Accelerating PS model-based dynamic cardiac MRI using compressed sensing.

Area of Science:

• Medical Imaging
• Magnetic Resonance Imaging (MRI)
• Image Reconstruction

Background:

• Dynamic MRI requires high spatiotemporal resolution, which is challenging to achieve.
• Partial separability (PS) model exploits data redundancy for dynamic cardiac MRI but needs extensive preprocessing data.
• Compressed sensing (CS) accelerates MRI by reducing acquired data, but its integration with PS needs optimization.

Purpose of the Study:

• To introduce and evaluate Stepped-SparsePS, a combined PS and CS method.
• To accelerate the preprocessing data acquisition for the PS model in dynamic MRI.
• To maintain high spatiotemporal resolution in dynamic MRI under accelerated acquisition.

Main Methods:

• The Stepped-SparsePS method sequentially reconstructs aliased dynamic images using the PS model.
• Compressed sensing (CS) is then applied to reconstruct final dynamic images from these aliased images.
• The approach was validated using numerical simulations and in vivo experiments.

Main Results:

• Stepped-SparsePS significantly reduces data acquisition time in dynamic MRI.
• The method successfully preserves high spatiotemporal resolution.
• Numerical simulations and in vivo experiments confirmed the efficacy of Stepped-SparsePS.

Conclusions:

• Stepped-SparsePS offers an effective solution for accelerating dynamic MRI acquisition.
• The combined PS and CS approach overcomes limitations of the PS model's preprocessing requirements.
• High spatiotemporal resolution dynamic MRI is achievable with reduced scan times using Stepped-SparsePS.