Literature DB >> 26153580

Improved accuracy of markerless motion tracking on bone suppression images: preliminary study for image-guided radiation therapy (IGRT).

Rie Tanaka, Shigeru Sanada, Keita Sakuta, Hiroki Kawashima.   

Abstract

The bone suppression technique based on advanced image processing can suppress the conspicuity of bones on chest radiographs, creating soft tissue images obtained by the dual-energy subtraction technique. This study was performed to evaluate the usefulness of bone suppression image processing in image-guided radiation therapy. We demonstrated the improved accuracy of markerless motion tracking on bone suppression images. Chest fluoroscopic images of nine patients with lung nodules during respiration were obtained using a flat-panel detector system (120 kV, 0.1 mAs/pulse, 5 fps). Commercial bone suppression image processing software was applied to the fluoroscopic images to create corresponding bone suppression images. Regions of interest were manually located on lung nodules and automatic target tracking was conducted based on the template matching technique. To evaluate the accuracy of target tracking, the maximum tracking error in the resulting images was compared with that of conventional fluoroscopic images. The tracking errors were decreased by half in eight of nine cases. The average maximum tracking errors in bone suppression and conventional fluoroscopic images were 1.3 ± 1.0 and 3.3 ± 3.3 mm, respectively. The bone suppression technique was especially effective in the lower lung area where pulmonary vessels, bronchi, and ribs showed complex movements. The bone suppression technique improved tracking accuracy without special equipment and implantation of fiducial markers, and with only additional small dose to the patient. Bone suppression fluoroscopy is a potential measure for respiratory displacement of the target.

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Year:  2015        PMID: 26153580     DOI: 10.1088/0031-9155/60/10/n209

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


  8 in total

1.  Evaluation of a template-based algorithm for markerless lung tumour localization on single- and dual-energy kilovoltage images.

Authors:  Alec M Block; Rakesh Patel; Murat Surucu; Matthew M Harkenrider; John C Roeske
Journal:  Br J Radiol       Date:  2016-10-12       Impact factor: 3.039

Review 2.  Dynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging.

Authors:  Rie Tanaka
Journal:  Radiol Phys Technol       Date:  2016-06-13

3.  Comments on "Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy" by Terunuma et al.

Authors:  Shinichiro Mori; Masahiro Endo
Journal:  Radiol Phys Technol       Date:  2018-03-06

4.  Deep learning-based bone suppression in chest radiographs using CT-derived features: a feasibility study.

Authors:  Ge Ren; Haonan Xiao; Sai-Kit Lam; Dongrong Yang; Tian Li; Xinzhi Teng; Jing Qin; Jing Cai
Journal:  Quant Imaging Med Surg       Date:  2021-12

5.  Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.

Authors:  Toshiyuki Terunuma; Aoi Tokui; Takeji Sakae
Journal:  Radiol Phys Technol       Date:  2017-12-28

Review 6.  Dynamic Chest X-Ray Using a Flat-Panel Detector System: Technique and Applications.

Authors:  Akinori Hata; Yoshitake Yamada; Rie Tanaka; Mizuki Nishino; Tomoyuki Hida; Takuya Hino; Masako Ueyama; Masahiro Yanagawa; Takeshi Kamitani; Atsuko Kurosaki; Shigeru Sanada; Masahiro Jinzaki; Kousei Ishigami; Noriyuki Tomiyama; Hiroshi Honda; Shoji Kudoh; Hiroto Hatabu
Journal:  Korean J Radiol       Date:  2020-11-30       Impact factor: 3.500

7.  A novel bone suppression algorithm in intensity-based 2D/3D image registration for real-time tumor motion monitoring: Development and phantom-based validation.

Authors:  Ingo Gulyas; Petra Trnkova; Barbara Knäusl; Joachim Widder; Dietmar Georg; Andreas Renner
Journal:  Med Phys       Date:  2022-06-06       Impact factor: 4.506

Review 8.  Machine learning applications in radiation oncology.

Authors:  Matthew Field; Nicholas Hardcastle; Michael Jameson; Noel Aherne; Lois Holloway
Journal:  Phys Imaging Radiat Oncol       Date:  2021-06-24
  8 in total

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