Improved subspace estimation for low-rank model-based accelerated cardiac imaging.

Area of Science:

• Biomedical Imaging
• Magnetic Resonance Imaging
• Cardiovascular Imaging

Background:

• Sparse sampling accelerates cardiac MRI, with low-rank models relying on accurate temporal subspace estimation.
• Current methods are sensitive to data acquisition, with limited research on improving subspace estimation strategies.

Purpose of the Study:

• To investigate non-Cartesian k-space trajectories and self-navigated pulse sequences for enhanced cardiac MRI.
• To improve temporal subspace estimation for accelerated cardiac MRI reconstruction.

Main Methods:

• Utilized non-Cartesian k-space trajectories to replace sensitive Cartesian trajectories.
• Developed self-navigated pulse sequences for simultaneous navigator and imaging data acquisition.
• Analyzed phantom images and performed in vivo imaging in rats and mice without ECG or respiratory gating.

Main Results:

• Achieved 3-D cardiac imaging in rats (wall motion, perfusion, enhancement) at 74 frames/s.
• Enabled 2-D cardiac imaging in mice (wall motion) at 97 frames/s.
• Demonstrated effective subspace estimation and acceleration through proposed methods.

Conclusions:

• Non-Cartesian trajectories and self-navigated sequences significantly improve cardiac MRI acceleration.
• High-speed, gated-free cardiac imaging is feasible with the proposed sparse sampling strategies.
• These advancements offer potential for faster and more efficient cardiovascular assessments.