Abstract
Tetralogy of Fallot (TOF) is the most common complex congenital heart disease (CHD) of the cyanotic type. Studies on ventricular functions have received an increasing amount of attention as the development of diagnosis and treatment technology for CHD continues to advance. Reasonable options for imaging examination and accurate assessment of preoperative and postoperative left ventricular functions of TOF patients are important in improving the cure rate of TOF radical operation, therapeutic evaluation, and judgment prognosis. Therefore, with the aid of dual-source computed tomography (DSCT), cardiac images with high temporal resolution and high definition, we measured the left ventricular time-volume curve using image data and calculating the left ventricular function parameters to conduct the preliminary evaluation on TOF patients. To comprehensively evaluate the cardiac function, the segmental ventricular wall function parameters were measured, and the measurement results were mapped to a bull's eye diagram to realize the standardization of segmental ventricular wall function evaluation. Finally, we introduced a new clustering method based on auto-regression model parameters and combined this method with Euclidean distance measurements to establish an intelligent diagnosis of TOF. The results of this experiment show that the TOF evaluation and the intelligent diagnostic methods proposed in this article are feasible.
Published Version
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