Neural Network Spatial Coherence Function Inference

The patent creates a simplified copy or approximation of the traditional beamforming calculation through machine learning models. The neural network is trained to replicate the output of traditional spatial coherence calculations, providing a faster alternative that maintains sufficient accuracy for clinical applications while dramatically reducing computational time and resources

The patent performs preliminary action by training the neural network model in advance using datasets generated from traditional beamforming methods. This pre-training phase allows the model to learn the complex relationships in ultrasound data, enabling it to quickly predict spatial coherence functions during actual imaging without performing time-consuming traditional calculations in real-time

The machine learning model creates a computational copy that replicates the lesion detection functionality of traditional beamforming. By training on datasets from traditional methods, the model learns to identify patterns associated with lesions and mass differentiation, providing equivalent clinical utility with fraction of the computational resource consumption

The patent substitutes the mechanical computational system (traditional beamforming algorithms requiring extensive CPU/GPU resources) with an intelligent system (trained neural network) that has learned optimal detection patterns. This substitution replaces resource-intensive iterative calculations with efficient pattern recognition, dramatically reducing energy and computational resource usage while maintaining detection reliability