Automated measurement of arterial input function in first-pass myocardial perfusion magnetic resonance images using independent component analysis

Quantitative assessment of first-pass cardiac magnetic resonance (CMR) perfusion imaging is useful for detecting coronary artery disease, but it requires the measurement of the arterial input function (AIF) from the left ventricle. This is usually done manually, which is time consuming and subjective. This study presents an automated method for measuring the AIF from the first-pass CMR perfusion images. It was tested on 194 clinical perfusion studies and compared with manual reference measurements. Our results show the proposed method successfully measured 98.79% of the perfusion series, with manual and automated measurements strongly correlating. Temporal statistics were similar for both measurements, showing agreement between the automated and manual AIFs. The automated method, however, more accurately selected the brightest left ventricle pixels and excluded papillary muscles. These improvements may help make AIF measurement and quantitative CMR myocardial perfusion analysis more accurate and readily available.