Integrating Quantitative Mapping into Physics-Based Deep Learning for Improved Accelerated Image Reconstruction

Motivation: Physics-based deep learning has been increasingly applied to MRI image reconstruction to accelerate acquisitions. Goal(s): Here, we investigate whether including a relaxometry model into these networks enables higher quality accelerated reconstructions, and consequently more accurate quantitative maps. Approach: Two recurrent inference machines with different physics models were implemented: (1) reconstruction of contrast-weighted image series and (2) direct T2 map estimation, from undersampled k-space dataResults: Including relaxometry into physics-informed networks improves reconstruction and T2 map quality for acceleration factors as high as 8-fold. Impact: Integrating relaxometry models into physics-informed deep learning-based image reconstruction methods enables high quality quantitative mapping directly from undersampled k-space data, from which contrast-weighted images can also be accurately synthesised.