RATIONALE AND OBJECTIVES: Detection of endocardial and epicardial borders of the left ventricle (LV) using various imaging modalities is time-consuming and prone to interpretive error. An automatic border detection algorithm is presented that is used with ultrafast computed tomographic images of the heart to compute cavity volumes. METHODS: The basal-level slice is identified, and the algorithm automatically detects the endocardial and epicardial borders of images from the basal to the apical levels. From these, the ventricular areas and chamber volumes are computed. The algorithm uses the Fuzzy Hough Transform, region-growing schemes, and optimal border-detection techniques. The cross-sectional areas and the chamber volumes computed with this technique were compared with those from manually traced images using canine hearts in vitro (n = 8) and studies in clinical patients (n = 27). RESULTS: Though the correlation was good (r = .88), the algorithm overestimated the LV epicardial area by 4.8 +/- 6.4 cm2, though this error was not statistically different from zero (P > .05). There was no difference in endocardial areas (r = .95, P > .05). The algorithm tended to underestimate the end-diastolic volume (r = .94) and the end-systolic volume (r = .94), although these errors were not statistically different from zero (P > .05). The algorithm tended to underestimate the ejection fraction (r = .80), although this error was not statistically different from zero (P > .05). CONCLUSIONS: Automatic detection of myocardial borders provides the clinician with a useful tool for calculating chamber volumes and ejection fractions. The algorithm, with the corrections suggested, provides an accurate estimation of areas and volumes. This algorithm may be useful for contour border identification with ultrasound, positron-emission tomography, magnetic resonance imaging, and other imaging modalities in the heart, as well as other structures.
RATIONALE AND OBJECTIVES: Detection of endocardial and epicardial borders of the left ventricle (LV) using various imaging modalities is time-consuming and prone to interpretive error. An automatic border detection algorithm is presented that is used with ultrafast computed tomographic images of the heart to compute cavity volumes. METHODS: The basal-level slice is identified, and the algorithm automatically detects the endocardial and epicardial borders of images from the basal to the apical levels. From these, the ventricular areas and chamber volumes are computed. The algorithm uses the Fuzzy Hough Transform, region-growing schemes, and optimal border-detection techniques. The cross-sectional areas and the chamber volumes computed with this technique were compared with those from manually traced images using canine hearts in vitro (n = 8) and studies in clinical patients (n = 27). RESULTS: Though the correlation was good (r = .88), the algorithm overestimated the LV epicardial area by 4.8 +/- 6.4 cm2, though this error was not statistically different from zero (P > .05). There was no difference in endocardial areas (r = .95, P > .05). The algorithm tended to underestimate the end-diastolic volume (r = .94) and the end-systolic volume (r = .94), although these errors were not statistically different from zero (P > .05). The algorithm tended to underestimate the ejection fraction (r = .80), although this error was not statistically different from zero (P > .05). CONCLUSIONS: Automatic detection of myocardial borders provides the clinician with a useful tool for calculating chamber volumes and ejection fractions. The algorithm, with the corrections suggested, provides an accurate estimation of areas and volumes. This algorithm may be useful for contour border identification with ultrasound, positron-emission tomography, magnetic resonance imaging, and other imaging modalities in the heart, as well as other structures.
Authors: P Brigger; S L Bacharach; G Srinivasan; K A Nour; J A Carson; V Dilsizian; A Aldroubi; M Unser Journal: J Nucl Cardiol Date: 1999 May-Jun Impact factor: 5.952
Authors: M Potse; R Hoekema; A C Linnenbank; A SippensGroenewegen; J Strackee; Bakker J M T de; C A Grimbergen Journal: Med Biol Eng Comput Date: 2002-01 Impact factor: 2.602