title: Daily target localization for prostate patients based on 3D image correlation.
abstract: There are several localization techniques that have been used for prostate treatment. Recently, the potential use of a variety of CT-based equipment in the treatment room has been discussed. The goal of our study was to develop an automated procedure for daily treatment table shift calculation based on two CT data sets: simulation CT data and localization CT data. The method suggested in this study is a 3D image cross-correlation of small regions of interest (ROI) within the two data sets. The relative position of the two ROIs with respect to each other is determined by the maximum value of the normalized cross-correlation function, calculated for all possible relative locations of the two ROIs. After the best match is found the shifts are given by the vector connecting the treatment isocentre and the planning isocentre (both determined by the radio opaque fiducial markers on the patient's skin). The results have been compared with shifts calculated through manual fusion. The shift differences, averaged over 17 statistically independent shift calculations, are less then 1 mm in the lateral and longitudinal directions, and about 1 mm in the AP direction. The impact of image noise on the performance of the algorithm has been tested. The results show that the algorithm accurately adjusts for target positional changes even with Gaussian noise levels as high as 20% inserted.
authors.affiliation: Department of Radiation Oncology, Fox Chase Cancer Center, Philadelphia, PA 19111, USA. K_Paskalev@fccc.edu
authors.forename: K
authors.initials: K
authors.lastname: Paskalev
authors.forename: C M
authors.initials: CM
authors.lastname: Ma
authors.forename: R
authors.initials: R
authors.lastname: Jacob
authors.forename: R
authors.initials: R
authors.lastname: Price
authors.forename: S
authors.initials: S
authors.lastname: McNeeley
authors.forename: L
authors.initials: L
authors.lastname: Wang
authors.forename: B
authors.initials: B
authors.lastname: Movsas
authors.forename: A
authors.initials: A
authors.lastname: Pollack
mesh_terms: D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D009068:Movement; D009367:Neoplasm Staging / Q000379:methods; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*