document: Matching PET and CT scans of the head and neck area : Development of method and validation Positron emission tomography -LRB- PET -RRB- provides important information on tumor biology , but lacks detailed anatomical information . Our aim in the present study was to develop and validate an automatic registration method for matching PET and CT scans of the head and neck . Three difficulties in achieving this goal are -LRB- 1 -RRB- nonrigid motions of the neck can hamper the use of automatic ridged body transformations ; -LRB- 2 -RRB- emission scans contain too little anatomical information to apply standard image fusion methods ; and -LRB- 3 -RRB- no objective way exists to quantify the quality of the match results . These problems are solved as follows : accurate and reproducible positioning of the patient was achieved by using a radiotherapy treatment mask . The proposed method makes use of the transmission rather than the emission scan . To obtain sufficient -LRB- anatomical -RRB- information for matching , two bed positions for the transmission scan were included in the protocol . A mutual information-based algorithm was used as a registration technique . PET and CT data were obtained in seven patients . Each patient had two CT scans and one PET scan . The datasets were used to estimate the consistency by matching PET to CT/sub 1 / , CT/sub 1 / to CT/sub 2 / , and CT/sub 2 / to PET using the full circle consistency test . It was found that using our method , consistency could be obtained of 4 mm and 1.3 degrees on average . The PET voxels used for registration were 5.15 mm , so the errors compared quite favorably with the voxel size . Cropping the images -LRB- removing the scanner bed from images -RRB- did not improve the consistency of the algorithm . The transmission scan , however , could potentially be reduced to a single position using this approach . In conclusion , the represented algorithm and validation technique has several features that are attractive from both theoretical and practical point of view , it is a user-independent , automatic validation technique for matching CT and PET scans of the head and neck , which gives the opportunity to compare different image enhancements
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