Simultaneous multi-modality ROI delineation in clinical practice.
The delineation of tumors and their surrounding organs at risk is a critical step of the treatment planning for radiation therapy. Besides computer tomography (CT), other imaging modalities are used to improve the quality of the delineations, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). A practical framework is presented for using multiple datasets from different modalities during the delineation phase. The system is based on two basic principles. First, all image datasets of all available modalities are displayed in their original form (in their own coordinate system, with their own spatial resolution and voxel aspect ratio), and second, delineations can take place on all orthogonal views of each dataset and changes made to a delineation are visualized in all image sets, giving direct feedback to the delineator. The major difference between the described approach and other existing delineation tools is that instead of resampling the image sets, the delineations are transformed from one dataset to another. The transformation used for transferring the delineations is obtained by rigid normalized mutual information registration. The crucial components and the benefits of the application are presented and discussed.
Bol
Gijsbert H
GH
Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands. G.H.Bol@umcutrecht.nl
Kotte
Alexis N T J
AN
van der Heide
Uulke A
UA
Lagendijk
Jan J W
JJ
D000465:Algorithms; D001185:Artificial Intelligence; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009369:Neoplasms / Q000175:diagnosis* / Q000532:radiotherapy*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011881:Radiotherapy, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity; D013382:Subtraction Technique