Compressed sensing acceleration of radial 3-D alternating Look-Locker T 1 mapping

Area of Science:

• Magnetic Resonance Imaging (MRI)
• Medical Imaging
• Biomedical Engineering

Background:

• T1 mapping is crucial for characterizing tissue properties in MRI.
• Accelerating T1 mapping acquisition is essential for reducing scan times and improving patient comfort.
• Compressed sensing (CS) offers potential for significant acceleration in MRI sequences.

Purpose of the Study:

• To evaluate the effectiveness of various compressed sensing (CS) models in accelerating alternating Look-Locker T1 mapping.
• To identify the optimal CS reconstruction strategy for accelerated T1 mapping.

Main Methods:

• Retrospective acceleration of alternating Look-Locker acquisition by factors of 1-12.
• Reconstruction of 12 images using multiple CS models with combinations of spatial total variation, locally low-rank regularization, and subspace constraints.
• Complex non-linear least squares signal fitting for T1 map generation and comparison with full data reference.

Main Results:

• A subspace-constrained reconstruction model incorporating spatial total variation and locally low-rank regularization demonstrated superior performance.
• This model outperformed others based on normalized root mean squared error, structural similarity index, and normalized mean absolute deviation.
• Subspace constraints improved models with spatial total variation but not those using only locally low-rank regularization.

Conclusions:

• Radial 3-D alternating Look-Locker T1 mapping acquisition can be effectively accelerated up to 12-fold using various CS models.
• The optimal acceleration was achieved with a subspace-constrained reconstruction model utilizing spatial total variation and locally low-rank regularization.