Journal article

Joint PET-MRI image reconstruction using a patch-based joint-dictionary prior

Viswanath P Sudarshan, Gary F Egan, Zhaolin Chen, Suyash P Awate

Medical Image Analysis | ELSEVIER | Published : 2020

Abstract

For simultaneous positron-emission-tomography and magnetic-resonance-imaging (PET-MRI) systems, while early methods relied on independently reconstructing PET and MRI images, recent works have demonstrated improvement in image reconstructions of both PET and MRI using joint reconstruction methods. The current state-of-the-art joint reconstruction priors rely on fine-scale PET-MRI dependencies through the image gradients at corresponding spatial locations in the PET and MRI images. In the general context of image restoration, compared to gradient-based models, patch-based models (e.g., sparse dictionaries) have demonstrated better performance by modeling image texture better. Thus, we propose..

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University of Melbourne Researchers

Grants

Awarded by Infrastructure Facility for Advanced Research and Education in Diagnostics - Department of Biotechnology (DBT), Government of India


Awarded by Australian Research Council


Funding Acknowledgements

The authors are grateful for support from the Infrastructure Facility for Advanced Research and Education in Diagnostics grant funded by Department of Biotechnology (DBT), Government of India (BT/INF/22/SP23026/2017), the Reignwood Cultural Foundation, and the Australian Research Council Linkage grant LP170100494. Thanks to Shenpeng Li (Monash University) for reconstructing and pre-processing in vivo PET and MRI data.