Literature DB >> 27405692

Hounsfield unit recovery in clinical cone beam CT images of the thorax acquired for image guided radiation therapy.

Rune Slot Thing1, Uffe Bernchou, Ernesto Mainegra-Hing, Olfred Hansen, Carsten Brink.   

Abstract

A comprehensive artefact correction method for clinical cone beam CT (CBCT) images acquired for image guided radiation therapy (IGRT) on a commercial system is presented. The method is demonstrated to reduce artefacts and recover CT-like Hounsfield units (HU) in reconstructed CBCT images of five lung cancer patients. Projection image based artefact corrections of image lag, detector scatter, body scatter and beam hardening are described and applied to CBCT images of five lung cancer patients. Image quality is evaluated through visual appearance of the reconstructed images, HU-correspondence with the planning CT images, and total volume HU error. Artefacts are reduced and CT-like HUs are recovered in the artefact corrected CBCT images. Visual inspection confirms that artefacts are indeed suppressed by the proposed method, and the HU root mean square difference between reconstructed CBCTs and the reference CT images are reduced by 31% when using the artefact corrections compared to the standard clinical CBCT reconstruction. A versatile artefact correction method for clinical CBCT images acquired for IGRT has been developed. HU values are recovered in the corrected CBCT images. The proposed method relies on post processing of clinical projection images, and does not require patient specific optimisation. It is thus a powerful tool for image quality improvement of large numbers of CBCT images.

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Year:  2016        PMID: 27405692     DOI: 10.1088/0031-9155/61/15/5781

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  7 in total

1.  Deep learning-based thoracic CBCT correction with histogram matching.

Authors:  Richard L J Qiu; Yang Lei; Joseph Shelton; Kristin Higgins; Jeffrey D Bradley; Walter J Curran; Tian Liu; Aparna H Kesarwala; Xiaofeng Yang
Journal:  Biomed Phys Eng Express       Date:  2021-10-29

2.  Evaluation of CBCT scatter correction using deep convolutional neural networks for head and neck adaptive proton therapy.

Authors:  Arthur Lalonde; Brian Winey; Joost Verburg; Harald Paganetti; Gregory C Sharp
Journal:  Phys Med Biol       Date:  2020-12-04       Impact factor: 3.609

3.  Image-based shading correction for narrow-FOV truncated pelvic CBCT with deep convolutional neural networks and transfer learning.

Authors:  Matteo Rossi; Gabriele Belotti; Chiara Paganelli; Andrea Pella; Amelia Barcellini; Pietro Cerveri; Guido Baroni
Journal:  Med Phys       Date:  2021-10-26       Impact factor: 4.506

4.  Assessing the impact of choosing different deformable registration algorithms on cone-beam CT enhancement by histogram matching.

Authors:  Halima Saadia Kidar; Hacene Azizi
Journal:  Radiat Oncol       Date:  2018-11-07       Impact factor: 3.481

5.  Accuracy of automatic deformable structure propagation for high-field MRI guided prostate radiotherapy.

Authors:  Rasmus Lübeck Christiansen; Lars Dysager; Anders Smedegaard Bertelsen; Olfred Hansen; Carsten Brink; Uffe Bernchou
Journal:  Radiat Oncol       Date:  2020-02-07       Impact factor: 3.481

6.  Cone beam CT based dose calculation in the thorax region.

Authors:  Laura Patricia Kaplan; Ulrik Vindelev Elstrøm; Ditte Sloth Møller; Lone Hoffmann
Journal:  Phys Imaging Radiat Oncol       Date:  2018-09-28

7.  Evaluation of an a priori scatter correction algorithm for cone-beam computed tomography based range and dose calculations in proton therapy.

Authors:  Andreas Gravgaard Andersen; Yang-Kyun Park; Ulrik Vindelev Elstrøm; Jørgen Breede Baltzer Petersen; Gregory C Sharp; Brian Winey; Lei Dong; Ludvig Paul Muren
Journal:  Phys Imaging Radiat Oncol       Date:  2020-10-27
  7 in total

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