Dynamic Contrast-Enhanced MRI Using Compressed Sensing
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Solution Overview
Problem
Current dynamic contrast-enhanced magnetic resonance angiography (CE-MRA) techniques face challenges in achieving high temporal and spatial resolution simultaneously due to limitations in image acquisition speed, resulting in temporal blurring of rapidly changing vascular events and lower spatial resolution than conventional CE-MRA.
Innovation Solution
The implementation of a magnitude subtraction-based compressed sensing algorithm for image reconstruction, which highly subsamples the acquisition and uses a Poisson-disk random sampling pattern in k-space, allowing for higher acceleration and improved image quality by minimizing the L1 norm of the pixel-wise magnitude difference between successive temporal frames, thereby enhancing sparsity and reducing computation costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If conventional dynamic CE-MRA sequences (e.g., TWIST) are used to achieve high temporal resolution, then temporal resolution is improved, but spatial resolution deteriorates
Solution Approach 1:
The patent changes the fundamental approach from view-sharing with temporal blurring to compressed sensing with random k-space sampling. By using L1-norm minimization and total variation regularization, the method reconstructs high-spatial-resolution images from highly subsampled data without temporal blurring, achieving both high temporal and spatial resolution simultaneously
Solution Approach 2:
The patent replaces the mechanical view-sharing technique with a computational compressed sensing reconstruction approach. Instead of physically sharing k-space views across time frames, the method uses iterative optimization algorithms to reconstruct images from randomly sampled k-space data, eliminating the need for temporal blurring
2Manufacturing precision
If high spatial resolution CE-MRA is acquired, then spatial resolution is improved, but acquisition time increases
Solution Approach 1:
The patent applies partial sampling in k-space by randomly selecting only a fraction of k-space lines for acquisition. The compressed sensing reconstruction algorithm recovers the full high-resolution image from this partial data, achieving high spatial resolution with significantly reduced acquisition time compared to full k-space sampling
Solution Approach 2:
The patent transforms the sampling strategy from systematic grid sampling to random Poisson-disk sampling. This parameter change in the sampling pattern, combined with L1-norm minimization reconstruction, enables efficient recovery of high-resolution images from highly subsampled data, reducing acquisition time while maintaining spatial resolution
3Speed
If view-sharing technique is used to reduce temporal footprint, then temporal resolution is improved, but temporal blurring occurs
Solution Approach 1:
The patent replaces the view-sharing mechanical technique with compressed sensing computational reconstruction. Instead of sharing views across time frames (which causes temporal blurring), the method uses random k-space sampling with L1-norm minimization to reconstruct each time frame independently from its own subsampled data, preserving temporal information without blurring
Solution Approach 2:
The patent introduces an intermediary computational reconstruction process that acts as a mediator between the subsampled k-space data and the final image. The L1-norm minimization algorithm with total variation regularization serves as this intermediary, recovering high-quality images with preserved temporal information without requiring view-sharing
Data Source
AI summary
A method for high spatial and temporal resolution dynamic contrast enhanced magnetic resonance imaging using a random subsampled Cartesian k-space using a Poisson-disk random pattern acquisition strategy and a compressed sensing reconstruction algorithm incorporating magnitude image subtraction is presented. One reconstruction uses a split-Bregman minimization of the sum of the L1 norm of the pixel-wise magnitude difference between two successive temporal frames, a fidelity term and a total variation (TV) sparsity term.