Can Planning Images Reduce Scatter in Follow-Up Cone-Beam CT?

Jonathan H. Mason*, Alessandro Perelli, William H. Nailon, Mike E. Davies

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Due to its wide field of view, cone-beam computed tomography (CBCT) is plagued by large amounts of scatter, where attenuated photons hit the detector, and corrupt the linear models used for reconstruction. Given that one can generate a good estimate of scatter however, then image accuracy can be retained. In the context of adaptive radiotherapy, one usually has a low-scatter planning CT image of the same patient at an earlier time. Correcting for scatter in the subsequent CBCT scan can either be self consistent with the new measurements or exploit the prior image, and there are several recent methods that report high accuracy with the latter. In this study, we will look at the accuracy of various scatter estimation methods, how they can be effectively incorporated into a statistical reconstruction algorithm, along with introducing a method for matching off-line Monte-Carlo (MC) prior estimates to the new measurements. Conclusions we draw from testing on a neck cancer patient are: statistical reconstruction that incorporates the scatter estimate significantly outperforms analytic and iterative methods with pre-correction; and although the most accurate scatter estimates can be made from the MC on planning image, they only offer a slight advantage over the measurement based scatter kernel superposition (SKS) in reconstruction error.
Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis - 21st Annual Conference, MIUA 2017, Proceedings
PublisherSpringer-Verlag
Pages629-640
Number of pages12
Volume723
ISBN (Print)9783319609638
DOIs
Publication statusPublished - 2017
Event21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017 - Edinburgh, United Kingdom
Duration: 11 Jul 201713 Jul 2017

Publication series

NameCommunications in Computer and Information Science
Volume723
ISSN (Print)18650929

Conference

Conference21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017
CountryUnited Kingdom
CityEdinburgh
Period11/07/1713/07/17

Keywords

  • Computed tomography
  • Cone-beam
  • Prior information
  • Scatter estimation
  • Statistical Reconstruction

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