Hyperpolarized Xenon-129 MRI Gas-Phase Contamination Correction
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Solution Overview
Problem
Current MRI techniques face challenges in effectively isolating and imaging hyperpolarized Xenon-129 dissolved-phase components due to gas-phase contamination, which leads to image artifacts and errors in pulmonary gas exchange measurements.
Innovation Solution
A method involving the acquisition of multiple echo times for both gas-phase and dissolved-phase datasets, allowing for the estimation and correction of gas-phase contamination using a scaling factor, thereby isolating and reducing gas-phase interference in MRI images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If selective excitation of dissolved phase is used to conserve HP magnetization and isolate TP and RBC components, then imaging specificity is improved, but gas phase contamination of dissolved phase signal remains substantial due to tradeoffs between RF pulse profile, amplifier performance, and echo time
Solution Approach 1:
The patent segments the imaging process into multiple echo time acquisitions, separating gas phase and dissolved phase signal capture into distinct temporal phases. This allows independent optimization of excitation parameters for each phase while maintaining the ability to reconstruct pure dissolved phase images through subsequent processing.
Solution Approach 2:
The patent performs preliminary calibration scans to characterize the actual gas phase contamination present in the system before acquiring the main dissolved phase images. This measured contamination profile is then used to correct the dissolved phase images, removing artifacts without requiring perfect selective excitation during the main imaging phase.
2Object-affected harmful factors
If calibration techniques are used to shift gas phase frequency to a local null in the RF pulse stop band, then gas phase excitation is reduced, but the approach depends upon the stability and linearity of the RF amplifier and is hard to predict precise hardware performance
Solution Approach 1:
The patent implements feedback by performing calibration scans that measure the actual gas phase signal present in the system. This measured signal characteristics are then fed into the image reconstruction process, where the known contamination profile is used to mathematically remove the gas phase contribution from dissolved phase images, making the system robust to hardware variations.
Solution Approach 2:
The patent changes the approach from trying to control excitation parameters (RF pulse frequency, amplitude) to instead controlling the image reconstruction parameters. By measuring the actual contamination and using this information in the reconstruction algorithm, the system achieves reliable gas phase removal without depending on precise control of hardware parameters.
3Measurement precision
If the large frequency shift between gaseous and dissolved HP 129Xe phases is utilized in IDEAL multi-component signal model, then three separate components can be modeled, but non-Cartesian k-space trajectories result in point spread function related spatial aliasing that leads to violations of necessary assumptions
Solution Approach 1:
The patent segments the signal separation problem into two independent steps: first separating gas phase from dissolved phase using echo time differences, then separating TP and RBC components using chemical shift differences. This segmentation allows the use of simpler non-Cartesian trajectories without violating IDEAL assumptions, as the gas phase contamination has already been removed in the first step.
Solution Approach 2:
The patent performs preliminary removal of gas phase contamination from the k-space data before applying the IDEAL multi-component separation algorithm. This preliminary action eliminates the spatial aliasing problems that would otherwise violate IDEAL assumptions, allowing the algorithm to work correctly with non-Cartesian trajectories.
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
This approach enables the reliable removal of gas-phase contamination, improving the accuracy and consistency of dissolved-phase imaging, reducing errors and artifacts, and enhancing the reliability of pulmonary gas exchange measurements.
Implementation Method 1
MRI uses the nuclear magnetic resonance ("NMR") phenomenon to produce images. When a substance such as human tissue is subjected to a uniform magnetic field, such as the so-called main magnetic field, B0, of an MRI system, the individual magnetic moments of the nuclei in the tissue attempt to align with this B0 field, but precess about it in random order at their characteristic Larmor frequency
Implementation Method 2
One of the most attractive properties of HP 129Xe is its solubility in both parenchymal lung tissue and blood plasma (TP), and red blood cells (RBC), collectively referred to as the 'dissolved phase' components. HP 129Xe in these compartments experiences a unique chemical shift relative to the gas phase
Data Source
AI summary
A system and method is provided to acquire images of a subject having received a tissue soluble hyperpolarized gas into the airways. The method includes performing a pulse sequence including (i) for each effective repetition time (TReff), acquiring at least one gas-phase dataset and at least one dissolved-phase dataset, wherein a gas-phase echo time (TEGas) of the at least one gas-phase dataset and a dissolved-phase echo time (TEDissolved) of the at least one dissolved-phase dataset are selected to isolate gas-phase contamination of the dissolved-phase dataset from dissolved-phase components in the dissolved-phase dataset. The method also includes (ii) estimating gas-phase contamination of the dissolved-phase dataset using the gas-phase dataset and a scaling factor (σ), (iii) producing a corrected dissolved-phase dataset by reducing the gas-phase contamination of the dissolved-phase dataset using the gas-phase contamination estimated in step (ii), and reconstructing an image from the corrected dissolved-phase dataset and the gas-phase dataset.