Accelerating SENSE using compressed sensing.

Area of Science:

• Medical Imaging
• Magnetic Resonance Imaging (MRI)

Background:

• Accelerating MRI scans is crucial for reducing patient discomfort and motion artifacts.
• Parallel MRI (e.g., SENSE) and compressed sensing (CS) are key techniques for MRI acceleration.
• Combining these methods offers potential for even greater speed improvements.

Purpose of the Study:

• To propose and evaluate a novel method, CS-SENSE, integrating SENSE and SparseMRI for accelerated MRI.
• To demonstrate the effectiveness of CS-SENSE in achieving higher acceleration factors compared to existing techniques.

Main Methods:

• Developed CS-SENSE, a hybrid approach combining SENSE and SparseMRI with Cartesian trajectories.
• Sequentially reconstructed aliased images per channel using SparseMRI.
• Reconstructed the final image from aliased images using Cartesian SENSE.

Main Results:

• CS-SENSE achieved higher acceleration factors than SENSE or SparseMRI alone.
• Simulations, phantom, and in vivo experiments validated the method's performance.
• CS-SENSE outperformed existing combined parallel MRI and CS methods.

Conclusions:

• CS-SENSE is an effective method for significantly accelerating MRI acquisition.
• This approach offers a promising solution for rapid MR imaging in clinical settings.