Image Reconstruction With Local Directional Total Variation Regularization Using Tomographic Incompleteness Maps - IMAG
Conference Papers Year : 2024

Image Reconstruction With Local Directional Total Variation Regularization Using Tomographic Incompleteness Maps

Abstract

Limited angle acquisition is a well-known challenge in computed tomography reconstruction because the lack of data generates severe geometric artifacts in reconstructed images. Recently, directional total variation (DTV) regularization has shown promising results for this kind of problem but it requires fine-tuning of a global directional hyperparameter in addition to the regularization weight. In this work, we propose a new regularization, called local directional total variation (LDTV), which is a DTV based on local directional weights determined from tomographic incompleteness maps. We evaluated LDTV and compared it to state-of-the-art algorithms on simulated twodimensional acquisitions of the Forbild head phantom with a source trajectory made of two orthogonal arcs of 60°each. The reconstructed images show that the LDTV regularization performs better in this geometry for both noiseless and noisy data.
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Dates and versions

hal-04704936 , version 1 (21-09-2024)

Licence

Public Domain

Identifiers

  • HAL Id : hal-04704936 , version 1

Cite

Matthieu Laurendeau, Laurent Desbat, Frédéric Jolivet, Guillaume Bernard, Sébastien Georges, et al.. Image Reconstruction With Local Directional Total Variation Regularization Using Tomographic Incompleteness Maps. The 8th International Conference on Image Formation in X-Ray Computed Tomography, Marc Kachelrieß, Aug 2024, Bamberg, Germany. pp.376-379. ⟨hal-04704936⟩
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