Info - Magnetic Resonance Spectroscopy
Information about the magnetic resonance spectroscopy projects and summary of published results.
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.
- 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
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]
, 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]
- 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]
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]
- 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
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