Fast T2 Mapping with Improved Accuracy Using Undersampled Spin-echo MRI and Model-based Reconstructions with a Generating Function

TL;DR

This paper introduces a novel model-based reconstruction method for accelerated T2 mapping in MRI that improves accuracy by accounting for indirect echoes and enables significant undersampling, validated through simulations and human brain imaging.

Contribution

It presents an advanced signal model and iterative reconstruction algorithm that directly estimates T2 maps from undersampled data, outperforming conventional pixel-based fitting methods.

Findings

More accurate T2 values than mono-exponential models

Supports undersampling factors of at least 6

Limitations for very long T2 times can be mitigated with gradient dampening

Abstract

A model-based reconstruction technique for accelerated T2 mapping with improved accuracy is proposed using undersampled Cartesian spin-echo MRI data. The technique employs an advanced signal model for T2 relaxation that accounts for contributions from indirect echoes in a train of multiple spin echoes. An iterative solution of the nonlinear inverse reconstruction problem directly estimates spin-density and T2 maps from undersampled raw data. The algorithm is validated for simulated data as well as phantom and human brain MRI at 3 T. The performance of the advanced model is compared to conventional pixel-based fitting of echo-time images from fully sampled data. The proposed method yields more accurate T2 values than the mono-exponential model and allows for undersampling factors of at least 6. Although limitations are observed for very long T2 relaxation times, respective reconstruction problems may be overcome by a gradient dampening approach. The analytical gradient of the utilized cost function is included as Appendix.