Integrative Cardiac Strain Analysis Systems and Methods

Publication ID: 24-11857288_0008_PTD
Published: November 07, 2025
Category:Synergistic Combinations

Legal Citation

pr1or.art Inc., “Integrative Cardiac Strain Analysis Systems and Methods,” Published Technical Disclosure No. 24-11857288_0008_PTD, Published November 07, 2025, available at https://archive.pr1or.art/24-11857288_0008_PTD
This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,857,288.

Summary of the Inventive Concept

This inventive concept integrates deep learning-based phase unwrapping for dense MRI with other distinct technologies, such as AI, IoT, blockchain, and new materials, to create a more powerful system for cardiac strain analysis.

Background and Problem Solved

The original patent addressed the limitations of myocardial strain imaging by utilizing deep learning-based phase unwrapping for dense MRI. However, this approach had limited scope and did not leverage the potential of other technologies. This new inventive concept solves this problem by synergistically combining the patented inventive concept with other distinct technologies to create a more comprehensive and powerful system for cardiac strain analysis.

Detailed Description of the Inventive Concept

The new system integrates a deep learning-based phase unwrapping module with a blockchain-based data sharing platform for secure and transparent data exchange between healthcare providers. Additionally, it utilizes an IoT-enabled wearable device for real-time monitoring of cardiac health, an AI-powered decision support module, and a new material-based MRI coil for enhanced signal quality. This synergistic combination enables more accurate and personalized cardiac strain analysis, improved data security, and enhanced patient care.

Novelty and Inventive Step

The new claims introduce novel and non-obvious combinations of the patented inventive concept with other distinct technologies, such as blockchain, IoT, AI, and new materials. These combinations provide a more comprehensive and powerful system for cardiac strain analysis, overcoming the limitations of the original patent.

Alternative Embodiments and Variations

Alternative embodiments may include integrating the deep learning-based phase unwrapping module with other technologies, such as cloud-based analytics platforms, patient-specific 3D printed heart models, or edge computing devices. Variations may also include using different AI architectures, IoT devices, or blockchain protocols to achieve similar goals.

Potential Commercial Applications and Market

This inventive concept has significant commercial potential in the healthcare industry, particularly in cardiac imaging and personalized medicine. The target market includes hospitals, research institutions, and medical device manufacturers seeking to improve cardiac strain analysis and patient care.

Field of Art

Medical imaging, specifically cardiac MRI and deep learning-based image processing techniques, with expertise in signal processing, machine learning, and medical diagnostic technologies

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with advanced degrees in biomedical engineering, medical imaging, or computer science, possessing knowledge of deep learning neural networks, MRI signal processing, and cardiac imaging techniques

Obviousness Rationale

A person having ordinary skill in the art would recognize that integrating the source patent's deep learning phase unwrapping technique with complementary technologies like blockchain, IoT, and AI represents a predictable combination of known elements to enhance medical imaging and data processing capabilities. The PTD's variations leverage standard techniques of technology integration and system enhancement that would be apparent to a skilled practitioner seeking to improve cardiac strain analysis methodologies.

Obvious Combinations & Variations

Source Patent Element
U-Net structured CNN for phase unwrapping in DENSE MRI
PTD Variation
Integrating U-Net CNN with IoT-enabled wearable device for real-time cardiac monitoring
Obviousness Reasoning
Combining machine learning image processing with real-time monitoring is a predictable extension using known IoT and AI technologies to enhance diagnostic capabilities
Source Patent Element
Displacement encoded MRI data acquisition method
PTD Variation
Adding blockchain-based data sharing platform for secure healthcare data exchange
Obviousness Reasoning
Implementing secure data transmission protocols is a standard approach to improving medical data management, representing an obvious design choice for protecting sensitive medical information
Source Patent Element
Cardiac strain analysis using deep learning techniques
PTD Variation
Incorporating AI-powered decision support module and advanced MRI coil materials
Obviousness Reasoning
Enhancing diagnostic systems with supplementary AI analysis and improved hardware represents a routine optimization approach familiar to medical imaging researchers
Source Patent Element
Phase unwrapping method for cardiac MRI
PTD Variation
Creating patient-specific 3D printed heart models for strain prediction
Obviousness Reasoning
Personalized medical modeling using 3D printing is a known technique for improving diagnostic visualization and represents an obvious extension of existing imaging technologies
Source Patent Element
Deep learning-based image processing for cardiac strain
PTD Variation
Implementing cloud-based analytics platform for remote data processing
Obviousness Reasoning
Utilizing cloud computing for medical image analysis is a standard approach to expanding computational capabilities and represents a predictable technological integration
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857288 and the disclosed variations, a person having ordinary skill in the art would find the proposed technical combinations obvious and predictable, thereby rendering potential patent claims covering similar systems and methods unpatentable under 35 U.S.C. ยง 103 due to the straightforward integration of known technologies in a manner that would be apparent to a skilled practitioner in medical imaging and machine learning.

Original Patent Information

Patent NumberUS 11,857,288
TitleSystems and methods for phase unwrapping for dense MRI using deep learning
Assignee(s)University of Virginia Patent Foundation