Task-Adaptive Angle Selection for Computed Tomography-Based Defect Detection

Tianyuan Wang*, Virginia Florian, Richard Schielein, Christian Kretzer, Stefan Kasperl, Felix Lucka, Tristan van Leeuwen*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Sparse-angle X-ray Computed Tomography (CT) plays a vital role in industrial quality control but leads to an inherent trade-off between scan time and reconstruction quality. Adaptive angle selection strategies try to improve upon this based on the idea that the geometry of the object under investigation leads to an uneven distribution of the information content over the projection angles. Deep Reinforcement Learning (DRL) has emerged as an effective approach for adaptive angle selection in X-ray CT. While previous studies focused on optimizing generic image quality measures using a fixed number of angles, our work extends them by considering a specific downstream task, namely image-based defect detection, and introducing flexibility in the number of angles used. By leveraging prior knowledge about typical defect characteristics, our task-adaptive angle selection method, adaptable in terms of angle count, enables easy detection of defects in the reconstructed images.

Original languageEnglish
Article number208
JournalJournal of Imaging
Volume10
Issue number9
DOIs
Publication statusPublished - Sept 2024

Keywords

  • adaptive angle selection
  • computed tomography
  • deep learning
  • defect detection
  • reinforcement learning

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