Dual-modality joint reconstruction of PET-MRI incorporating a cross-guided prior

Combining PET and MRI, the hybrid scanner can simultaneously acquire data from both the dual modalities. Therefore, the research on improving the quality of dual-modality images is necessary and important, and joint reconstruction of PET-MRI has the potential to enable superior imaging for both imaging modalities. We propose a cross-guided (CG) prior model based on guided functions, which can be incorporated into dual-modality joint reconstruction of PET and MRI by Maximum a posterior (MAP) framework. In our work, the proposed CG prior model was composed of two parts: PET part weighted with anatomy-guided knowledge of MRI, and MRI part weighted with anatomy-guided knowledge of PET. To test the feasibility and validity of the proposed CG prior, we compared it with separate reconstruction without prior (no prior), separate total variation (TV) prior, joint total variation (JTV) prior and linear parallel level sets (LPLS) prior. The objective functions based on different priors were achieved with the same quasi-Newton method. The performances of different reconstruction methods were verified with simulation data of
¹⁸
F- FDG PET and T1-weighted MRI from brain phantom. As the results indicated, the proposed algorithm substantially reduced the noise and reconstruction error of both PET and MR images and achieved sharper and higher resolution images, which outperformed other state-of-the-art methods. In conclusion, the proposed CG prior is considerably effective, which can provide a potential way for dual-modality joint reconstruction.