Optimization of megavoltage CT scan registration settings for thoracic cases on helical tomotherapy.
This study aims to investigate the settings that provide optimum registration accuracy when registering megavoltage CT (MVCT) studies acquired on tomotherapy with planning kilovoltage CT (kVCT) studies of patients with lung cancer. For each experiment, the systematic difference between the actual and planned positions of the thorax phantom was determined by setting the phantom up at the planning isocenter, generating and registering an MVCT study. The phantom was translated by 5 or 10 mm, MVCT scanned, and registration was performed again. A root-mean-square equation that calculated the residual error of the registration based on the known shift and systematic difference was used to assess the accuracy of the registration process. The phantom study results for 18 combinations of different MVCT/kVCT registration options are presented and compared to clinical registration data from 17 lung cancer patients. MVCT studies acquired with coarse (6 mm), normal (4 mm) and fine (2 mm) slice spacings could all be registered with similar residual errors. No specific combination of resolution and fusion selection technique resulted in a lower residual error. A scan length of 6 cm with any slice spacing registered with the full image fusion selection technique and fine resolution will result in a low residual error most of the time. On average, large corrections made manually by clinicians to the automatic registration values are infrequent. Small manual corrections within the residual error averages of the registration process occur, but their impact on the average patient position is small. Registrations using the full image fusion selection technique and fine resolution of 6 cm MVCT scans with coarse slices have a low residual error, and this strategy can be clinically used for lung cancer patients treated on tomotherapy. Automatic registration values are accurate on average, and a quick verification on a sagittal MVCT slice should be enough to detect registration outliers.
Woodford
Curtis
C
London Regional Cancer Program, London Health Sciences Centre, 790 Commissioners Road East, London, Ontario N6A 4L6, Canada.
Yartsev
Slav
S
Van Dyk
Jake
J
D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy*; D011785:Quality Assurance, Health Care; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000379:methods; D011881:Radiotherapy, Computer-Assisted / Q000379:methods; D020266:Radiotherapy, Conformal / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*