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Abstract #3126

Use of L1-norm solution to Impose Spatial Smoothness Constraints in Quantitative T2 Relaxometry

Dushyant Kumar 1,2 , Susanne Siemonsen 1,2 , Margherita Porcelli 3 , Jens Fiehler 1 , Christoph Heesen 4 , and Jan Sedlacik 1

1 Dept. of Neuroradiology, Universittsklinikum Hamburg-Eppendorf, Hamburg, Hamburg, Germany, 2 Multiple Sclerosis Imaging Section (SeMSI), Universittsklinikum Hamburg-Eppendorf, Hamburg, Hamburg, Germany, 3 Mathematics, University of Bologna, Bologna, Bologna, Italy, 4 Institute for Neuroimmunology and Clinical MS Research, Universittsklinikum Hamburg-Eppendorf, Hamburg, Hamburg, Germany

Problem: A moderately high SNR (~200) QT2R data is needed for robust tissue-water-fraction-map reconstruction if L2-norm based spatial smoothness is implemented. We are testing L1-norm-solver as other possible candidate. Methods: We are developing L1-norm-solver in this context and its performance is compared against L2-norm-solver in context of imposing spatial constraints. Results & Conclusions: Results using L2- and L1-norms are similar at high SNR (>200); however, L2-norm-solver performs better at lower SNR. In near future, we would develop hybrid filter to impose smoothness and sparsity simultaneously to make L1-norm performs better at low SNR.

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