Next-Generation Phase Unwrapping for Dense MRI using Deep Learning
Legal Citation
Summary of the Inventive Concept
A novel system and method for real-time phase unwrapping for dense MRI using deep learning, enabling accurate and efficient cardiac strain analysis.
Background and Problem Solved
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.
Detailed Description of the Inventive Concept
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.
Novelty and Inventive Step
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 and Variations
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.
Potential Commercial Applications and Market
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.
Section 103 Obviousness Analysis (PHOSITA)
Field of Art
Medical imaging, specifically cardiac magnetic resonance imaging (MRI) with a focus on deep learning-based image processing and strain analysis techniques
Person of Ordinary Skill (PHOSITA) Profile
A skilled practitioner with expertise in medical image processing, deep learning neural networks, signal processing for MRI, and cardiac imaging analysis, typically holding a PhD in biomedical engineering, medical physics, or computer science with specialized training in machine learning and medical image analysis
Obviousness Rationale
A person having ordinary skill in the art would recognize that the published technical disclosure represents predictable extensions of the source patent's deep learning approach to phase unwrapping in cardiac MRI. The variations introduce incremental improvements in neural network architecture, real-time processing, and personalized analysis that would be considered routine optimization techniques within the field of medical image processing and deep learning. The core technical problem of phase unwrapping and strain analysis remains consistent, with the PTD offering straightforward algorithmic and architectural enhancements.
Obvious Combinations & Variations
Original Patent Information
| Patent Number | US 11,857,288 |
|---|---|
| Title | Systems and methods for phase unwrapping for dense MRI using deep learning |
| Assignee(s) | University of Virginia Patent Foundation |