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3 January 2020 Enhancing DW images spatial resolution using correlated gradient information
Jennifer Salguero, Nelson Velasco, Eduardo Romero
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Proceedings Volume 11330, 15th International Symposium on Medical Information Processing and Analysis; 113300I (2020) https://doi.org/10.1117/12.2542552
Event: 15th International Symposium on Medical Information Processing and Analysis, 2019, Medelin, Colombia
Abstract
Diffusion-weighted imaging (DWI) is a magnetic resonance imaging technique commonly used to infer tissue microstructure, however, acquisition time requirements affect the effective spatial resolution for DWI high quality. This paper presents a novel super-resolution strategy to reconstruct high-resolution DW images by linearly combining information from different gradient acquisitions. The strategy comprises two main stages, a representation learning and a high-resolution mapping. In the former stage, information from different gradients is grouped by patch-wise statistical similarities. Representative coefficients are then estimated to represent each group. In the latter stage, adapted patch coefficients predict the high-resolution image while a regularization method eliminates possible reconstruction overlapping effects. Several tests evaluate the method ability to pre- dict high resolution information, PSNR and SSIM metrics were applied to quantitatively measure the quality improvement. Results demonstrate that quality reconstruction outperforms state of art methods in about 0.3 dB for PSNR and 1 % for SSIM.
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Jennifer Salguero, Nelson Velasco, and Eduardo Romero "Enhancing DW images spatial resolution using correlated gradient information", Proc. SPIE 11330, 15th International Symposium on Medical Information Processing and Analysis, 113300I (3 January 2020); https://doi.org/10.1117/12.2542552
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KEYWORDS
Spatial resolution
Diffusion weighted imaging
Image processing
Magnetic resonance imaging
Quality measurement
Super resolution
Tissues
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