Dr Martin Wilson BSc, PhD

Dr Martin Wilson

School of Psychology
MR Physicist

Contact details

Address
School of Psychology
University of Birmingham
Edgbaston
Birmingham
B15 2TT
UK

Dr Wilson has over 10 years’ experience in the field of Magnetic Resonance, with a particular interest in MR spectroscopy. He currently provides MR Physics support for research at Birmingham University Imaging Centre (BUIC).

Qualifications

2004 – 2007 University of Birmingham
PhD MR spectroscopy of pediatric brain tumours

2001 - 2004 University of Warwick
BSc (Hons) Physics

Biography

Following an undergraduate degree in Physics, Dr Wilson moved to the University of Birmingham to undertake a PhD using the technique of high-resolution magic angle spinning MR spectroscopy to investigate pediatric brain tumour tissue. His post-doctoral work was based at Birmingham Children’s Hospital - with an emphasis on developing in-vivo MR methods to improve the characterisation and clinical management of brain tumours. He is currently the MR Physicist at Birmingham University Imaging Centre (BUIC) and actively develops the MRS analysis package TARQUIN.

Postgraduate supervision

Dr Wilson is currently the main supervisor for one PhD student, and co-supervises two others.

Research

Dr Wilson’s main research interest is in the application and development of MR Spectroscopy (MRS) for investigating brain pathology and neurocognition. His research goals are as follows: 

  1. Investigate how improved MRS acquisition and analysis methods can provide novel insight into brain function and disease.
  2. Reduce the barriers towards widespread clinical adoption of MRS by developing automated analysis and decision support systems.
  3. Develop techniques for combining MRS with other functional imaging methods.

