UNLABELLED: Magnetic resonance (MR) diagnosis of regional left ventricular (LV) dysfunction relies on visual interpretation of cine images that suffers from wide inter-observer variability, especially when performed by readers not specifically trained in the assessment of LV wall motion. Quantitative analysis tools, though widely available, are rarely used because they provide large amounts of detailed information, the interpretation of which requires additional time-consuming processing. We tested the feasibility of fast automated interpretation of regional LV function using computer analysis of this wall motion information. METHODS: Dynamic, ECG-gated, steady-state free precession short-axis images were obtained in 6-10 slices in 28 subjects (10 normal volunteers; 18 patients). Images were reviewed by an expert cardiologist who provided "gold standard" grades (normal, abnormal) for regional wall motion and, independently, by four radiologists. Same images were then analyzed using custom software. Regional fractional area changes computed in normal volunteers were used to obtain the optimal segment- and slice-specific threshold values for automated classification of regional wall motion for each patient. The levels of agreement with the "gold standard" grades were compared between the radiologists and the automated interpretation. RESULTS: While the visual interpretation required 2-5 minute per patient, the automated interpretation required < 1 sec, after endocardial border detection was complete. The automated interpretation resulted in higher sensitivity, specificity, and accuracy (84%, 77%, 79%, respectively) than the radiologists' grades (80%, 76%, 77%, respectively) and eliminated the high interobserver variability. CONCLUSION: Once the endocardial boundaries are defined, computer analysis of the regional wall motion information allows accurate, fully automated, immediate, objective and experience-independent interpretation of regional LV function.
UNLABELLED: Magnetic resonance (MR) diagnosis of regional left ventricular (LV) dysfunction relies on visual interpretation of cine images that suffers from wide inter-observer variability, especially when performed by readers not specifically trained in the assessment of LV wall motion. Quantitative analysis tools, though widely available, are rarely used because they provide large amounts of detailed information, the interpretation of which requires additional time-consuming processing. We tested the feasibility of fast automated interpretation of regional LV function using computer analysis of this wall motion information. METHODS: Dynamic, ECG-gated, steady-state free precession short-axis images were obtained in 6-10 slices in 28 subjects (10 normal volunteers; 18 patients). Images were reviewed by an expert cardiologist who provided "gold standard" grades (normal, abnormal) for regional wall motion and, independently, by four radiologists. Same images were then analyzed using custom software. Regional fractional area changes computed in normal volunteers were used to obtain the optimal segment- and slice-specific threshold values for automated classification of regional wall motion for each patient. The levels of agreement with the "gold standard" grades were compared between the radiologists and the automated interpretation. RESULTS: While the visual interpretation required 2-5 minute per patient, the automated interpretation required < 1 sec, after endocardial border detection was complete. The automated interpretation resulted in higher sensitivity, specificity, and accuracy (84%, 77%, 79%, respectively) than the radiologists' grades (80%, 76%, 77%, respectively) and eliminated the high interobserver variability. CONCLUSION: Once the endocardial boundaries are defined, computer analysis of the regional wall motion information allows accurate, fully automated, immediate, objective and experience-independent interpretation of regional LV function.
Authors: Hans Jaochim Nesser; Lissa Sugeng; Cristiana Corsi; Lynn Weinert; Johannes Niel; Christian Ebner; Regina Steringer-Mascherbauer; Frank Schmidt; Georg Schummers; Roberto M Lang; Victor Mor-Avi Journal: Heart Date: 2006-09-15 Impact factor: 5.994