Model-based nonlinear inverse reconstruction for T2 mapping using highly undersampled spin-echo MRI

Abstract

Purpose: To develop a model-based reconstruction technique for T2 mapping based on multi-echo spin-echo MRI sequences with highly undersampled Cartesian data encoding.

Materials and methods: The proposed technique relies on a nonlinear inverse reconstruction algorithm which directly estimates a T2 and spin-density map from a train of undersampled spin echoes. The method is applicable to acquisitions with single receiver coils but benefits from multi-element coil arrays. The algorithm is validated for trains of 16 spin echoes with a spacing of 10 to 12 ms using numerical simulations as well as human brain MRI at 3 Tesla (T).

Results: When compared with a standard T2 fitting procedure using fully sampled T2-weighted images, and depending on the available signal-to-noise ratio and number of coil elements, model-based nonlinear inverse reconstructions for both simulated and in vivo MRI data yield accurate T2 estimates for undersampling factors of 5 to 10.

Conclusion: This work describes a promising strategy for T2-weighted MRI that simultaneously offers accurate T2 relaxation times and properly T2-weighted images at arbitrary echo times. For a standard spin-echo MRI sequence with Cartesian encoding, the method allows for a much higher degree of undersampling than obtainable by conventional parallel imaging.