Optimizing Cardiac Image Registration for Coronary Artery Disease Assessment: A Simulated Annealing Approach
Keywords:
Coronary Artery Disease, Prediction, Simulated Annealing Approach, Machine Learning, Disease AssessmentAbstract
One of the most important diagnostic procedures in contemporary medicine is the evaluation of coronary artery disease (CAD) by cardiac image registration. In this study, simulated annealing a potent optimisation technique motivated by the annealing process in metallurgyis used to optimize cardiac image registration. The goal is to improve picture alignment in order to increase the precision and effectiveness of CAD assessment.Comparing images collected at multiple times or using different imaging modalities, such angiography or MRI, requires cardiac image registration. For identifying changes in coronary arteries and determining the course of the disease, accurate alignment of these pictures is essential. The optimisation of registration parameters is difficult because conventional registration techniques frequently have trouble with the intricate deformations and non-linear transformations in cardiac pictures.A flexible optimisation approach with promise in many areas is simulated annealing. It is used in this study to register cardiac images with an emphasis on CAD evaluation. Simulated annealing searches the parameter space iteratively in search of the ideal set of transformation parameters to reduce registration error. The technique escapes local minima and converges to a global minimum, increasing the registration accuracy. This is accomplished by emulating the annealing process.The findings of this study show how the simulated annealing method can be used to optimise cardiac image registration for CAD evaluation. Our methodology outperforms traditional approaches in terms of accuracy and resilience, making it an important tool for physicians in the detection and monitoring of CAD. Through earlier and more precise diagnosis of coronary artery disease, this discovery has the potential to enhance patient outcomes and marks a significant advancement in the quality of cardiac image analysis.
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