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Retrospective Cardiac Gating with A Prototype Small-Animal X-ray Computed Tomograph
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MRI Retrospective Respiratory Gating and Cardiac Sensing by CW Doppler Radar: A Feasibility Study
IEEE Transactions on Bio-Medical Engineering
|August 8, 2024
Summary
Continuous wave Doppler radar enables non-contact sensing of respiration and cardiac signals within MRI systems. This technology facilitates retrospective motion correction, improving patient handling and throughput.
Area of Science:
- Medical Imaging Physics
- Biomedical Engineering
- Electromagnetic Sensing
Background:
- Conventional MRI gating sensors can be challenging for specific patient groups.
- Non-contact sensing offers a potential solution to improve patient comfort and MRI workflow.
Purpose of the Study:
- To investigate the feasibility of non-contact respiratory gating and cardiac sensing using continuous wave (CW) Doppler radar within an MRI environment.
- To assess the compatibility and interference of radar sensing with MRI systems.
- To evaluate the potential of radar sensing for retrospective motion correction.
Main Methods:
- Utilized a software-defined radio operating at 2.4 GHz for CW Doppler radar.
- Demodulated in-vivo respiratory and cardiac signals.
- Validated radar functionality through electromagnetic simulations, bench tests, and human subject studies within the MRI bore.
- Compared radar sensing with electrocardiography, system bellows, and plethysmography.
Main Results:
- Demonstrated feasibility of non-contact cardiac rate sensing and dynamic breathing synchronization.
- Achieved in-bore motion correction for retrospective respiratory gating.
- Ensured MRI compatibility and interference-free operation through optimal radar design and spectral isolation.
- Observed minimal signal-to-noise ratio degradation (within 4.5%) on phantom images.
Conclusions:
- Confirmed the feasibility of in-bore retrospective motion correction using CW Doppler radar, independent of MRI system constraints.
- Non-contact radar sensing can complement existing MRI sensors and algorithms, potentially enhancing patient management and throughput.
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