Share Email Print

Proceedings Paper

Deep learning-based artifact detection for diagnostic CT images
Author(s): Prakhar Prakash; Sandeep Dutta
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

Calibrated detector response is crucial to good image quality in diagnostic CT and imaging systems in general. Defects during manufacturing, component failures and system aging can introduce shift in detector response which, if left uncorrected, can lead to image artifacts. Such artifacts reduce the image quality and can cause misdiagnosis in clinical practice. In this work a deep learning (DL)-based artifact detection method is developed to automatically screen for common imaging detector induced artifacts such as rings, streaks and bands in images. To circumvent the difficulty in obtaining and annotating the artifact images, a diagnostic CT physics simulator is utilized to generate CT images across a range of acquisition and reconstruction settings. Artifacts are introduced in the projection view data by perturbing the detector gain relative to the gain normalization scan during the simulation. The artifact images and corresponding ground truth segmentation of the artifact type and location serve as the training dataset. Linear support vector machine with squared hinge loss (L2-SVM) was used as the loss function during training as early experiments showed small but consistent improvements over the more commonly used cross-entropy loss for segmentation. The trained network achieved ~97%, ~86% and ~93% independent test accuracy for ring, streak and band artifacts respectively. Since deep learning methods learn by example, the detection method is not limited to the imaging scenarios presented in this work and can be extended to other applications.

Paper Details

Date Published: 1 March 2019
PDF: 6 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 109484C (1 March 2019); doi: 10.1117/12.2511766
Show Author Affiliations
Prakhar Prakash, GE Healthcare (United States)
Sandeep Dutta, GE Healthcare (United States)

Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?