Image registration in myocardial perfusion MRI

Cardiac MR perfusion image processing techniques: a survey

First-pass cardiac MR perfusion (CMRP) imaging has undergone rapid technical advancements in recent years. Although the efficacy of CMRP imaging in the assessment of coronary artery diseases (CAD) has been proven, its clinical use is still limited. This limitation stems, in part, from manual interaction required to quantitatively analyze the large amount of data. This process is tedious, time-consuming, and prone to operator bias. Furthermore, acquisition and patient related image artifacts reduce the accuracy of quantitative perfusion assessment. With the advent of semi- and fully automatic image processing methods, not only the challenges posed by these artifacts have been overcome to a large extent, but a significant reduction has also been achieved in analysis time and operator bias. Despite an extensive literature on such image processing methods, to date, no survey has been performed to discuss this dynamic field. The purpose of this article is to provide an overview of the current state of the field with a categorical study, along with a future perspective on the clinical acceptance of image processing methods in the diagnosis of CAD.

A new similarity measure for non-rigid breathing motion compensation of myocardial perfusion MRI

Breathing movements during the image acquisition of first-pass gadolinium enhanced, myocardial perfusion Magnetic Resonance Imaging (MRI) hinder a direct automatic analysis of the blood flow of the myocardium. In addition, a qualitative readout by visual tracking is more difficult as well. Non-rigid registration can be used to compensate for these movements in the image series. Because of the local contrast and intensity change over time, the registration criterion needs to be chosen carefully. We propose a measure based on Normalized Gradient Fields (NGF) in order to obtain registration. Since this measure requires strong gradients in the images, we also test combining the measure with the Sum of Squared Differences (SSD) to maintain registration forces over the whole image area. To ensure smoothness, we employ a Laplacian regularizer and use the B-spline based approach to describe the transformation of the image space. Our experiments show that by using NGF good registration results can be obtained for image exhibiting a high intensity contrast. For images with a low intensity contrast, combining NGF and SSD improves the registration results significantly over using NGF only. Both measures are differentiable making possible the application of fast, gradient based optimizers.