Fast Correction of Analytical Reconstructions in Sparse View X-ray Computed Tomography / D. Trinca, Zhong Yang, J. Royuela-del-Val

Основной Автор-лицо: Trinca, D.Альтернативный автор-лицо: Zhong Yang, specialist in the field of lightning engineering, Associate Professor of Tomsk Polytechnic University, Ph.D, 1990-;Royuela-del-Val, J., JavierКоллективный автор (вторичный): Национальный исследовательский Томский политехнический университет, Инженерная школа новых производственных технологий, Отделение материаловеденияЯзык: английский.Резюме или реферат: With the availability of more powerful computers, iterative reconstruction algorithms are the subject of an ongoing work in the design of more efficient reconstruction algorithms for X-ray computed tomography. In this work, we show how two analytical reconstruction algorithms can be improved by correcting the corresponding reconstructions using a randomized iterative reconstruction algorithm. The combined analytical reconstruction followed by randomized iterative reconstruction can also be viewed as a reconstruction algorithm which, in the experiments we have conducted, uses up to 35% less projection angles as compared to the analytical reconstruction algorithms and produces the same results in terms of quality of reconstruction, without increasing the execution time significantly..Примечания о наличии в документе библиографии/указателя: [References: 7 tit.].Аудитория: .Тематика: электронный ресурс | труды учёных ТПУ | reconstruction algorithms | iterative algorithms | detectors | computed tomography | image reconstruction | electromagnetics | springs | алгоритмы | детекторы | компьютерная томография | изображения | электромагнетизм Ресурсы он-лайн:Щелкните здесь для доступа в онлайн
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[References: 7 tit.]

With the availability of more powerful computers, iterative reconstruction algorithms are the subject of an ongoing work in the design of more efficient reconstruction algorithms for X-ray computed tomography. In this work, we show how two analytical reconstruction algorithms can be improved by correcting the corresponding reconstructions using a randomized iterative reconstruction algorithm. The combined analytical reconstruction followed by randomized iterative reconstruction can also be viewed as a reconstruction algorithm which, in the experiments we have conducted, uses up to 35% less projection angles as compared to the analytical reconstruction algorithms and produces the same results in terms of quality of reconstruction, without increasing the execution time significantly.

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