Info - Magnetic Resonance Spectroscopy

Info - Magnetic Resonance Spectroscopy

We investigate machine learning and control approaches for analysing magnetic resonance spectra, in particular for metabolite quantification, and developing MR pulse sequences.

We have contributed theory and efficient methods for

  • Designing chemically specific magnetic resonance spectroscopy pulse sequences via quantum control and metabolite quantification via deep learning.

People

Publications

  • Zien Ma, S. M. Shermer, Oktay Karakuş, Frank C. Langbein. The Sim-to-Real Gap in MRS Quantification: A Systematic Deep Learning Validation for GABA. Preprint, February 2026. [PDF] [arXiv:2602.20289]
  • FC Langbein. Quantum Control and AI in Medical Diagnosis: Progress and Challenges. Guest Lecture, Google Deep Mind Programme. Cardiff Metropolitan University, 24th June 2025.
  • Z Ma, S Shermer, O Karakus, FC Langbein. Deep Learning vs Conventional Methods for Metabolite Quantification in MR Spectra: Challenges and Solutions. All-Wales Medical Physics & Clinical Engineering Summer Meeting, June 2025.
  • Z Ma, O Karakus, SM Shermer, FC Langbein. The Impact of Training Data on MRS Metabolite Quantification with Deep Learning. Poster. ISMRM & ISMRT Annual Meeting & Exhibition, Honolulu, Hawaiʻi, USA, 10-15 May 2025. [PDF:Abstract] [PDF:Poster]
  • Zien Ma, Oktay Karakus, Sophie Shermer, Frank Langbein. Quantification of Metabolites in Magnetic Resonance Spectra with Deep Learning: Insights on Simulated and Real Data. One Day Meeting: Synthetic Data for Machine Learning, The British Machine Vision Association and Society for Pattern Recognition, Wednesday 8 November 2023. [PDF:abstract]
  • 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. Preprint, 2021. [arXiv:1909.02163] [PDF] [Abstract]
  • M Chandler, C Jenkins, SM Shermer, FC Langbein. MRSNet: Metabolite Quantification from Edited Magnetic Resonance Spectra With Convolutional Neural Network. Preprint, 2019. [arXiv:1909.03836] [PDF]
  • C Jenkins, M Chandler, 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]
  • SM Schirmer, FC Langbein, C Jenkins, M Chandler. Design of novel MRI pulse sequences for GABA quantification using optimal control. Proc 4th Int Symp on MRS of GABA, 2017. [Abstract]
  • C Jenkins, M Chandler, FC Langbein, SM Schirmer. Modelling, Optimization and QA for Magnetic Resonance Spectroscopy. All Wales Medical Physics and Engineering Summer Meeting, short talk and poster, 2017. [PDF:poster]
  • 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:poster]

Presentations

  • Frank C Langbein. Metabolite Quantification with AI from MR Spectra. Cardiff/University of Chinese Academy of Sciences (UCAS) Workshop on Visual Computing, 29th January 2024. [PDF]
  • 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]
  • Zien Ma, Frank Langbein, Oktay Karakus, Sophie Shermer. Metabolites Quantification with Magnetic Resonance Spectroscopy and Spectrum Signal Processing using an Autoencoder Architecture. VLunch talk, Cardiff University, 11th May 2022.

PhDs

  • Z Ma. Quantification of metabolites in magnetic resonance spectroscopy. Cardiff University, School of Computer Science and Informatics, ongoing.
  • 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]

Reports

  • UM Sawant. Web Interface and Cloud Deployment for MRSNet, MSc Advanced Computer Science dissertation, Cardiff University, 2023. [Archive]
  • Z Ma. MRS Literautre Review. Technical Report, rardiff University, 2022.
  • J Chen. Quantifying Glutamate and Glutamine in MEGA-PRESS MR Spectra, BSc Computer Science dissertation, Cardiff University, 2020.

Code and Results

Data

Locations

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

Contact

For any general enquiries relating to this project group, send an e-mail.

License

CC BY-NC-SA 4.0 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.

If you wish to use any content available under the qyber.black and qyber.dev domains to develop an AI system (or anything else), we expect that any code, models, and other results are made available under a license compatible with the contents' license. We reserve the right to corrupt models, code or anything else that violates this by inserting suitable mechanisms in the content of qyber.black and qyber.dev.