Keywords: Data Processing, Data Processing, Optimization, Torch, Auto-differentiation.
Motivation: Addressing the time constraints in Magnetic Resonance Spectroscopy (MRS) metabolite quantification, TensorFit aims to overcome processing delays hindering clinical use.
Goal(s): TensorFit seeks to accelerate MRS analysis with a strong focus in time efficiency, using GPU acceleration, and modeling capabilities within the Torch framework.
Approach: Implemented in Python, employs Torch for efficient forward- and back-propagation, allowing rapid quantification of large datasets. It supports GPU usage, and integration with SpectrIm-QMRS for clinical practices.
Results: TensorFit achieves speed-ups surpassing existing methods by up to 200x on GPU and 17x on CPU, making it a powerful tool for metabolite quantification in EPSI data.
Impact: TensorFit speed-up MRS metabolite quantification in clinical practices, enabling ultra-fast analysis. This tool could lead to enhance the use of high-resolution MRS adquisition for both research and clinical practices.
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