The projects and published results in this group deal with magnetic resonance spectroscopy.

We investigate machine learning and control approahes for analysis and quantification of magnetic resonance spectra and developing MR pulse sequences.

People

• Frank C Langbein, School of Computer Science and Informatics, Cardiff University; langbein.org
• Sophie Schirmer, Physics, Swansea University
• Chris Jenkins, Swansea University, Cardiff University
• Max Chandler, School of Computer Science and Informatics, Cardiff University
• Oktay Karakus, School of Computer Science and Informatics, Cardiff University
• Zien Ma, School of Computer Science and Informatics, Cardiff University
• André Döring, Cardiff University
• Frank Rosler, Cardiff University

Publications

• M Chandler, C Jenkins, SM Shermer, FC Langbein. MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Network. In preparation, 2019. [arxiv:1909.03836] [PDF] [Details]

• C Jenkins, M Chandler, FC Langbein, SM Shermer. Benchmarking GABA Quantification: A Ground Truth Data Set and Comparative Analysis of TARQUIN, LCModel, jMRUI and Gannet. In preparation, 2021. [arxiv:1909.02163] [PDF] [Details]

• C Jenkins, MChandler, FC Langbein, SM Shermer. Quantification of edited magnetic resonance spectroscopy: a comparative phantom based study of analysis methods. ISMRM 27th Annual Meeting & Exhibition, Montréal, QC, Canada, 11th-16th May 2019. [PDF] [Details]

• SM Schirmer, FC Langbein, C Jenkins, M Chandler. Design of novel MRI pulse sequences for GABA quantification using optimal control. In: 4th Int Symp on MRS of GABA, 2017. [Details]

• C Jenkins, M Chandler, FC Langbein, SM Schirmer. Modelling, Optimization and QA for Magnetic Resonance SpectroscopyPDFPoster-Chris.pdf [Details]

• M Chandler, FC Langbein, C Jenkins, SM Schirmer. Quantum Control for Magnetic Resonance Spectroscopy. All Wales Medical Physics and Engineering Summer Meeting, poster, 2017. [PDF] [Details]

Presentations

• Zien Ma, Oktay Karakus, Sophie Shermer, Frank Langbein. Quantification of Metabolites in Magnetic Resonance Spectra with Deep Learning: Insights on Simulated and Real Data. Presentation at the One Day Meeting: Synthetic Data for Machine Learning, The British Machine Vision Association and Society for Pattern Recognition, Wednesday 8 November 2023. [PDF:abstract]

• FC Langbein. Control and Machine Learning for Magnetic Resonance Spectroscopy. Keynote talk, Frontiers of Intelligent Computing: Theory and Applications (FICTA), 11-12 April 2023. [PDF]

PhDs

• Z Ma. Quantification of metabolites in magnetic resonance spectroscopy. Cardiff University, School of Computer Science and Informatics.
• M Chandler. New methods in quantification and RF pulse optimisation for magnetic resonance spectroscopy. PhD Thesis, Cardiff University, School of Computer Science and Informatics, 2019. [PDF]
• C Jenkins. New techniques for quantification of biomarkers and metabolites by magnetic resonance imaging and spectroscopy. PhD Thesis, Swansea University, College Of Science, 2019. [DOI:10.23889/Suthesis.50804]

Code

• M Chandler, C Jenkins, SM Shermer, FC Langbein. MRSNet, V1.0. Code, https://qyber.black/mrs/code-mrsnet, https://github.com/MaxChandler/MRSNet, 16th August 2019. [DOI:10.6084/m9.figshare.9824417.v1]

• SM Shermer, FC Langbein, CW Jenkins, M Chandler. Code - LWFIT. Version 1.0. FigShare, Software. May 2021. [DOI:10.6084/m9.figshare.14540757.v1] [DEV:https://qyber.black/mrs/code-lwfit] [MIRROR:https://github.com/qyber-black/Code-LWFIT/tree/v1.0]

Data

• SM Shermer, C Jenkins, M Chandler, FC. Langbein. Magnetic resonance spectroscopy data for GABA quantification using MEGAPRESS pulse sequence. Data set, IEEE Data Port, 15th August 2019. DOI:10.21227/ak1d-3s20

Locations

The wiki is written and maintained on Qyber\black at https://qyber.black/pca/info-mrs/

License

This wiki is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The publications, code, data, etc. may be under a different license. Check the relevant information provided with these products.