MRI Neural Network Hallucination Detection

The patent implements a feedback mechanism where the neural network's output is evaluated by comparing it against the original input image and ground truth data. The loss function incorporates both reconstruction accuracy and hallucination detection, allowing the network to learn from errors and continuously improve its output reliability while maintaining processing speed.

The patent introduces an intermediary evaluation module that acts as a mediator between the neural network output and the final image. This module includes a hallucination detector that identifies artificial structures and a loss function that penalizes hallucinations, effectively filtering out unreliable generated content while preserving the speed benefits of neural network processing.

The patent converts the harmful effect of hallucinations into a beneficial training signal. By incorporating a hallucination detection mechanism into the loss function, the network learns to recognize and avoid generating artificial structures. The hallucination detector's ability to identify false structures is transformed into a teaching tool that guides the network to produce more accurate corrections without introducing spurious features.

The patent creates a composite training framework that combines multiple loss components: reconstruction loss for image quality, hallucination detection loss for reliability, and consistency loss for maintaining fidelity to the original image. This composite approach integrates multiple objectives into a unified training process, enabling the network to achieve both high correction quality and low hallucination rates simultaneously.