Neural Network Correction of Medical Projection Data

The patent applies preliminary action by pre-training neural network models with large amounts of projection data and correction parameters before actual use. The neural network learns optimal correction strategies during the training phase, so that during actual correction operations, the pre-learned knowledge can be rapidly applied without requiring time-consuming iterative calculations, thus resolving the contradiction between accuracy and speed.

The patent replaces traditional mechanical correction algorithms (which rely on iterative mathematical computations) with a neural network-based system. The neural network, after being trained with sufficient data, can perform corrections in a single pass without requiring repeated iterative calculations, thereby dramatically reducing processing time while maintaining correction accuracy through the learned patterns.

The patent applies parameter changes by training the neural network to learn optimal correction parameters (such as correction coefficients and weighting factors) from training data. Instead of using fixed or manually tuned parameters, the neural network dynamically determines the best parameters based on the input projection data characteristics, thereby improving image quality while maintaining high correction efficiency.

The neural network performs self-service by automatically learning correction strategies from training data without requiring manual intervention or iterative adjustment during the correction process. The model self-optimizes its parameters during training and then autonomously applies corrections to new data, eliminating the need for time-consuming manual parameter tuning and improving both image quality and processing efficiency.

The patent replaces complex mechanical correction algorithms with a neural network system. Although the neural network training process is complex, the actual correction operation becomes simple and fast once trained. The neural network encapsulates the complex correction logic within its trained weights and biases, allowing simple forward propagation to achieve high-quality corrections without requiring complex runtime computations.

The patent applies preliminary action by performing the complex algorithmic work during the offline training phase rather than during online correction operations. The neural network learns complex correction patterns from training data, and this pre-learned knowledge is then applied efficiently during actual use, separating the complexity of learning from the simplicity of application.