Hybrid MRI Reconstruction for Non-Contrast Angiography
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional non-contrast MRI angiography techniques face challenges in achieving optimal artery-to-background contrast without venous contamination, requiring calibration of flow-spoiling dephasing gradient moments, which is time-consuming and prone to errors, and may result in sub-optimal vasculature depiction due to varying flow rates across the imaging volume.
Innovation Solution
A hybrid image reconstruction algorithm combining multiple MRI data sets acquired with different flow-dephasing gradient moments and phases within the cardiac cycle to maximize arterial signal while minimizing venous contamination, eliminating the need for calibration and improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If conventional non-contrast MRI angiography uses flow-spoiling dephasing gradient moments to achieve artery-to-background contrast, then arterial signal enhancement is improved, but venous contamination increases and calibration complexity increases
Solution Approach 1:
The patent segments the vascular signal into arterial and venous components by acquiring multiple data sets with different flow-dephasing gradient moments. Each gradient moment selectively suppresses different flow velocities, allowing separation of arterial (fast flow) and venous (slow flow) signals through multi-parameter acquisition and hybrid reconstruction
Solution Approach 2:
The patent changes the flow-dephasing gradient moment parameter across multiple acquisitions to differentiate arterial and venous signals. By varying this parameter and combining the results through hybrid reconstruction, the system optimizes arterial enhancement while suppressing venous contamination without requiring manual calibration
2Manufacturing precision
If conventional techniques calibrate flow-spoiling dephasing gradient moments to optimize contrast, then image quality may be improved, but processing time increases and operator error risk increases
Solution Approach 1:
The system performs self-calibration by automatically acquiring multiple data sets with predetermined gradient moments and using hybrid reconstruction algorithms to optimize contrast. This eliminates the need for manual operator calibration while maintaining or improving image quality, thereby reducing processing time and eliminating operator error
Solution Approach 2:
The patent implements preliminary action by acquiring multiple data sets with different flow-dephasing gradient moments before final image reconstruction. This pre-acquisition of varied parameter data enables automatic optimization through hybrid reconstruction, eliminating the need for time-consuming manual calibration during the imaging process
3Productivity
If conventional MRI uses single gradient moment acquisition to reduce scan time, then productivity is improved, but vasculature depiction quality deteriorates due to varying flow rates
Solution Approach 1:
The patent applies multi-functionality by using multiple flow-dephasing gradient moments within a single imaging protocol. Each gradient moment targets different flow velocity ranges, allowing the system to universally depict both fast-flowing arteries and slow-flowing veins in one scan, improving overall vasculature depiction quality without requiring separate scans
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The hybrid approach enhances arterial signal by 80-100% while reducing venous contamination to almost zero, providing robust and efficient non-contrast MRA images without the need for precise calibration of gradient moments.
Implementation Method 1
utilizing flow-spoiled dephasing (FSD) MRA with non-zero first moment gradient (m1) to dephase flowing spins
Implementation Method 2
A method and system for magnetic resonance (MR) imaging using a hybrid image
Data Source
AI summary
Magnetic resonance images (MRI) are generated by acquiring a plurality of N>2 image data sets for an imaged patient volume using respectively corresponding different data acquisition imaging parameters. At least one hybrid image data set X is generated for the imaged patient volume based on a combination of at least a subset of the plurality of image data sets. If desired, a further subtraction image (e.g., MRA) data set is generated based on a difference between the at least one hybrid image data set and another image data set, and the subtraction image data set, which may, depending upon implementation, optimize flowing fluids such as blood within arteries or veins, CSF, etc within the imaged patent volume, is output for storage or display as an MR image of the imaged patient volume.


