Keywords: Machine Learning/Artificial Intelligence, Quantitative Imaging
Motivation: T2* mapping is an important tool for evaluating of healthy and pathological tissues. High-resolution acquisitions can detect submillimeter anomalies but require substantial acceleration for feasible scan times.
Goal(s): To demonstrate feasibility of brain T2* mapping at a 0.6mm isotropic resolution in 6min at 7T.
Approach: A multi-echo GRE sequence was acquired from four volunteers using incoherent undersampling with an initial acceleration factor of 4. These datasets were then retrospectively undersampled up to a factor of 8 and reconstructed using either conventional or deep learning-based methods.
Results: The DL-based reconstruction outperforms conventional methods, enabling acceleration up to 8 with minimal impact on T2* maps.
Impact: We demonstrate the efficacy of deep learning-based reconstruction for highly accelerated acquisitions, enabling 0.6mm isotropic R2* mapping of the brain in 6 minutes at 7T. This method highlights submillimeter T2* contrast, potentially enhancing its application in detecting microstructural alterations.
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