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Learning The Invisible: A Hybrid Deep Learning-Shearlet-Based Framework for Limited Angle Computed Tomography

Lunedì 29 Ottobre 2018, ore 15:00, aula M2.5, edificio Matematica, Dipartimento FIM, Modena

Relatore: Dott.ssa Tatiana Bubba (Department of Mathematics and Statistics - University of Helsinki)

Abstract: Limited angle geometry is still a rather challenging modality in computed tomography (CT). Compared to the standard filtered back-projection (FBP), regularization-based methods, combined with iterative schemes, help in removing artifacts but still cannot deliver satisfactory reconstructions.
Based on the result that limited tomographic datasets reveal parts of the wavefront (WF) set in a stable way and artifacts from limited angle CT have some directional property, we propose a method that combines, in the phase space, the information coming from the visible part of the WF set and "inpaints" the invisible one by learning it with a convolutional neural network (CNN) architecture. The WF set information is accessed by using the directional features of shearlets combined with a compressed sensing formulation, which is well suited to derive visible and invisible coefficients. Compared to other recently proposed deep learning strategies for (limited data) CT, our method provides a superior performance, an (heuristic) understanding of why the method works, providing a more reliable approach especially for medical applications.

Ospiti: Marco Prato

[Ultimo aggiornamento: 01/04/2021 13:04:25]