Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network
المؤلفون المشاركون
Zeng, Lei
Xu, Yifu
Zhang, Jingfang
Chen, Jian
Wang, Linyuan
Yan, Bin
المصدر
Computational and Mathematical Methods in Medicine
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-09-05
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Background.
Dual-energy computed tomography (DECT) has been widely used due to improved substances identification from additional spectral information.
The quality of material-specific image produced by DECT attaches great importance to the elaborated design of the basis material decomposition method.
Objective.
The aim of this work is to develop and validate a data-driven algorithm for the image-based decomposition problem.
Methods.
A deep neural net, consisting of a fully convolutional net (FCN) and a fully connected net, is proposed to solve the material decomposition problem.
The former net extracts the feature representation of input reconstructed images, and the latter net calculates the decomposed basic material coefficients from the joint feature vector.
The whole model was trained and tested using a modified clinical dataset.
Results.
The proposed FCN delivers image with about 60% smaller bias and 70% lower standard deviation than the competing algorithms, suggesting its better material separation capability.
Moreover, FCN still yields excellent performance in case of photon noise.
Conclusions.
Our deep cascaded network features high decomposition accuracies and noise robust property.
The experimental results have shown the strong function fitting ability of the deep neural network.
Deep learning paradigm could be a promising way to solve the nonlinear problem in DECT.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Xu, Yifu& Yan, Bin& Zhang, Jingfang& Chen, Jian& Zeng, Lei& Wang, Linyuan. 2018. Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network. Computational and Mathematical Methods in Medicine،Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1131849
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Xu, Yifu…[et al.]. Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network. Computational and Mathematical Methods in Medicine No. 2018 (2018), pp.1-9.
https://search.emarefa.net/detail/BIM-1131849
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Xu, Yifu& Yan, Bin& Zhang, Jingfang& Chen, Jian& Zeng, Lei& Wang, Linyuan. Image Decomposition Algorithm for Dual-Energy Computed Tomography via Fully Convolutional Network. Computational and Mathematical Methods in Medicine. 2018. Vol. 2018, no. 2018, pp.1-9.
https://search.emarefa.net/detail/BIM-1131849
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1131849
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر