Zakkaroff, C, Biglands, JD, Greenwood, JP et al. (4 more authors) (2016) Investigation into diagnostic accuracy of common strategies for automated perfusion motion correction. Journal of Medical Imaging, 3 (2). 024002. ISSN 2329-4302
Abstract
Respiratory motion is a significant obstacle to the use of quantitative perfusion in clinical practice. Increasingly complex motion correction algorithms are being developed to correct for respiratory motion. However, the impact of these improvements on the final diagnosis of ischemic heart disease has not been evaluated. The aim of this study was to compare the performance of four automated correction methods in terms of their impact on diagnostic accuracy. Three strategies for motion correction were used: (1) independent translation correction for all slices, (2) translation correction for the basal slice with transform propagation to the remaining two slices assuming identical motion in the remaining slices, and (3) rigid correction (translation and rotation) for the basal slice. There were no significant differences in diagnostic accuracy between the manual and automatic motion-corrected datasets (p=0.88). The area under the curve values for manual motion correction and automatic motion correction were 0.93 and 0.92, respectively. All of the automated motion correction methods achieved a comparable diagnostic accuracy to manual correction. This suggests that the simplest automated motion correction method (method 2 with translation transform for basal location and transform propagation to the remaining slices) is a sufficiently complex motion correction method for use in quantitative myocardial perfusion.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016, Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | automated perfusion motion correction; perfusion registration; quantitative perfusion analysis |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) > Artificial Intelligence & Biological Systems (Leeds) The University of Leeds > Faculty of Medicine and Health (Leeds) |
Funding Information: | Funder Grant number Wellcome Trust 088908/Z/09/Z British Heart Foundation 29062 British Heart Foundation 29062 Leeds Teaching Hospitals CharitableFoundation 3T92/1205 |
Depositing User: | Symplectic Publications |
Date Deposited: | 19 May 2016 12:18 |
Last Modified: | 23 Jun 2023 22:06 |
Published Version: | http://dx.doi.org/10.1117/1.JMI.3.2.024002 |
Status: | Published |
Publisher: | SPIE |
Identification Number: | 10.1117/1.JMI.3.2.024002 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:99881 |