2 citations · 2 across the 3 of their papers we have counts for
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Deep Image Prior for photoacoustic tomography can mitigate limited-view artifacts
Hanna Pulkkinen, Jenni Poimala, Leonid Kunyansky +2
We study the deep image prior (DIP) framework applied to photoacoustic tomography (PAT) as an unsupervised reconstruction approach to mitigate limited-view artifacts and noise comm…
Fast algorithms enabling optimization and deep learning for photoacoustic tomography in a circular detection geometry
Andreas Hauptmann, Leonid Kunyansky, Jenni Poimala
The inverse source problem arising in photoacoustic tomography and in several other coupled-physics modalities is frequently solved by iterative algorithms. Such algorithms are bas…
Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography
Andreas Hauptmann, Jenni Poimala
Learned iterative reconstructions hold great promise to accelerate tomographic imaging with empirical robustness to model perturbations. Nevertheless, an adoption for photoacoustic…