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Journal of Medical Imaging • Open Access

Investigation into diagnostic accuracy of common strategies for automated perfusion motion correction
Author(s): Constantine Zakkaroff; John D. Biglands; John P. Greenwood; Sven Plein; Roger D. Boyle; Aleksandra Radjenovic; Derek R. Magee

Paper 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.

Paper Details

Date Published: 13 May 2016
PDF: 10 pages
J. Med. Img. 3(2) 024002 doi: 10.1117/1.JMI.3.2.024002
Published in: Journal of Medical Imaging Volume 3, Issue 2
Show Author Affiliations
Constantine Zakkaroff, Univ. of Leeds (United Kingdom)
John D. Biglands, Univ. of Leeds (United Kingdom)
John P. Greenwood, Univ. of Leeds (United Kingdom)
Sven Plein, Univ. of Leeds (United Kingdom)
Roger D. Boyle, Aberystwyth Univ. (United Kingdom)
Aleksandra Radjenovic, Univ. of Glasgow (United Kingdom)
Derek R. Magee, Univ. of Leeds (United Kingdom)

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