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Abstract #2514

Joint sparsity multi-component MRF reconstruction - directly from k-space to component maps

Martijn Nagtegaal1, Emiel Hartsema1, Kirsten Koolstra2, and Frans Vos1,3
1Imaging Physics, Delft University of Technology, Delft, Netherlands, 2Department of Radiology, Leiden University Medical Centre, Leiden, Netherlands, 3Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands


The use of high undersampling factors and short flip angle trains leads to shorter acquisition times in MR Fingerprinting acquisitions. To obtain accurate multi-component estimates from this data advanced reconstructions are required. We study a low-rank ADMM based reconstruction method that adds a multi-component constraint to the inverse reconstruction problem (MC-ADMM). This method is combined with a joint-sparsity constraint yielding higher quality multi-component estimates with k-SPIJN than with previous methods. In simulations we observed increased stability to sequence truncation and in vivo multi-component estimates contained less noise-like effects.

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