Alternating low-rank tensor reconstruction for improved multiparametric mapping with cardiovascular MR Multitasking

Area of Science:

• Cardiovascular Magnetic Resonance Imaging
• Quantitative Imaging Techniques
• Biomedical Engineering

Background:

• Multiparametric mapping in cardiovascular MR Multitasking (MRMT) is crucial for accurate diagnosis.
• Current methods for MRMT reconstruction face limitations in precision and repeatability.
• Improving the efficiency of MRMT is essential for clinical applicability.

Purpose of the Study:

• To develop and validate a novel low-rank tensor reconstruction approach for cardiovascular MR Multitasking.
• To enhance the precision and repeatability of T1 and T2 mapping using the complete acquired dataset.
• To assess the potential for reducing scan times in MRMT.

Main Methods:

• A novel low-rank tensor reconstruction method was developed, alternating between temporal and spatial component estimation.
• The approach utilized the entire acquired dataset, including navigator and imaging data.
• Performance was evaluated using numerical simulations, healthy subjects, and cardiomyopathy patients, comparing it to a previous fixed-basis method.

Main Results:

• The proposed method demonstrated superior performance in numerical simulations with lower T1 and T2 errors.
• In human subjects, it significantly improved T1 precision (20-25%) and T2 precision (10-15%).
• Repeatability was enhanced by approximately 30% for T1 and 25-35% for T2, enabling comparable results at shorter scan times (50s vs 90s).

Conclusions:

• The novel low-rank tensor approach significantly improves precision and repeatability in cardiovascular MR Multitasking.
• It maintains comparable motion fidelity and quantitative measurement accuracy.
• The method shows potential for reducing scan times to 50 seconds without compromising image quality.