This is a project which is currently making use of HPC facilities at Newcastle University. It is active.
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This project uses high-performance computing to develop and validate accelerated magnetic resonance spectroscopy methods for intra-tumoural lipid composition mapping in breast cancer. HPC resources will be used for large-scale simulations, optimisation of data-reduction algorithms , and reconstruction of spectroscopic imaging data. Computational workflows will support algorithm testing on existing patient datasets and phantom studies, enabling efficient evaluation of signal-to-noise performance and measurement accuracy prior to clinical translation.
HPC resources will be used for simulation, reconstruction, and analysis of magnetic resonance spectroscopy and imaging data. The software stack will include MATLAB and Python-based tools for signal processing, Monte Carlo simulations, and optimisation of undersampling and data-reduction strategies. An existing dynamic contrast-enhanced (DCE) imaging processing pipeline will be integrated for image handling and quantitative analysis. Compute workloads will consist of parallel Monte Carlo simulations, iterative reconstruction algorithms, and large-scale batch processing of patient and phantom datasets to evaluate signal-to-noise performance, accuracy, and robustness of the proposed methods.