Premium
Investigating CT to CBCT image registration for head and neck proton therapy as a tool for daily dose recalculation
Author(s) -
Landry Guillaume,
Nijhuis Reinoud,
Dedes George,
Handrack Josefine,
Thieke Christian,
Janssens Guillaume,
Orban de Xivry Jonathan,
Reiner Michael,
Kamp Florian,
Wilkens Jan J.,
Paganelli Chiara,
Riboldi Marco,
Baroni Guido,
Ganswindt Ute,
Belka Claus,
Parodi Katia
Publication year - 2015
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 - 10.1118/1.4908223
Subject(s) - image registration , proton therapy , nuclear medicine , medicine , head and neck , radiation therapy , radiation treatment planning , cone beam computed tomography , head and neck cancer , computed tomography , artificial intelligence , radiology , computer science , image (mathematics) , surgery
Purpose: Intensity modulated proton therapy (IMPT) of head and neck (H&N) cancer patients may be improved by plan adaptation. The decision to adapt the treatment plan based on a dose recalculation on the current anatomy requires a diagnostic quality computed tomography (CT) scan of the patient. As gantry‐mounted cone beam CT (CBCT) scanners are currently being offered by vendors, they may offer daily or weekly updates of patient anatomy. CBCT image quality may not be sufficient for accurate proton dose calculation and it is likely necessary to perform CBCT CT number correction. In this work, the authors investigated deformable image registration ( dir ) of the planning CT (pCT) to the CBCT to generate a virtual CT (vCT) to be used for proton dose recalculation. Methods: Datasets of six H&N cancer patients undergoing photon intensity modulated radiation therapy were used in this study to validate the vCT approach. Each dataset contained a CBCT acquired within 3 days of a replanning CT (rpCT), in addition to a pCT. The pCT and rpCT were delineated by a physician. A Morphons algorithm was employed in this work to perform dir of the pCT to CBCT following a rigid registration of the two images. The contours from the pCT were deformed using the vector field resulting from dir to yield a contoured vCT. The dir accuracy was evaluated with a scale invariant feature transform (SIFT) algorithm comparing automatically identified matching features between vCT and CBCT. The rpCT was used as reference for evaluation of the vCT. The vCT and rpCT CT numbers were converted to stopping power ratio and the water equivalent thickness (WET) was calculated. IMPT dose distributions from treatment plans optimized on the pCT were recalculated with a Monte Carlo algorithm on the rpCT and vCT for comparison in terms of gamma index, dose volume histogram (DVH) statistics as well as proton range. The dir generated contours on the vCT were compared to physician‐drawn contours on the rpCT. Results: The dir accuracy was better than 1.4 mm according to the SIFT evaluation. The mean WET differences between vCT (pCT) and rpCT were below 1 mm (2.6 mm). The amount of voxels passing 3%/3 mm gamma criteria were above 95% for the vCT vs rpCT. When using the rpCT contour set to derive DVH statistics from dose distributions calculated on the rpCT and vCT the differences, expressed in terms of 30 fractions of 2 Gy, were within [−4, 2 Gy] for parotid glands ( D mean ), spinal cord ( D 2% ), brainstem ( D 2% ), and CTV ( D 95% ). When using dir generated contours for the vCT, those differences ranged within [−8, 11 Gy]. Conclusions: In this work, the authors generated CBCT based stopping power distributions using dir of the pCT to a CBCT scan. dir accuracy was below 1.4 mm as evaluated by the SIFT algorithm. Dose distributions calculated on the vCT agreed well to those calculated on the rpCT when using gamma index evaluation as well as DVH statistics based on the same contours. The use of dir generated contours introduced variability in DVH statistics.