Physics-driven Deep Learning for PET/MRI

• URL: http://arxiv.org/abs/2206.06788v1
• Date: Sat, 11 Jun 2022 21:35:27 GMT
• Title: Physics-driven Deep Learning for PET/MRI
• Authors: Abhejit Rajagopal, Andrew P. Leynes, Nicholas Dwork, Jessica E. Scholey, Thomas A. Hope, and Peder E. Z. Larson
• Abstract summary: We review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems. These reconstruction approaches utilize priors, either structural or statistical, together with a physics-based description of the PET system response. We elucidate how a multi-faceted approach accommodates hybrid data- and physics-driven machine learning for reconstruction of 3D PET/MRI.
• Score: 2.2113800586902608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we review physics- and data-driven reconstruction techniques for simultaneous positron emission tomography (PET) / magnetic resonance imaging (MRI) systems, which have significant advantages for clinical imaging of cancer, neurological disorders, and heart disease. These reconstruction approaches utilize priors, either structural or statistical, together with a physics-based description of the PET system response. However, due to the nested representation of the forward problem, direct PET/MRI reconstruction is a nonlinear problem. We elucidate how a multi-faceted approach accommodates hybrid data- and physics-driven machine learning for reconstruction of 3D PET/MRI, summarizing important deep learning developments made in the last 5 years to address attenuation correction, scattering, low photon counts, and data consistency. We also describe how applications of these multi-modality approaches extend beyond PET/MRI to improving accuracy in radiation therapy planning. We conclude by discussing opportunities for extending the current state-of-the-art following the latest trends in physics- and deep learning-based computational imaging and next-generation detector hardware.

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