title: Registering prostate external beam radiotherapy with a boost from high-dose-rate brachytherapy: a comparative evaluation of deformable registration algorithms.
abstract: BACKGROUND
  Registering CTs for patients receiving external beam radiotherapy (EBRT) with a boost dose from high-dose-rate brachytherapy (HDR) can be challenging due to considerable image discrepancies (e.g. rectal fillings, HDR needles, HDR artefacts and HDR rectal packing materials). This study is the first to comparatively evaluate image processing and registration methods used to register the rectums in EBRT and HDR CTs of prostate cancer patients. The focus is on the rectum due to planned future analysis of rectal dose-volume response.
  
  
  METHODS
  For 64 patients, the EBRT CT was retrospectively registered to the HDR CT with rigid registration and non-rigid registration methods in VelocityAI. Image processing was undertaken on the HDR CT and the rigidly-registered EBRT CT to reduce the impact of discriminating features on alternative non-rigid registration methods applied in the software suite for Deformable Image Registration and Adaptive Radiotherapy Research (DIRART) using the Horn-Schunck optical flow and Demons algorithms. The propagated EBRT-rectum structures were compared with the HDR structure using the Dice similarity coefficient (DSC), Hausdorff distance (HD) and average surface distance (ASD). The image similarity was compared using mutual information (MI) and root mean squared error (MSE). The displacement vector field was assessed via the Jacobian determinant (JAC). The post-registration alignments of rectums for 21 patients were visually assessed.
  
  
  RESULTS
  The greatest improvement in the median DSC relative to the rigid registration result was 35 % for the Horn-Schunck algorithm with image processing. This algorithm also provided the best ASD results. The VelocityAI algorithms provided superior HD, MI, MSE and JAC results. The visual assessment indicated that the rigid plus deformable multi-pass method within VelocityAI resulted in the best rectum alignment.
  
  
  CONCLUSIONS
  The DSC, ASD and HD improved significantly relative to the rigid registration result if image processing was applied prior to DIRART non-rigid registrations, whereas VelocityAI without image processing provided significant improvements. Reliance on a single rectum structure-correspondence metric would have been misleading as the metrics were inconsistent with one another and visual assessments. It was important to calculate metrics for a restricted region covering the organ of interest. Overall, VelocityAI generated the best registrations for the rectum according to the visual assessment, HD, MI, MSE and JAC results.
authors.affiliation: School of Physics (M013), University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, 6009, Australia. calyn.moulton@research.uwa.edu.au.
authors.forename: Calyn R
authors.initials: CR
authors.lastname: Moulton
authors.affiliation: School of Physics (M013), University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, 6009, Australia.
authors.forename: Michael J
authors.initials: MJ
authors.lastname: House
authors.affiliation: Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, Western Australia, 6009, Australia.
authors.forename: Victoria
authors.initials: V
authors.lastname: Lye
authors.affiliation: Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, Western Australia, 6009, Australia.
authors.forename: Colin I
authors.initials: CI
authors.lastname: Tang
authors.affiliation: Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, Western Australia, 6009, Australia.
authors.forename: Michele
authors.initials: M
authors.lastname: Krawiec
authors.affiliation: Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, Western Australia, 6009, Australia.|School of Surgery, University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, 6009, Australia.
authors.forename: David J
authors.initials: DJ
authors.lastname: Joseph
authors.affiliation: School of Medicine and Population Health, University of Newcastle, University Drive, Callaghan, New South Wales, 2308, Australia.
authors.forename: James W
authors.initials: JW
authors.lastname: Denham
authors.affiliation: School of Physics (M013), University of Western Australia, 35 Stirling Highway, Crawley, Western Australia, 6009, Australia.|Radiation Oncology, Sir Charles Gairdner Hospital, Hospital Avenue, Nedlands, Western Australia, 6009, Australia.
authors.forename: Martin A
authors.initials: MA
authors.lastname: Ebert
mesh_terms: D000465:Algorithms; D001918:Brachytherapy / Q000379:methods; D054893:Cone-Beam Computed Tomography / Q000379:methods; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D011467:Prostate / Q000000981:diagnostic imaging; D011471:Prostatic Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D011878:Radiotherapy / Q000379:methods; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*