A data driven motion compensation for head movement in CT for hybrid PET/CT brain scan: A phantom and patient study

Ziel/Aim The aim is to investigate the impact of a 3D iterative data driven motion compenstaion method to compensate head movement during computed tomography (CT) scan of brain PET/CT and examine its significance for final image reconstruction.

Methodik/Methods A series of PET/CT scans of Hoffman brain phantom filled with fluorodeoxyglucose (FDG) were acquired using mCT Siemens Biograph PET/CT scanner. The phantom was acquired with a variety of movements during CT part of the acquisition, to simulate patient movements, but the phantom remained stationary during PET scan. Each motion corrupted CT scan was reconstructed using fully automated 3D iterative data driven image reconstruction with motion compensation (MC) to remove motion artifacts and afterward an attenuation map was generated from this motion corrected CT. The PET raw data was reconstructed offline using JSrecon algorithm with attenuation map from motion corrected CT and compared with the PET scan reconstructed with attenuation map from motion corrupted CT. The data driven motion compensation approach was also implemented on patient presenting head movement during CT part of brain PET/CT scan. All reconstructed images were scored for qualitative analysis and the absolute difference and normalized mean square error (NMSE) were calculated for quantitative analysis.

Ergebnisse/Results The PET/CT images with motion corrected CT appeared always superior. The NMSE values were 0.071 for motion corrupted CT, 0.0523 in motion corrected images for phantom scans and 0.082 and 0.051 for patient scan respectively.

Schlussfolgerungen/Conclusions Data driven iterative motion compensation approach for head CT siginificantly increases quantitative and qualitative accuracy of the PET/CT brain image.

Publication History

Article published online:
08 April 2021