Imaging based on Compton scattering: model uncertainty and data-driven reconstruction methods

Abstract

The recent development of scintillation crystals combined with $\gamma$-rays sources opens the way to an imaging concept based on Compton scattering, namely Compton scattering tomography (CST). The associated inverse problem rises many challenges: non-linearity, multiple order-scattering and high level of noise. Already studied in the literature, these challenges lead unavoidably to uncertainty of the forward model. This work proposes to study exact and approximated forward models and develops two data-driven reconstruction algorithms able to tackle the inexactness of the forward model. The first one is based on the projective method called regularized sequential subspace optimization (RESESOP). We consider here a finite dimensional restriction of the semi-discrete forward model and show its well-posedness and regularisation properties. The second one considers the unsupervised learning method, deep image prior (DIP), inspired by the construction of the model uncertainty in RESESOP. The methods are validated on Monte-Carlo data.

On a cylindrical scanning modality in three-dimensional Compton scatter tomography

“…Compton scatter tomography is an imaging technique which uses Compton scattered photons to recover an electron density, which has applications in security screening, medical and cultural heritage imaging, i.e., imaging objects with historical significance, such as fossils and museum artifacts [23,27,3,8,35]. The literature presents a number of scanning modalities in two and threedimensional CST [27,35,17,34,19,18,38,33,24,26,4,30,7,15,25,39].…”