Abstract
This work introduces a model-based super-resolution reconstruction (SRR) technique for achieving high-resolution diffusion-weighted MRI. Diffusion-weighted imaging (DWI) is a key technique for investigating white matter non-invasively. However, due to hardware and imaging time constraints, the technique offers limited spatial resolution. A SRR technique was recently proposed to address this limitation. This approach is attractive because it can produce high-resolution DWI data without the need for onerously long scan time. However, the technique treats individual DWI data from different diffusion-sensitizing gradients as independent, which in fact are coupled through the common underlying tissue. The proposed technique addresses this issue by explicitly accounting for this intrinsic coupling between DWI scans from different gradients. The key technical advance is in introducing a forward model that predicts the DWI data from all the diffusion gradients by the underpinning tissue microstructure. As a proof-of-concept, we show that the proposed SRR approach provides more accurate reconstruction results than the current SRR technique with synthetic white matter phantoms.
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Tobisch, A., Neher, P.F., Rowe, M.C., Maier-Hein, K.H., Zhang, H. (2014). Model-Based Super-Resolution of Diffusion MRI. In: Schultz, T., Nedjati-Gilani, G., Venkataraman, A., O'Donnell, L., Panagiotaki, E. (eds) Computational Diffusion MRI and Brain Connectivity. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-319-02475-2_3
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DOI: https://doi.org/10.1007/978-3-319-02475-2_3
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