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Deformable registration of abdominal kilovoltage treatment planning CT and tomotherapy daily megavoltage CT for treatment adaptation
Author(s) -
Yang Deshan,
Chaudhari Summer R.,
Goddu S. Murty,
Pratt David,
Khullar Divya,
Deasy Joseph O.,
El Naqa Issam
Publication year - 2009
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.3049594
Subject(s) - image registration , tomotherapy , medicine , medical imaging , radiation treatment planning , computer vision , preprocessor , artificial intelligence , nuclear medicine , radiology , computer science , radiation therapy , image (mathematics)
In adaptive radiation therapy the treatment planning kilovoltage CT (kVCT) images need to be registered with daily CT images. Daily megavoltage CT (MVCT) images are generally noisier than the kVCT images. In addition, in the abdomen, low image contrast, differences in bladder filling, differences in bowel, and rectum filling degrade image usefulness and make deformable image registration very difficult. The authors have developed a procedure to overcome these difficulties for better deformable registration between the abdominal kVCT and MVCT images. The procedure includes multiple image preprocessing steps and a two deformable registration steps. The image preprocessing steps include MVCT noise reduction, bowel gas pockets detection and painting, contrast enhancement, and intensity manipulation for critical organs. The first registration step is carried out in the local region of the critical organs (bladder, prostate, and rectum). It requires structure contours of these critical organs on both kVCT and MVCT to obtain good registration accuracy on these critical organs. The second registration step uses the first step results and registers the entire image with less intensive computational requirement. The two‐step approach improves the overall computation speed and works together with these image preprocessing steps to achieve better registration accuracy than a regular single step registration. The authors evaluated the procedure on multiple image datasets from prostate cancer patients and gynecological cancer patients. Compared to rigid alignment, the proposed method improves volume matching by over 60% for the critical organs and reduces the prostate landmark registration errors by 50%.