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A method for automatic edge detection and volume computation of the left ventricle from ultrafast computed
E L Dove1, K Philip, N L Gotteiner
1Department of Biomedical Engineering, University of Iowa, Iowa City 52245.
Insights
An automated algorithm accurately detects left ventricular borders from cardiac CT images, enabling precise calculation of chamber volumes and ejection fractions. This tool aids clinicians by reducing errors and saving time in cardiac imaging analysis.
Area of Science:
- Cardiovascular Imaging
- Medical Image Analysis
- Computational Cardiology
Background:
- Manual detection of left ventricular (LV) endocardial and epicardial borders is time-consuming and prone to errors.
- Accurate LV volume and ejection fraction calculation is crucial for diagnosing and managing cardiac conditions.
Purpose of the Study:
- To develop and validate an automatic border detection algorithm for left ventricular analysis using ultrafast computed tomographic images.
- To assess the accuracy of the algorithm in computing cardiac chamber volumes and ejection fractions compared to manual methods.
Main Methods:
- An algorithm was developed using Fuzzy Hough Transform, region-growing, and optimal border detection techniques.
- The algorithm automatically identifies LV endocardial and epicardial borders from basal to apical levels on cardiac CT scans.
- Computed areas and volumes were compared with manual tracings in canine hearts and clinical patient studies.
Main Results:
- The algorithm showed good correlation with manual measurements for endocardial areas (r=0.95) and ventricular volumes (r=0.94).
- Overestimation of LV epicardial area and underestimation of ejection fraction were observed but not statistically significant.
- The automated method provided accurate estimations of cardiac areas and volumes, comparable to manual analysis.
Conclusions:
- Automatic myocardial border detection is a valuable tool for clinicians to calculate LV chamber volumes and ejection fractions.
- The validated algorithm offers an accurate and efficient method for cardiac image analysis.
- This technique holds potential for application across various cardiac imaging modalities and other anatomical 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.