Ultrasonic Imaging Phase Compensation Neural Network

The patent applies preliminary action by pre-training a neural network model offline to learn phase compensation mappings. During actual ultrasonic imaging, the pre-trained model is directly applied to compensate phase distortions in channel data without requiring complex real-time calculations. This separates the complex training phase from the simple application phase, improving image quality while keeping the actual imaging process efficient.

The patent replaces traditional mechanical or algorithmic phase compensation methods with an intelligent neural network-based system. Instead of using complex signal processing algorithms or physical calibration procedures, the system uses a trained neural network to automatically learn and apply phase corrections, substituting conventional computational approaches with machine learning-based processing.

The neural network model is trained offline in advance using representative ultrasonic data. Once trained, the model contains learned phase compensation patterns that can be rapidly applied during actual imaging. This preliminary training phase shifts the computational burden to an offline setting, allowing fast real-time inference during ultrasonic imaging while maintaining high spatial resolution through accurate phase compensation.

The neural network learns to copy or replicate the complex phase compensation mappings from training data. Instead of performing complex calculations during imaging, the system uses the learned model to generate compensation patterns that replicate the效果 of extensive computational processing, achieving high spatial resolution with reduced real-time computational requirements.

The patent substitutes traditional signal processing methods for improving signal-to-noise ratio with a neural network-based approach. Instead of using complex filtering, averaging, or other conventional signal enhancement techniques, the system uses the trained neural network to automatically learn and apply phase corrections that inherently improve signal quality and contrast, replacing multiple processing steps with a single intelligent model.

The neural network model serves multiple functions simultaneously: it performs phase compensation, improves signal-to-noise ratio, enhances imaging contrast, and corrects focusing errors all through a single unified system. This multi-functionality reduces overall system complexity compared to implementing separate dedicated systems for each function.