Machine Learning Optimisation for Realistic 2D and 3D PET-CT Phantom Study
Article
Sharif, M., Abbod, Maysam, Sonoda, Luke I. and Sanghera, Bal 2013. Machine Learning Optimisation for Realistic 2D and 3D PET-CT Phantom Study. British Journal of Applied Science & Technology. 4 (4), pp. 634-649. https://doi.org/10.9734/bjast/2014/5084
Authors | Sharif, M., Abbod, Maysam, Sonoda, Luke I. and Sanghera, Bal |
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Abstract | An experimental study using artificial neural network (ANN) is carried out to achieve the optimal network architecture for proposed positron emission tomography (PET) application. 55 experimental phantom datasets acquired under clinically realistic conditions with different 2-D and 3-D acquisitions and image reconstruction parameters along with 2min, 3min and 4min scan times |
Keywords | Image Analysis; Positron Emission Tomography (PET); Tumour; Segmentation; Artificial Neural Network |
Journal | British Journal of Applied Science & Technology |
Journal citation | 4 (4), pp. 634-649 |
ISSN | 2231-0843 |
Year | 2013 |
Publisher | Science Domain International |
Publisher's version | License CC BY |
Digital Object Identifier (DOI) | https://doi.org/10.9734/bjast/2014/5084 |
Web address (URL) | https://doi.org/10.9734/bjast/2014/5084 |
Publication dates | |
20 Nov 2013 | |
Publication process dates | |
Deposited | 06 Mar 2017 |
Accepted | 03 Aug 2013 |
Copyright information | © 2014 The Authors. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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