OBJECT: Although, there is no global consensus on their measurement, magnetic resonance imaging (MRI) appears to be particularly attractive for the study of the sinuses of Valsalva (SV). The purpose of this study was to automatically evaluate the SV from cine-MRI using a standardized method. MATERIALS AND METHODS: An automatic method based on mathematical morphology was elaborated to segment the aortic root from cross-sectional cine-MRI, and to detect relevant points, such as the commissures, the cusps and the centre of the SV. The distances between these points allow a metric evaluation of the SV. Our method was tested on synthesized data and 41 patient data sets and radii calculations were compared with manual processing. RESULTS: On the patient data sets, there are excellent correlation and concordance between manual and automatic measurements for images at diastole (r=0.97; y=0.97x+0.57; P<10(-5); mean of differences=0.2 mm; standard deviation of differences=2.0 mm) and at systole (r=0.96; y=0.96x+1.2; P<10(-5); mean of differences<0.1 mm; standard deviation of differences=2.4 mm). CONCLUSION: Our automatic method provides reliable morphometric evaluation of the SV. Measures of distances between relevant points allow a precise evaluation of each cusp of the SV. This robust evaluation can be helpful in the follow-up of patients with aortic root diseases.
OBJECT: Although, there is no global consensus on their measurement, magnetic resonance imaging (MRI) appears to be particularly attractive for the study of the sinuses of Valsalva (SV). The purpose of this study was to automatically evaluate the SV from cine-MRI using a standardized method. MATERIALS AND METHODS: An automatic method based on mathematical morphology was elaborated to segment the aortic root from cross-sectional cine-MRI, and to detect relevant points, such as the commissures, the cusps and the centre of the SV. The distances between these points allow a metric evaluation of the SV. Our method was tested on synthesized data and 41 patient data sets and radii calculations were compared with manual processing. RESULTS: On the patient data sets, there are excellent correlation and concordance between manual and automatic measurements for images at diastole (r=0.97; y=0.97x+0.57; P<10(-5); mean of differences=0.2 mm; standard deviation of differences=2.0 mm) and at systole (r=0.96; y=0.96x+1.2; P<10(-5); mean of differences<0.1 mm; standard deviation of differences=2.4 mm). CONCLUSION: Our automatic method provides reliable morphometric evaluation of the SV. Measures of distances between relevant points allow a precise evaluation of each cusp of the SV. This robust evaluation can be helpful in the follow-up of patients with aortic root diseases.