Keywords: AI/ML Image Reconstruction, Quantitative Imaging
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 data
Results: 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.
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