Semi-automated Matlab®-based Software for the analysis of first pass myocardial perfusion images

Semi-automated Matlab®-based Software for the analysis of first pass myocardial perfusion images

International Congress Series 1256 (2003) 1399 Semi-automated MatlabR-based Software for the analysis of first pass myocardial perfusion images M. Fe...

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International Congress Series 1256 (2003) 1399

Semi-automated MatlabR-based Software for the analysis of first pass myocardial perfusion images M. Fenchel a,*, U. Kramer a, N. Stauder a, U. Helber b, C.D. Claussen a, S. Miller a a

b

Department of Diagnostic Radiology, Eberhard-Karls-University, Tu¨bingen, Germany Division of Cardiology, Department of Internal Medicine, Eberhard-Karls-University, Tu¨bingen, Germany Received 13 March 2003; received in revised form 13 March 2003; accepted 19 March 2003

1. Purpose Recently, the detection of myocardial perfusion defects in patients with CAD via MRI became feasible. After the administration of a contrast agent bolus, the regional distribution of the contrast agent in the left ventricular myocardium is measured. Semi-quantitative perfusion parameters are easy to determine and highly reproducible. Previous studies revealed good correlation of myocardial perfusion with the maximum upslope of the tissue response curve during contrast agent wash in, as well as with peak signal intensity and area under the curve to peak signal intensity. The aim of this study was to develop a software in order to ease the evaluation of MR first-pass perfusion image data. This software should perform some image post processing (e.g. registration, filtering), derive perfusion parameters from the calculated SI-time curves and present the large amount of pixel data in a clear and concise way. 2. Methods We chose a MatlabR (The MathWorks, Natick, USA)-based approach (Version 6.1). Myocardial perfusion measurements were performed in 10 patients with angiographically proven CAD using a 1.5 T MR imager (Sonata, Siemens, Erlangen, Germany). After injection of Gd-DTPA (MagnevistR (Schering, Berlin, Germany), 0.025 mmol/kg bw) as a bolus, images were acquired using a SR TrueFISP 2D (TR 2.4 ms/TE 1.2 ms/FA 55/ matrix 72*128/FOV 300 mm/SL 8 mm) sequence. MR perfusion data was analyzed with our software and compared to SPECT and angiographic findings. 3. Results Data of eight patients could be adequately analysed with the software developed. Semi-quantitative perfusion parameters upslope, area under the curve and peak signal intensity in regions found to be malperfused by MRI correlated well with angiographically detected CA stenosis and hypoperfused areas detected by SPECT. Sensitivities for detection of malperfused areas were between 75% and 78% compared to SPECT, whereas the specificity was in the range of 74% to 84%. Correlation with angiographic findings yielded a sensitivity of 63% and a specificity 92%. Main limitation of the method was incorrect registration of images, caused by cardiac arrhythmia and patient motion. 4. Conclusions Semi-automated pixel-based analysis of MR perfusion images is possible and the promising results of this analysis correlate with angiographic and SPECT findings. However, artefact free data acquisition and exact image registration are important for the quality of the resulting data and represent the limiting factor for the pixel-based analysis. Despite those problems, the image quality was sufficient in 8 out of 10 patients. The pixel-based analysis with our software provided the examiner with a more detailed information about regional myocardial perfusion, which is essential for detecting subtle subendocardial perfusion defects. Further studies with a larger number of patients and rest – stress perfusion examinations have to be performed to confirm the results of this initial study. * Corresponding author. Department of Radiology, University of Tu¨bingen, Hoppe-Seyler-Str. 3, Tu¨bingen 72076, Germany. Tel.: +49-7071-2985837; fax: +49-7071-295845. E-mail address: [email protected] (M. Fenchel). 0531-5131/03 D 2003 Published by Elsevier Science B.V. doi:10.1016/S0531-5131(03)00478-3