Investigation into diagnostic accuracy of common strategies for automated perfusion motion correction.

Constantine Zakkaroff,Sven Plein,John D Biglands,Aleksandra Radjenovic,Derek R Magee,Roger D Boyle,John P Greenwood

doi:10.1117/1.jmi.3.2.024002

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.

Highlights

Magnetic resonance imaging (MRI) dynamic contrast enhanced (DCE) myocardial perfusion data have been shown to have a high sensitivity and specificity for diagnosing myocardial ischemia.[1,2] DCE-MRI perfusion datasets can be analyzed to provide quantitative myocardial perfusion estimates, which have been shown to perform well diagnostically.[3,4] Quantitation of myocardial perfusion requires regions of interest (ROIs) to be drawn over the myocardium and blood pool in every frame in the DCE-MRI dataset to obtain signal enhancement versus time curves
Motion correction methods attempt to remove the respiratory motion from the dataset so that ROIs are required on a single frame only
In the images prior to correction, the magnitude of respiratory motion can be observed from the changing position of the interventricular septum and ventricles, Fig. 5 Visual comparison of perfusion correction stages

Summary

Introduction

Magnetic resonance imaging (MRI) dynamic contrast enhanced (DCE) myocardial perfusion data have been shown to have a high sensitivity and specificity for diagnosing myocardial ischemia.[1,2] DCE-MRI perfusion datasets can be analyzed to provide quantitative myocardial perfusion estimates, which have been shown to perform well diagnostically.[3,4] Quantitation of myocardial perfusion requires regions of interest (ROIs) to be drawn over the myocardium and blood pool in every frame in the DCE-MRI dataset to obtain signal enhancement versus time curves. The main challenge of manual motion correction is the respiratory motion, causing the imaging plane to pass through an entirely different location (above or below the intended slice location), which breaks the underlying assumption of perfusion analysis. Motion correction methods attempt to remove the respiratory motion from the dataset so that ROIs are required on a single frame only. Automated motion correction is challenging in DCE-MRI due to poor signal-to-noise ratio in the images, the temporally changing image contrast, and through-plane motion

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