Detecting Infarct Region in Cardiac Magnetic Resonance Images Through Weighted Normalized Mutual Information
Abstract:
Background
Myocardial infarction remains a leading cause of morbidity and mortality among cardiac disease. Cardiac wall thickening in patients with myocardial infarction is less than healthy individuals. Accurate measurement of cardiac wall fractional thickening and path-length of myocardium points in healthy data and patients with myocardial infarction can help physicians in diagnosing the affected area.
Patients and
Methods
Epi/Endocardium of all slices in end-diastole frame were segmented, then more than 150 points in each slice were selected to track by weighted normalized mutual information algorithm over all frames. Weighted normalized mutual information was computed between two three-dimensional masks sized 3× 3 × 3, pixel that were located in end-diastole and subsequent frames centroid of the selected points. Finally, by computing the distance between endocardium and epicardium in each slice over all frames, cardiac wall thickness and fractional thickening was measured. Moreover, the path-length of each data point during cardiac period was calculated and sketched in bulls-eye format. Evaluation of the method was done by ten healthy and twenty patients with myocardial infarction.
Results
Cardiac wall kinesis was evaluated by normalized path length, which was presented in standard 17-segment bull’s-eye format. Wall thickness and fractional wall thickening for all slices over all frames were extracted in order to determine the infarct region. Infarct regions had minimal fractional thickening and normalized path length. All evaluations demonstrated hypo-kinesis in the damaged region.
Conclusion
Evaluation of obtained results showed significant difference between local parameters of healthy and infarcted myocardium. In all patients, the process was able to precisely determine the affected region that was all well matched with clinical evidence.
Language:
English
Published:
Iranian Journal of Radiology, Volume:14 Issue: 3, 2017
Page:
11
magiran.com/p1750627  
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