Amirmohammad Shamaei

The paper introduces a Python toolbox, SMART_MRS, to simulate common artifacts in GABA-edited MRS data, enhancing the diversity and quality of simulated data.

This paper introduces a self-supervised deep learning method for quantifying metabolite concentrations in MR spectroscopy data, showing comparable accuracy to traditional methods with faster processing.

This paper develops unsupervised deep learning methods for frequency and phase correction of MRS signals, showing comparable performance to supervised approaches and improved efficiency.

The paper introduces NIfTI-MRS, a standardized format for magnetic resonance spectroscopy data to improve sharing and integration across the MRS community.