Accelerated Low-rank MPnRAGE Denoising and T1 Reconstruction using Jointly Trained U-Net Regularizers

Motivation: Quantitative T1 (qT1) using MPnRAGE is a promising brain biomarker. However, reliable qT1 measurements at 1 mm isotropic resolution across the brain may require long scan times (>8min). Goal(s): We aim to develop a low-rank denoising strategy providing reliable qT1 estimations from accelerated scans in about 2 minutes. Approach: We developed a novel strategy using two U-Net models, the denoiser and T1-estimator, trained together to jointly convert accelerated low-rank scans into accurate qT1 maps. Results: Our method exhibits good bias correction with low errors in both gray matter and white matter (<3%) with high image acceleration. Impact: Our method provides fast and accurate whole-brain high-resolution qT1 estimation from MPnRAGE scans in about 2 minutes.