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Genetic Programming Optimisation of Nuclear Magnetic Resonance Pulse Shapes

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Book cover Medical Data Analysis (ISMDA 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1933))

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Abstract

Genetic Programming is used to generate pulse sequence elements for a Nuclear Magnetic Resonance system and evaluate them directly on that system without human intervention. The method is used to optimise pulse shapes for a series of solvent suppression problems. The method proves to be successful, with results showing an improvement in fitness of up to two orders of magnitude. The method is capable of producing both simple and novel solutions.

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References

  1. Koza,.J.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, Massachusetts (1992)

    MATH  Google Scholar 

  2. Gray, H., Maxwell, R., Martinez-Perez, I., Arus, C., Cerdan, S.: Genetic Programming for Classification and Feature Selection: Analysis of 1H Nuclear Magnetic Resonance Spectra from Human Brain Tumour Biopsies. NMR in Biomedicine 11 (1998) 217–224

    Article  Google Scholar 

  3. Poli, R., Cagnoni, S.: Genetic Programming with User-Driven Selection: Experiments on the Evolution of Algorithms for Image Enhancement. In Koza et al (ed.), Genetic Programming 1997: Proceedings of the Second Annual Conference (1997) 269–277, Morgan Kaufmann

    Google Scholar 

  4. van den Broek, W., Wienke, D., Melssen, W., Buydens, L.: Optimal Wavelength Range selection by a Genetic Algorithm for Discrimination Purposes in Spectroscopic Infrared Imaging. Applied Spectroscopy 51:8 (1997) 1210–1217

    Article  Google Scholar 

  5. Assion, A., Baumert, T., Bergt, M., Brixner, T., Kiefer, B., Seyfried, V., Strehle, M., Gerber, G.: Control of Chemical Reactions by Feedback-Optimized PhaseShaped Femtosecond Laser Pulses. Science 282 (1998) 919–922

    Article  Google Scholar 

  6. Freeman, R., Wu, X.: Design of Magnetic Resonance Experiments by Genetic Evolution. Journal of Magnetic Resonance 75 (1987) 184–189

    Google Scholar 

  7. Geen, H., Freeman, R.: Band-Selective Radiofrequency Pulses. Journal of Magnetic Resonance 93 (1991) 93–141

    Google Scholar 

  8. Zongker, D., Punch, B.: Lil-gp User’s Manual. Michigan State University (1996)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, City University, Northampton Square, London, UK

    Helen Frances Gray

  2. Gray Laboratory Cancer Research Trust, Northwood, HA6 2JR, Middlesex, UK

    Ross James Maxwell

Authors
  1. Helen Frances Gray
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  2. Ross James Maxwell
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Editor information

Editors and Affiliations

  1. Fachbereich Biologie und Informatik AG Adaptive Systemarchitektur, Johann Wolfgang Goethe-Universität, Robert-Mayer-Str. 11-15, 60054, Frankfurt a.M., Germany

    Rüdiger W. Brause

  2. Klinik für Allgemein- und Gefässchirurgie, Johann Wolfgang Goethe-Universität, Theodor-Stern-Kai 7, 60590, Frankfurt a.M., Germany

    Ernst Hanisch

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© 2000 Springer-Verlag Berlin Heidelberg

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Frances Gray, H., James Maxwell, R. (2000). Genetic Programming Optimisation of Nuclear Magnetic Resonance Pulse Shapes. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_30

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  • DOI: https://doi.org/10.1007/3-540-39949-6_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41089-8

  • Online ISBN: 978-3-540-39949-0

  • eBook Packages: Springer Book Archive

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