MRI Spectrum Generation Using Morphology-Based Neural Estimation

The patent uses deep learning to create a neural network that copies the complex relationships between morphological parameters and spectral characteristics. The network is trained on measured spectrum data to learn the mapping relationships, enabling it to generate artificial spectra that accurately replicate real measurement characteristics without requiring actual physical measurement for each case.

The patent transforms the spectrum generation approach by changing from direct physical simulation to data-driven parameter transformation. The neural network learns to transform morphological correlation parameters into spectral parameters through training on measured data, allowing accurate spectrum generation by transforming input parameters rather than through physical simulation.

The patent replaces traditional physical simulation mechanisms with a neural network-based computational system. Instead of using complex physical models to simulate spectrum generation, the system uses a neural network that has learned the underlying relationships from training data, substituting mechanical/physical simulation with intelligent computational transformation.

The neural network acts as an intermediary between the morphological correlation parameters and the spectral output. It mediates the transformation by learning the complex mapping relationships from training data, enabling accurate spectrum generation without requiring direct physical simulation or complex analytical models.