title: Investigation into diagnostic accuracy of common strategies for automated perfusion motion correction.
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 ([Formula: see text]). 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.
authors.affiliation: University of Leeds , School of Computing, Leeds LS2 9JT, United Kingdom.
authors.forename: Constantine
authors.initials: C
authors.lastname: Zakkaroff
authors.affiliation: University of Leeds, Division of Medical Physics, Leeds LS2 9JT, United Kingdom; University of Leeds, Multidisciplinary Cardiovascular Research Centre and Leeds Institute of Cardiovascular and Metabolic Medicine, Leeds LS2 9JT, United Kingdom.
authors.forename: John D
authors.initials: JD
authors.lastname: Biglands
authors.affiliation: University of Leeds, Division of Medical Physics, Leeds LS2 9JT, United Kingdom; University of Leeds, Multidisciplinary Cardiovascular Research Centre and Leeds Institute of Cardiovascular and Metabolic Medicine, Leeds LS2 9JT, United Kingdom.
authors.forename: John P
authors.initials: JP
authors.lastname: Greenwood
authors.affiliation: University of Leeds, Division of Medical Physics, Leeds LS2 9JT, United Kingdom; University of Leeds, Multidisciplinary Cardiovascular Research Centre and Leeds Institute of Cardiovascular and Metabolic Medicine, Leeds LS2 9JT, United Kingdom.
authors.forename: Sven
authors.initials: S
authors.lastname: Plein
authors.affiliation: University of Aberystwyth , Institute of Biological, Environmental and Rural Sciences, Aberystwyth, Ceredigion SY23 3DA, United Kingdom.
authors.forename: Roger D
authors.initials: RD
authors.lastname: Boyle
authors.affiliation: University of Glasgow , Institute of Cardiovascular and Medical Sciences, Glasgow Cardiovascular Centre, Glasgow G12 8QQ, United Kingdom.
authors.forename: Aleksandra
authors.initials: A
authors.lastname: Radjenovic
authors.affiliation: University of Leeds , School of Computing, Leeds LS2 9JT, United Kingdom.
authors.forename: Derek R
authors.initials: DR
authors.lastname: Magee
keywords: automated perfusion motion correction; perfusion registration; quantitative perfusion analysis