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A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications
المؤلفون المشاركون
Ichiji, Kei
Cejnek, Matous
Slama, Matous
Benes, Peter M.
Bila, Jiri
Homma, Noriyasu
Bukovsky, Ivo
المصدر
العدد
المجلد 2015، العدد 2015 (31 ديسمبر/كانون الأول 2015)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2015-03-29
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
During radiotherapy treatment for thoracic and abdomen cancers, for example, lung cancers, respiratory motion moves the target tumor and thus badly affects the accuracy of radiation dose delivery into the target.
A real-time image-guided technique can be used to monitor such lung tumor motion for accurate dose delivery, but the system latency up to several hundred milliseconds for repositioning the radiation beam also affects the accuracy.
In order to compensate the latency, neural network prediction technique with real-time retraining can be used.
We have investigated real-time prediction of 3D time series of lung tumor motion on a classical linear model, perceptron model, and on a class of higher-order neural network model that has more attractive attributes regarding its optimization convergence and computational efficiency.
The implemented static feed-forward neural architectures are compared when using gradient descent adaptation and primarily the Levenberg-Marquardt batch algorithm as the ones of the most common and most comprehensible learning algorithms.
The proposed technique resulted in fast real-time retraining, so the total computational time on a PC platform was equal to or even less than the real treatment time.
For one-second prediction horizon, the proposed techniques achieved accuracy less than one millimeter of 3D mean absolute error in one hundred seconds oftotal treatment time.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Bukovsky, Ivo& Homma, Noriyasu& Ichiji, Kei& Cejnek, Matous& Slama, Matous& Benes, Peter M.…[et al.]. 2015. A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications. BioMed Research International،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1055675
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Bukovsky, Ivo…[et al.]. A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications. BioMed Research International No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1055675
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Bukovsky, Ivo& Homma, Noriyasu& Ichiji, Kei& Cejnek, Matous& Slama, Matous& Benes, Peter M.…[et al.]. A Fast Neural Network Approach to Predict Lung Tumor Motion during Respiration for Radiation Therapy Applications. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1055675
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1055675
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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