Deep morphological network-based artifact suppression for limited-angle tomography

Ishak Ayad (1) , Cécilia Tarpau (1, 2, 3) , Mai K. Nguyen (1) , Ngoc-Son Vu (1)

1 ETIS - UMR 8051 - Equipes Traitement de l'Information et Systèmes
2 LPTM - UMR 8089 - Laboratoire de Physique Théorique et Modélisation
3 LMV - Laboratoire de Mathématiques de Versailles

Computed tomography has been widely used in biomedical and industrial applications. The well-known filtered back-projection algorithm, probably the most used reconstruction technique, fails when the angular range used for data acquisition is not sufficient. As a consequence, reconstructions exhibit artifacts. In order to eliminate these artifacts, we propose in this article a new deep learning approach based on a U-net architecture which includes a morphological operation. This operation of mathematical morphology allows us to capture better some non-linear properties of the object to reconstruct. The proposed method provides good results for angular ranges of 170, 150, 130 and even 110 degrees. To the best of our knowledge, it is the first time a limited-angle artifact suppression method works with 110 projections.

Mots clés: Computed tomography; Image reconstruction; Limited angular projections; Mathematical morphology; Deep learning; U-Net; Computed tomography

Domaines: Intelligence artificielle [cs.AI]; Imagerie médicale; Traitement des images [eess.IV]

Ishak Ayad, Cécilia Tarpau, Mai K. Nguyen, Ngoc-Son Vu. Deep morphological network-based artifact suppression for limited-angle tomography. 25nd International Conference on Image Processing, Computer Vision and Pattern Recognition (IPCV’21), Jul 2021, Las Vegas, United States. ⟨hal-03736966⟩