Literature DB >> 29122361

Ventilation measured on clinical 4D-CBCT: Increased ventilation accuracy through improved image quality.

Kasper R Jensen1, Carsten Brink2, Olfred Hansen3, Uffe Bernchou2.   

Abstract

BACKGROUND AND
PURPOSE: Ventilation measured on 4D cone-beam computed tomography (CBCT) using deformable image registration (DIR) may predict specific radiation sensitivity, but the measurement is affected by the current image quality. With 4D computed tomography (CT) measured ventilation acting as a gold standard the current study investigates if image improvements increase the accuracy of 4D-CBCT measured ventilation.
MATERIAL AND METHODS: The study consists of 4D-CBCT and 4D-CT scans of 20 non-small-cell lung cancer patients. Raw CBCT projections were subjected to a standard or an improved projection correction and reconstructed by the common FDK-algorithm or the more advanced SART-algorithm. Ventilation was measured as Jacobians calculated from DIR and the comparison between CBCT and CT was done by Spearman correlation.
RESULTS: A significant increase in the mean correlation was observed when combining improved projection correction and SART reconstruction (0.34) compared to the clinical standard (0.21). The correlation further increased when averaging ventilation measured from three successive CBCT scans (0.38).
CONCLUSION: The study showed that the combination of improved projection correction and the SART reconstruction increased the accuracy of CBCT ventilation and this result can be a stepping stone to extract dynamic changes in respiration pattern of patients during radiotherapy.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Functional imaging; Image improvement; Lung cancer; Ventilation measure

Mesh:

Year:  2017        PMID: 29122361     DOI: 10.1016/j.radonc.2017.10.024

Source DB:  PubMed          Journal:  Radiother Oncol        ISSN: 0167-8140            Impact factor:   6.280


  1 in total

1.  Deriving Pulmonary Ventilation Images From Clinical 4D-CBCT Using a Deep Learning-Based Model.

Authors:  Zhiqiang Liu; Yuan Tian; Junjie Miao; Kuo Men; Wenqing Wang; Xin Wang; Tao Zhang; Nan Bi; Jianrong Dai
Journal:  Front Oncol       Date:  2022-05-02       Impact factor: 5.738

  1 in total

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