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Automated 3-Dimensional Registration of Stand-Alone ¹⁸F-FDG Whole-Body PET with CT

¹ Diagnostic Radiology and Nuclear Medicine Department, University of Western Ontario, London, Ontario, Canada
² Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California
³ Department of Nuclear Medicine, Center for PET, Zentralklinik Bad Berka, Bad Berka, Germany

Image registration and fusion of whole-body ¹⁸F-FDG PET with thoracic CT would allow combination of anatomic detail from CT with functional PET information, which could lead to improved diagnosis or PET-based radiotherapy planning. Methods: We have designed a practical and fully automated algorithm for the elastic 3-dimensional image registration of whole-body PET and CT images, which compensates for the nonlinear deformation due to breath-hold CT imaging. A set of 18 PET and CT patient datasets has been evaluated by the algorithm. Initially, a 9-parameter linear registration is performed by maximizing the mutual information (MI)-based cost function, between the CT and the combination of emission and transmission PET volumes, using progressively increased matrix sizes to increase speed and provide better convergence. Subsequently, lung contours on transmission maps and corresponding contours on CT volumes are automatically detected. A large number (few hundreds) of corresponding point pairs are automatically derived, defining a thin-plate-spline (TPS) elastic transformation of PET emission and transmission scans to match the CT scan. Results: In all 18 patients the automatic linear registration with multiresolution converged close to the final alignment, but, in 10 cases, the nonlinear differences in the diaphragm position and chest wall were still clearly visible. The nonlinear adjustment, which was in the order of 40–75 mm, significantly improved the alignment between breath-hold CT and PET, especially in the areas of the diaphragm. Lung volumes measured from transmission and CT scans match closely after the warping has been applied. The average computation time is <40 s for the linear component and <30 s for the nonlinear component for a typical PET scan with 4–6 bed positions. Conclusion: We have developed a technique for automatic nonlinear registration of CT and PET whole-body images to common spatial coordinates. This technique may be applied for automatic fusion of PET with CT acquired on stand-alone scanners during normal breathing or breath-hold data acquisition.

Key Words: thoracic PET • image registration • multimodality • image fusion • nonlinear registration

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