Multi-modal weighted quadratic priors for robust intensity independent synergistic PET-MR reconstruction

TY - CHAP

T1 - Multi-modal weighted quadratic priors for robust intensity independent synergistic PET-MR reconstruction

AU - Mehranian, Abolfazl

AU - Belzunce, Martin A.

AU - McGinnity, Colm Joseph

AU - Prieto Vasquez, Claudia

AU - Hammers, Alexander

AU - Reader, Andrew Jonathan

PY - 2017/7/3

Y1 - 2017/7/3

N2 - We propose a simple and robust synergistic PET-MR reconstruction algorithm using mutually-weighted quadratic priors. Maximum a-posteriori (MAP) objective functions were used for PET and MR reconstructions, including MAP expectation maximization (MAPEM) for PET and MAP sensitivity encoding (SENSE) for MR reconstruction. For both reconstructions, mutually-weighted quadratic priors were used for reduction of noise and artifacts, with preservation of PET-MR common boundaries. The weighting coefficients are updated from the current PET and MR estimates using normalized multi-modal Gaussian similarity kernels, which are in turn derived as the product of modality-specific kernels. Hence, the resulting kernels are independent of both signal intensities and contrast orientations. The performance of the proposed method was evaluated using 3D realistic simulations and a clinical FDG PET/T1-MPRAGE/FLAIR MR dataset. For simulations, undersampled MR reconstructions with undersampling factors of 4, 6 and 8 were considered while for the clinical dataset an MR undersampling factor of 4 was used. For PET reconstructions, the proposed method was compared with maximum likelihood expectation maximization (MLEM) and fully-sampled MR guided MAPEM (as a PET benchmark). For MR reconstructions, the proposed method was compared with fully-sampled reconstruction (as an MR benchmark), total variation (TV) regularized undersampled SENSE, and PET/FLAIR guided undersampled SENSE. Results showed that the proposed method can outperform conventional reconstructions especially for highly undersampled MR data, while preserving modality-unique features. For the clinical dataset, the proposed method showed promising results especially for PET reconstruction, in spite of the substantial PET-MR intensity and contrast differences. In summary, the proposed synergistic algorithm and priors offer a robust multi-modal synergistic image reconstruction framework.

AB - We propose a simple and robust synergistic PET-MR reconstruction algorithm using mutually-weighted quadratic priors. Maximum a-posteriori (MAP) objective functions were used for PET and MR reconstructions, including MAP expectation maximization (MAPEM) for PET and MAP sensitivity encoding (SENSE) for MR reconstruction. For both reconstructions, mutually-weighted quadratic priors were used for reduction of noise and artifacts, with preservation of PET-MR common boundaries. The weighting coefficients are updated from the current PET and MR estimates using normalized multi-modal Gaussian similarity kernels, which are in turn derived as the product of modality-specific kernels. Hence, the resulting kernels are independent of both signal intensities and contrast orientations. The performance of the proposed method was evaluated using 3D realistic simulations and a clinical FDG PET/T1-MPRAGE/FLAIR MR dataset. For simulations, undersampled MR reconstructions with undersampling factors of 4, 6 and 8 were considered while for the clinical dataset an MR undersampling factor of 4 was used. For PET reconstructions, the proposed method was compared with maximum likelihood expectation maximization (MLEM) and fully-sampled MR guided MAPEM (as a PET benchmark). For MR reconstructions, the proposed method was compared with fully-sampled reconstruction (as an MR benchmark), total variation (TV) regularized undersampled SENSE, and PET/FLAIR guided undersampled SENSE. Results showed that the proposed method can outperform conventional reconstructions especially for highly undersampled MR data, while preserving modality-unique features. For the clinical dataset, the proposed method showed promising results especially for PET reconstruction, in spite of the substantial PET-MR intensity and contrast differences. In summary, the proposed synergistic algorithm and priors offer a robust multi-modal synergistic image reconstruction framework.

M3 - Conference paper

BT - IEEE NSS MIC Conference

PB - IEEE

T2 - 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

Y2 - 21 October 2017 through 28 October 2017

ER -