Panatomic Imaging–Derived 4D Hemodynamics Without Specialized Scans
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Solution Overview
Problem
The reliable measurement and calculation of hemodynamic parameters for vascular diseases are challenging due to limited availability of appropriate equipment, high cost, time-consuming data analysis, and lack of local expertise, limiting their application in most medical centers.
Innovation Solution
A method using deep learning networks to derive 4D hemodynamic parameters from standard anatomic imaging data, such as CTA or MRA, by training the networks with 4D flow MRI data to generate spatially and temporally resolved blood flow velocities, enabling visualization and quantification of hemodynamic metrics like peak velocity, wall shear stress, and kinetic energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If specialized hemodynamic imaging equipment and software are used, then measurement precision of hemodynamic parameters is improved, but device complexity and cost increase
Solution Approach 1:
The patent creates a virtual copy of hemodynamic data by training deep learning models on standardized imaging data to predict 4D flow MRI hemodynamic parameters. This allows conventional imaging equipment to generate hemodynamic information without requiring specialized hemodynamic imaging devices, thus maintaining measurement precision while reducing device complexity
Solution Approach 2:
The patent replaces complex specialized hemodynamic imaging equipment with standard imaging devices combined with artificial intelligence algorithms. The deep learning models substitute for specialized hardware by computationally deriving hemodynamic parameters from conventional imaging data, eliminating the need for dedicated hemodynamic imaging equipment
2Measurement precision
If dedicated hemodynamic imaging is performed, then measurement precision is improved, but loss of time increases due to additional imaging acquisition
Solution Approach 1:
The patent merges hemodynamic parameter derivation with standard imaging workflows by using the same imaging data for both anatomical assessment and hemodynamic quantification. The deep learning model processes standard imaging data to simultaneously provide anatomical information and hemodynamic parameters, eliminating the need for separate dedicated hemodynamic imaging sessions
Solution Approach 2:
The patent creates virtual hemodynamic data from standard imaging through deep learning, allowing hemodynamic assessment to be performed as a computational post-processing step rather than requiring additional time-consuming imaging acquisitions. This copying approach generates hemodynamic information on-demand without extending scan time
3Measurement precision
If specialized equipment and expertise are used, then measurement precision is improved, but ease of operation deteriorates due to limited availability
Solution Approach 1:
The patent makes hemodynamic quantification universal by developing deep learning models that can be deployed on standard imaging equipment and infrastructure. The system processes conventional imaging data through AI algorithms to provide hemodynamic parameters at any medical center with standard imaging capabilities, eliminating the need for specialized equipment and local expertise
Solution Approach 2:
The patent introduces artificial intelligence algorithms as an intermediary between standard imaging data and hemodynamic parameter extraction. This AI mediator enables centers without specialized hemodynamic imaging expertise to obtain accurate hemodynamic measurements by processing conventional imaging data through trained deep learning models
4Measurement precision
If comprehensive hemodynamic analysis is performed, then measurement precision is improved, but loss of time increases due to cumbersome data analysis
Solution Approach 1:
The patent replaces manual and computationally intensive hemodynamic analysis with automated deep learning models. The AI system performs comprehensive 4D hemodynamic quantification including velocity fields, wall shear stress, and kinetic energy calculations automatically, substituting for time-consuming traditional analysis methods and providing results rapidly
Solution Approach 2:
The patent implements self-service hemodynamic analysis where the deep learning model automatically processes imaging data and generates comprehensive hemodynamic parameters without requiring expert intervention. The system performs 3D segmentation, velocity field derivation, and hemodynamic quantification autonomously, eliminating the need for time-consuming manual analysis by specialists
Data Source
AI summary
A method for non-invasive assessment of vascular 4D hemodynamics includes receiving standard anatomic imaging data at a local network or cloud-based analysis platform and identifying a vessel of interest from the received anatomic imaging data. The method also includes deriving hemodynamic features from the vessel of interest from the received anatomic imaging data using deep learning by inputting the received anatomic imaging data into a deep learning network. The method further includes calculating 4D hemodynamic parameters and generating output data based on the hemodynamic features derived from the vessel of interest.