ORCID ID: orcid.org/0000-0002-2089-3956

Publications

  1. Grech-Sollars, M. et al. Multi-centre reproducibility of diffusion MRI parameters for clinical sequences in the brain. NMR Biomed 28, 468-485, doi:10.1002/nbm.3269 (2015).
  2. Birch, R., Peet, A. C., Arvanitis, T. N. & Wilson, M. Sensitivity encoding for fast (1) H MR spectroscopic imaging water reference acquisition. Magn Reson Med 73, 2081-2086, doi:10.1002/mrm.25355 (2015).
  3. Wilson, M. et al. Noninvasive detection of glutamate predicts survival in pediatric medulloblastoma. Clinical cancer research 20, 4532-4539, doi:10.1158/1078-0432.CCR-13-2320 (2014).
  4. Novak, J. et al. Clinical protocols for (3)(1)P MRS of the brain and their use in evaluating optic pathway gliomas in children. Eur J Radiol 83, e106-112, doi:10.1016/j.ejrad.2013.11.009 (2014).
  5. Gill, S. K. et al. Diagnosing relapse in children's brain tumors using metabolite profiles. Neuro-Oncology 16, 156-164, doi:10.1093/neuonc/not143 (2014).
  6. Babourina-Brooks, B., Wilson, M., Arvanitis, T. N., Peet, A. C. & Davies, N. P. MRS water resonance frequency in childhood brain tumours: a novel potential biomarker of temperature and tumour environment. NMR in Biomedicine, doi:10.1002/nbm.3177 (2014).
  7. Wilson, M. et al. Magnetic resonance spectroscopy metabolite profiles predict survival in paediatric brain tumours. Eur J Cancer 49, 457-464 (2013).
  8. Vicente, J. et al. Accurate classification of childhood brain tumours by in vivo 1H MRS - a multi-centre study. Eur J Cancer 49, 658-667 (2013).
  9. Raschke, F., Davies, N. P., Wilson, M., Peet, A. C. & Howe, F. A. Classification of single-voxel 1H spectra of childhood cerebellar tumors using LCModel and whole tissue representations. Magnetic resonance in medicine : official journal of the Society of Magnetic Resonance in Medicine / Society of Magnetic Resonance in Medicine 70, 1-6, doi:10.1002/mrm.24461 (2013).
  10. Pan, X. et al. Cytoplasmic lipid droplets in nervous system tumour cell lines: Size and lipid species as analysed by 1H nuclear magnetic resonance spectroscopy. Biomed Spec Imag 2, 9-19 (2013).
  11. Pan, X. et al. Increased unsaturation of lipids in cytoplasmic lipid droplets in DAOY cancer cells in response to cisplatin treatment. Metabolomics 9, 722-729, doi:10.1007/s11306-012-0483-8 (2013).
  12. Wilson, M. & Peet, A. C. Pediatric Cancer, Volume 2: Teratoid/Rhabdoid, Brain Tumors, and Glioma, Chapter 11. Vol. 2 107-116 (Springer, 2012).
  13. Smith, S. J. et al. Recapitulation of tumor heterogeneity and molecular signatures in a 3D brain cancer model with decreased sensitivity to histone deacetylase inhibition. PLoS One 7, e52335, doi:10.1371/journal.pone.0052335 (2012).
  14. Pan, X. et al. The lipid composition of isolated cytoplasmic lipid droplets from a human cancer cell line, BE(2)M17. Mol Biosyst 8, 1694-1700, doi:10.1039/c2mb05485j (2012).
  15. Pan, X. et al. The size of cytoplasmic lipid droplets varies between tumour cell lines of the nervous system: a (1)H NMR spectroscopy study. Magn Reson Mater Phy 25, 479-485, doi:10.1007/s10334-012-0315-x (2012).
  16. Mirbahai, L. et al. Lipid biomarkers of glioma cell growth arrest and cell death detected by (1) H magic angle spinning MRS. NMR Biomed 25, 1253-1262, doi:10.1002/nbm.2796 (2012).
  17. Wilson, M., Reynolds, G., Kauppinen, R. A., Arvanitis, T. N. & Peet, A. C. A constrained least-squares approach to the automated quantitation of in vivo (1)H magnetic resonance spectroscopy data. Magn Reson Med 65, 1-12 (2011).
  18. Pan, X. et al. An in vitro metabonomic study detects increases in UDP-GlcNAc and UDP-GalNAc, as early phase markers of cisplatin treatment response in brain tumour cells. J Proteome Res 10, 3493-3500, doi:10.1021/pr200114v (2011).
  19. Hao, J. et al. A hybrid method of application of independent component analysis to in vivo (1) H MR spectra of childhood brain tumours. NMR Biomed 25, 594-606, doi:10.1002/nbm.1776 (2011).
  20. Davison, J. E. et al. MR spectroscopy-based brain metabolite profiling in propionic acidaemia: metabolic changes in the basal ganglia during acute decompensation and effect of liver transplantation. Orphanet J Rare Dis 6, 19, doi:10.1186/1750-1172-6-19 (2011).
  21. Wright, A. J. et al. Ex-vivo HRMAS of adult brain tumours: metabolite quantification and assignment of tumour biomarkers. Mol Cancer 9, 66, doi:1476-4598-9-66 [pii]10.1186/1476-4598-9-66 (2010).
  22. Wilson, M. et al. A comparison between simulated and experimental basis sets for assessing short-TE in vivo (1) H MRS data at 1.5 T. NMR Biomed 23, 1117-1126, doi:10.1002/nbm.1538 (2010).
  23. Mirbahai, L. et al. 1H magnetic resonance spectroscopy metabolites as biomarkers for cell cycle arrest and cell death in rat glioma cells. Int J Biochem Cell Biol 43, 990-1001, doi:S1357-2725(10)00250-5 [pii] 10.1016/j.biocel.2010.07.002 (2010).
  24. Hekmatyar, S. K. et al. (1)H nuclear magnetic resonance spectroscopy characterisation of metabolic phenotypes in the medulloblastoma of the SMO transgenic mice. Br J Cancer 103, 1297-1304, doi:6605890 [pii], 10.1038/sj.bjc.6605890 (2010).
  25. Davies, N. P. et al. Non-invasive detection of glycine as a biomarker of malignancy in childhood brain tumours using in-vivo 1H MRS at 1.5 tesla confirmed by ex-vivo high-resolution magic-angle spinning NMR. NMR Biomed 23, 80-87, doi:10.1002/nbm.1432 (2010).
  26. Wilson, M., Davies, N. P., Grundy, R. G. & Peet, A. C. A quantitative comparison of metabolite signals as detected by in vivo MRS with ex vivo 1H HR-MAS for childhood brain tumours. NMR Biomed 22, 213-219, doi:10.1002/nbm.1306 (2009).
  27. Wilson, M. et al. High resolution magic angle spinning 1H NMR of childhood brain and nervous system tumours. Mol Cancer 8, 6, doi:1476-4598-8-6 [pii], 10.1186/1476-4598-8-6 (2009).
  28. Hao, J. et al. A comparative study of feature extraction and blind source separation of independent component analysis (ICA) on childhood brain tumour (1)H magnetic resonance spectra. NMR Biomed 22, 809-818, doi:10.1002/nbm.1393 (2009).
  29. Davies, N. P. et al. Identification and characterisation of childhood cerebellar tumours by in vivo proton MRS. NMR Biomed 21, 908-918, doi:10.1002/nbm.1283 (2008).
  30. Peet, A. C. et al. 1H MRS identifies specific metabolite profiles associated with MYCN-amplified and non-amplified tumour subtypes of neuroblastoma cell lines. NMR Biomed 20, 692-700, doi:10.1002/nbm.1181 (2007).
  31. Reynolds, G., Wilson, M., Peet, A. & Arvanitis, T. N. An algorithm for the automated quantitation of metabolites in in vitro NMR signals. Magn Reson Med 56, 1211-1219, doi:10.1002/mrm.21081