The advent of new image reconstruction and image processing techniques for CT images has increased the need for robust objective image quality assessment methods. One of the most common quality assessment methods is the measurement of signal detectability for a known signal at a known location using supervised classification techniques. However, this method requires a large number of simulations or physical measurements, and its underlying assumptions may be considered clinically unrealistic. In this study we focus on objective assessment of image quality in terms of detection of a signal with unknown location, size, shape, and contrast. We explore several unsupervised saliency detection methods which assume no knowledge about the signal, along with a template matching technique which uses information about the signal's size and shape in the object domain, for simulated phantoms that have been reconstructed using filtered back projection (FBP) and iterative reconstruction algorithms (IRA). The performance of each of the image reconstruction algorithms is then measured using the area under the localization receiver operating characteristic curve (LROC) and exponential transformation of the free response operating characteristic curve (EFROC). Our results indicate that unsupervised saliency detection methods can be effectively used to determine image quality in terms of signal detectability for unknown signals given only a small number of sample images.

Date Published: 17 March 2015
PDF: 7 pages
Proc. SPIE 9416, Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment, 94160J (17 March 2015); doi: 10.1117/12.2082254

Published in SPIE Proceedings Vol. 9416:
Medical Imaging 2015: Image Perception, Observer Performance, and Technology Assessment
Claudia R. Mello-Thoms; Matthew A. Kupinski, Editor(s)