SCSA based MATLAB toolbox for $^1$H MR spectroscopic water suppression and denoising

Sourav Bhaduri, Abderrazak Chahid, Eric Achten, Taous-Meriem Laleg-Kirati, Hacene Serrai

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In vivo 1H Magnetic Resonance Spectroscopy (MRS) is a useful tool in assessing neurological and metabolic disease, and to improve tumor treatment. Different pre-processing pipelines have been developed to obtain optimal results from the acquired data with sophisticated data fitting, peak suppression, and denoising protocols. We introduce a Semi-Classical Signal Analysis (SCSA) based Spectroscopy pre-processing toolbox for water suppression and data denoising, which allows researchers to perform water suppression using SCSA with phase correction and apodization filters and denoising of MRS data, and data fitting has been included as an additional feature, but it is not the main aim of the work. The fitting module can be passed on to other software. The toolbox is easy to install and to use: 1) import water unsuppressed MRS data acquired in Siemens, Philips and.mat file format and allow visualization of spectroscopy data, 2) allow pre-processing of single voxel and multi-voxel spectra, 3) perform water suppression and denoising using SCSA, 4) incorporate iterative nonlinear least squares fitting as an extra feature. This article provides information about how the above features have been included, along with details of the graphical user interface using these features in MATLAB. The code can be downloaded from
Original languageEnglish (US)
Pages (from-to)100294
JournalInformatics in Medicine Unlocked
StatePublished - Jan 14 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): BAS/1/1627-01-01
Acknowledgements: This research project has been jointly funded by King Abdullah University of Science and Technology (KAUST) Base Research Fund (BAS/1/1627-01-01) and Ghent University Funding.The research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST) in collaboration with Ghent University. The authors would like to thank Dr. Sabine Van Huffel from KU Leuven for the HLSVD software, and the Ghent Institute for Functional and Metabolic Imaging (GIfMI) team for their help in data acquisition.


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