Share Email Print

Proceedings Paper • new

Automatic strategy for CHO channel reduction in x-ray angiography systems
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Multiple efforts have been made in x-ray angiography to transition from traditional image quality metrics to mathematical observer models. Recent works have successfully implemented the channelized Hotelling observer (CHO) model for x-ray angiography systems. However, in these works the channel selection process is ambiguous and limits to identifying a range of frequencies and other channel parameters that are believed to represent the most relevant features of the imaging tasks. This channel selection rationale can be sufficient for certain simple scenarios but it might not be enough for more complex ones. On the other hand, it has been shown that besides dealing with the well-known bias caused by a finite number of samples, there is also another source of bias in the estimation of the detectability index in x-ray angiography. Such source of bias has been attributed to nonrandom differences in noise between images acquired at different time points, also referred as temporally variable nonstationary noise. This work proposes a task-specific automated method for optimal channel selection and corrects for the influence of bias due to temporally variable nonstationary noise, particular from x-ray angiography systems. The proposed method is computationally inexpensive, provides time efficient selection of optimal channels, and contributes to minimize bias, all of these without significantly compromising the accuracy of the detectability index estimation. This method for channel optimization can be readily adapted to other imaging modalities.

Paper Details

Date Published: 4 March 2019
PDF: 7 pages
Proc. SPIE 10952, Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, 1095205 (4 March 2019); doi: 10.1117/12.2513609
Show Author Affiliations
Daniel Gomez-Cardona, Mayo Clinic (United States)
Shuai Leng, Mayo Clinic (United States)
Christopher P. Favazza, Mayo Clinic (United States)
Beth A. Schueler, Mayo Clinic (United States)
Kenneth A. Fetterly, Mayo Clinic (United States)

Published in SPIE Proceedings Vol. 10952:
Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment
Robert M. Nishikawa; Frank W. Samuelson, Editor(s)

© SPIE. Terms of Use
Back to Top