Rapid simultaneous estimation of relaxation rates using multi-echo, multi-contrast MRI.

Area of Science:

• Medical Imaging
• Biophysics
• Quantitative MRI

Background:

• Multi-echo, multi-contrast dynamic imaging methods are crucial for simultaneously quantifying R2* and R2.
• Traditional nonlinear least squares (NLSQ) fitting presents significant computational challenges for these dynamic imaging studies.

Purpose of the Study:

• To introduce and validate a generalized linear least squares (LLSQ) solution for rapid R2* and R2 fitting.
• To overcome the computational burden associated with NLSQ fitting in dynamic imaging.

Main Methods:

• Simulated spin- and gradient-echo (SAGE) data across varying T2* and T2 values at high and low signal-to-noise ratios (SNR).
• Comparison of LLSQ and NLSQ fitting for both three- and four-parameter models.
• In vivo SAGE perfusion data acquisition from 20 subjects with relapsing-remitting multiple sclerosis.

Main Results:

• LLSQ demonstrated reliable fitting for R2* and R2 across simulated and in vivo data, with high concordance correlation coefficients (CCC) and low coefficients of variation (CV).
• In vivo LLSQ R2* and R2 estimates closely matched NLSQ results, with minimal differences observed at high SNR.
• LLSQ significantly reduced whole-brain fitting time from 16-18 hours (NLSQ) to 3-4 minutes.

Conclusions:

• LLSQ provides a computationally efficient and reliable alternative to NLSQ for R2* and R2 quantification in dynamic imaging.
• The reduced computational demand of LLSQ enables rapid estimation of R2* and R2, facilitating advanced quantitative MRI applications.