Deep Learning-Based Phase Unwrapping for Diverse Applications
Legal Citation
Summary of the Inventive Concept
The inventive concept applies the core technology of deep learning-based phase unwrapping for dense MRI to entirely new industries, including agriculture, construction, industrial processes, pharmaceuticals, and neuroscience.
Background and Problem Solved
The original patent addressed the challenge of myocardial strain imaging in cardiac MRI using deep learning-based phase unwrapping. However, this technology has broader applications beyond cardiac imaging. The inventive concept solves the problem of limited application scope by extending the technology to various fields, enabling new use cases and applications.
Detailed Description of the Inventive Concept
The inventive concept leverages the deep learning-based phase unwrapping methodology to analyze displacement encoded MRI data in diverse contexts. For instance, in agriculture, the technology can monitor and predict crop yields by analyzing soil moisture and crop growth. In construction, it can detect early signs of structural damage in buildings by analyzing displacement encoded MRI data of building materials and structures. Similarly, in industrial processes, it can analyze fluid dynamics, and in pharmaceuticals, it can monitor product consistency and purity. In neuroscience, it can non-invasively monitor brain activity and function.
Novelty and Inventive Step
The inventive concept's novelty lies in its application of deep learning-based phase unwrapping to entirely new fields, which was not anticipated by the original patent. The inventive step is the recognition of the technology's broader applicability and its adaptation to diverse industries, resulting in new and non-obvious use cases.
Alternative Embodiments and Variations
Alternative embodiments of the inventive concept could include variations in the deep learning architecture, the type of MRI data used, or the specific application domains. For example, using different CNN structures or incorporating additional sensors could further enhance the technology's capabilities.
Potential Commercial Applications and Market
The inventive concept has significant commercial potential in various industries, including agriculture, construction, industrial processes, pharmaceuticals, and neuroscience. The target market includes companies and research institutions seeking innovative solutions for monitoring and analysis in these fields.
Section 103 Obviousness Analysis (PHOSITA)
Field of Art
Medical imaging, machine learning, and signal processing, with expertise in deep learning techniques for medical image analysis, particularly magnetic resonance imaging (MRI) and phase unwrapping algorithms
Person of Ordinary Skill (PHOSITA) Profile
A skilled practitioner with advanced degrees in biomedical engineering, computer science, or medical imaging, possessing expertise in convolutional neural networks, signal processing, and MRI data analysis techniques
Obviousness Rationale
A person having ordinary skill in the art would recognize that the deep learning-based phase unwrapping technique disclosed in the source patent represents a generalizable signal processing methodology that could be readily adapted to various domains requiring displacement or phase analysis. The core technical innovation of using convolutional neural networks to decode complex signal phase information is fundamentally transferable across different application contexts. The PTD demonstrates that the underlying machine learning approach can be systematically applied to analyze displacement-encoded data in multiple industries by leveraging the same core computational principles.
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 |