Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence
Motivation: Complex-valued deep neural network has not been fully investigated in MRS reconstruction and preprocessing.
Goal(s): We aim to solve the spectroscopy inverse problems in domain transform from FIDs to spectra, especially for accelerated MRS reconstruction.
Approach: A complex-valued deep learning framework artificial Fourier transform network (AFT-Net) is proposed to directly reconstruct and process the complex-valued raw data in the sensor domain.
Results: Evaluation of different acceleration rates was performed on the in vivo dataset. AFT-Net demonstrated the ability to reconstruct the data under up to 80 times acceleration rate. The proposed AFT-Net is an efficient and accurate approach for MEGA-PRESS MRS accelerated reconstruction.
Impact: MRS reconstruction and preprocessing with AFT-Net should be able to determine the domain-manifold mapping and process FID data directly, which shows superior performance compared with numerical method and can be served as an efficient and accurate approach for MRS acquisition.
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