Next-Generation Phase Unwrapping for Dense MRI using Deep Learning

A novel system and method for real-time phase unwrapping for dense MRI using deep learning, enabling accurate and efficient cardiac strain analysis.

The original patent, 'Systems and methods for phase unwrapping for dense MRI using deep learning', has limitations in terms of processing speed and accuracy. The new inventive concept addresses these limitations by introducing real-time phase unwrapping capabilities, improved spatial and temporal dependencies learning, and personalized cardiac strain analysis.

The new inventive concept comprises a neural network trained on a dataset of cardiac MRI images to predict phase unwrapping labels. The neural network is adapted to learn spatial and temporal dependencies in the cardiac MRI images, enabling accurate and efficient phase unwrapping. The system further includes a processing unit configured to receive the phase unwrapped MRI images and apply a deep learning model to generate a cardiac strain map. Additionally, the inventive concept enables personalized cardiac strain analysis using patient-specific cardiac strain analysis models.

The new claims introduce real-time phase unwrapping capabilities, improved spatial and temporal dependencies learning, and personalized cardiac strain analysis, which are not present in the original patent. The inventive concept's ability to learn spatial and temporal dependencies in cardiac MRI images and generate accurate phase unwrapped images in real-time constitutes a significant improvement over the original patent.

Alternative embodiments of the inventive concept could include the use of different deep learning architectures, such as transformers or graph neural networks, to improve the accuracy and efficiency of phase unwrapping. Additionally, the inventive concept could be adapted for use with other medical imaging modalities, such as CT or ultrasound.

The inventive concept has significant commercial potential in the medical imaging and diagnostics industry, particularly in the areas of cardiac strain analysis and personalized medicine. The ability to perform real-time phase unwrapping and generate accurate cardiac strain maps could revolutionize the field of cardiac imaging, enabling faster and more accurate diagnoses.