• Nuclear Engineering and Technology
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• 제51권3호
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• Nuclear Medicine and Molecular Imaging
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• 제42권sup1호
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• pp.91-100
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• 2008

Ovarian cancer is often fatal since it is difficult to diagnose early and recurrence is quite frequent despite successful implementation of cytoreductive surgery and chemotherapy, thus exact diagnosis and early detection of recurrence are crucial to patient management. For pre-treatment staging, FDG PET could be helpful in a limited patient group possessing high risks of ovarian cancer. Besides, FDG PET could be recommended to patients with a high suspicion of recurrence i.e. rise of CA-125, especially in cases of conventional diagnostic imaging modalities presenting no evidence of disease because FDG PET provides critical information for treatment planning such as recurrence site or pattern. In order to expand the use of FDG PET to general population at staging or routine surveillance of ovarian cancer, more investigation is needed. The usefulness of FDG PET in evaluating treatment response and prognosis of ovarian cancer has not yet been determined, but it has been reported that FDG PET could evaluate treatment response early and show a close relationship with overall survival. PET/CT has been actively adopted in management of ovarian cancer. Not only in detecting tumor recurrence and evaluating treatment response but also in pre-treatment staging, FDG PET/CT is expected to playa role due to available anatomical information.