Cardiac MR perfusion image processing techniques: A survey

• Published in: Medical image analysis Vol. 16; no. 4; pp. 767 - 785
• Main Authors: Gupta, Vikas, Kirişli, Hortense A., Hendriks, Emile A., van der Geest, Rob J., van de Giessen, Martijn, Niessen, Wiro, Reiber, Johan H.C., Lelieveldt, Boudewijn P.F.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01-05-2012
• Subjects: Algorithms; Cardiac MR; Coronary Artery Disease - diagnosis; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Angiography - methods; MRI; Myocardial Perfusion Imaging - methods; Myocardium; Perfusion; Reproducibility of Results; Sensitivity and Specificity; Survey; Myocardium; Survey; Perfusion; MRI; Cardiac MR
• ISSN: 1361-8415; 1361-8423
• DOI: 10.1016/j.media.2011.12.005

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► Survey of semi- and fully automatic image processing methods for cardiac MR perfusion quantification. ► Classification of image processing methods based on their registration, segmentation and multimodality fusion and visualization algorithms. ► Investigation of the advantages and drawbacks of the surveyed methods and their robustness to acquisition artifacts. ► Extensive literature overview of the last 20 years.
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.