Abstract
The kinetics of the uptake of a magnetic resonance (MR) contrast agent (gadolinium—DTPA) can be monitored via the effect on signal intensity in a series of MR images. This can be used to estimate blood flow in normal tissues or tumours. We have used a competitive neural network approach to identify image pixels with similar patterns of contrast agent kinetics. This allows automatic (and un-supervised) visualisation of regions with similar blood flow. Averaging of contrast agent kinetics over self-similar pixels was useful for fitting to model functions to obtain quantitative measures of blood flow in untreated tumours and in tumours treated with the anti-vascular drug, combretastatin.
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© 2000 Springer-Verlag London
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Maxwell, R.J., Wilson, J., Tozer, G.M., Barber, P.R., Vojnovic, B. (2000). Segmentation of Magnetic Resonance Images According to Contrast Agent Uptake Kinetics Using a Competitive Neural Network. In: Malmgren, H., Borga, M., Niklasson, L. (eds) Artificial Neural Networks in Medicine and Biology. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0513-8_12
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DOI: https://doi.org/10.1007/978-1-4471-0513-8_12
Publisher Name: Springer, London
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