文章基本信息

标题：Machine Learning Optimisation for Realistic 2D and 3DPET-CT Phantom Study
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作者：Mhd Saeed Sharif ; Maysam Abbod ; Luke I Sonoda 等
期刊名称：Current Journal of Applied Science and Technology
印刷版ISSN：2457-1024
出版年度：2013
卷号：4
期号：4
页码：634-649
语种：English
出版社：Sciencedomain International
摘要：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 timesper bed are used in this study. The best scanner parameters are determined based on the ANN experimental evaluation of the proposed datasets. The analysis methodology of phantom PET data has shown promising results and can successfully classify and quantify malignant lesions in clinically realistic datasets.
关键词：Image Analysis;Positron Emission Tomography (PET);Tumour;Segmentation;ArtificialNeural Network