24 March 2016 Quantitative MRI myocarditis analysis by a PCA-based object recognition algorithm
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Abstract
Magnetic Resonance Imaging (MRI) has shown promising results in diagnosing myocarditis that can be qualitatively observed as enhanced pixels on the cardiac muscles images. In this paper, a quantitative MRI Myocarditis Analysis is proposed. Analysis consists in introducing a myocarditis index, defined as the ratio between enhanced pixels, representing an inflammation, and the total pixels of myocardial muscle. In order to recognize and quantify enhanced pixels, a PCA-based recognition algorithm is used. The algorithm, implemented in Matlab, was tested by examining a group of 12 patients, referred to MRI with presumptive, clinical diagnosis of myocarditis. To assess intra- and interobserver variability, two observers blindly analyzed data related to the 12 patients by delimiting myocardial region and selecting enhanced pixels. After 10 days the same observers redid the analysis. The obtained myocarditis indexes were compared to an ordinal variable (values in the 1 - 5 range) that represented the blind assessment of myocarditis seriousness given by two radiologists on the base of the patient case histories. Results show that there is a significant correlation (P < 0:001; r = 0:96) between myocarditis indexes and the radiologists' clinical judgments. Furthermore, a good intraobserver and interobserver reproducibility was obtained.
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Rocco Romano, Rocco Romano, Fausto Acernese, Fausto Acernese, Gerardo Giordano, Gerardo Giordano, Igino De Giorgi, Igino De Giorgi, Antonio Orientale, Antonio Orientale, Giovanni Babino, Giovanni Babino, Fabrizio Barone, Fabrizio Barone, } "Quantitative MRI myocarditis analysis by a PCA-based object recognition algorithm", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 978506 (24 March 2016); doi: 10.1117/12.2216050; https://doi.org/10.1117/12.2216050
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