT) images. The method, that includes a DIR based on the demons algorithm, has been tested in 13 prostate cancer cases, each comprising of two CT scans, for a total of 26 CT scans.
RESULTS: The use of the manual segmentation in the planning image and the proposed rectum mask method (RMM) method in the daily image leads to an improvement in the DIR performance in pelvic CT images, obtaining a mean value of overlap volume index = 0.89, close to the values obtained using the manual segmentations in both images.
CONCLUSIONS: The application of the RMM method in the daily image and the manual segmentations in the planning image during prostate cancer treatments increases the performance of the registration in presence of rectal fillings, obtaining very good agreement with a physician's manual contours.

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Year:  2012        PMID: 23039671     DOI: 10.1118/1.4754798

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


  2 in total

1.  Methodology for Registration of Shrinkage Tumors in Head-and-Neck CT Studies.

Authors:  Jianhua Wang; Jianrong Dai; Yongjie Jing; Yanan Huo; Tianye Niu
Journal:  Comput Math Methods Med       Date:  2015-05-18       Impact factor: 2.238

2.  Robust contour propagation using deep learning and image registration for online adaptive proton therapy of prostate cancer.

Authors:  Mohamed S Elmahdy; Thyrza Jagt; Roel Th Zinkstok; Yuchuan Qiao; Rahil Shahzad; Hessam Sokooti; Sahar Yousefi; Luca Incrocci; C A M Marijnen; Mischa Hoogeman; Marius Staring
Journal:  Med Phys       Date:  2019-07-12       Impact factor: 4.071

  2 in total

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