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» Articles » PMID: 29659997

Image Quality Improvement in Cone-beam CT Using the Super-resolution Technique

Overview
Journal J Radiat Res
Publisher Oxford University Press
Specialties Environmental Health
Oncology
Radiology
Date 2018 Apr 17
PMID 29659997
Citations 8
Authors
Asuka Oyama
Shinobu Kumagai
Norikazu Arai
Takeshi Takata
Yusuke Saikawa
Kenshiro Shiraishi
Takenori Kobayashi
Junichi Kotoku
Affiliations
Soon will be listed here.
Abstract

This study was conducted to improve cone-beam computed tomography (CBCT) image quality using the super-resolution technique, a method of inferring a high-resolution image from a low-resolution image. This technique is used with two matrices, so-called dictionaries, constructed respectively from high-resolution and low-resolution image bases. For this study, a CBCT image, as a low-resolution image, is represented as a linear combination of atoms, the image bases in the low-resolution dictionary. The corresponding super-resolution image was inferred by multiplying the coefficients and the high-resolution dictionary atoms extracted from planning CT images. To evaluate the proposed method, we computed the root mean square error (RMSE) and structural similarity (SSIM). The resulting RMSE and SSIM between the super-resolution images and the planning CT images were, respectively, as much as 0.81 and 1.29 times better than those obtained without using the super-resolution technique. We used super-resolution technique to improve the CBCT image quality.

Citing Articles

Cone Beam Computed Tomography Image-Quality Improvement Using "One-Shot" Super-resolution.

Tsuji T, Yoshida S, Hommyo M, Oyama A, Kumagai S, Shiraishi K J Imaging Inform Med. 2024; .

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NRG Oncology and Particle Therapy Co-Operative Group Patterns of Practice Survey and Consensus Recommendations on Pencil-Beam Scanning Proton Stereotactic Body Radiation Therapy and Hypofractionated Radiation Therapy for Thoracic Malignancies.

Liu W, Feng H, Taylor P, Kang M, Shen J, Saini J Int J Radiat Oncol Biol Phys. 2024; 119(4):1208-1221.

PMID: 38395086 PMC: 11209785. DOI: 10.1016/j.ijrobp.2024.01.216.

Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.

Gong Z, Li X, Shi M, Cai G, Chen S, Ye Z Quant Imaging Med Surg. 2023; 13(12):8053-8066.

PMID: 38106266 PMC: 10722026. DOI: 10.21037/qims-23-744.

Reference-free learning-based similarity metric for motion compensation in cone-beam CT.

Huang H, Siewerdsen J, Zbijewski W, Weiss C, Unberath M, Ehtiati T Phys Med Biol. 2022; 67(12).

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A low-cost pathological image digitalization method based on 5 times magnification scanning.

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References
1.
Kurz C, Kamp F, Park Y, Zollner C, Rit S, Hansen D . Investigating deformable image registration and scatter correction for CBCT-based dose calculation in adaptive IMPT. Med Phys. 2016; 43(10):5635. DOI: 10.1118/1.4962933. View
2.
Karimi D, Ward R . Reducing streak artifacts in computed tomography via sparse representation in coupled dictionaries. Med Phys. 2016; 43(3):1473-86. DOI: 10.1118/1.4942376. View
3.
Wang Z, Bovik A, Sheikh H, Simoncelli E . Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004; 13(4):600-12. DOI: 10.1109/tip.2003.819861. View
4.
Chen Y, Song Y, Ma J, Zhao J . Optimization-based scatter estimation using primary modulation for computed tomography. Med Phys. 2016; 43(8):4753. DOI: 10.1118/1.4958680. View
5.
Jaffray D, Siewerdsen J, Wong J, Martinez A . Flat-panel cone-beam computed tomography for image-guided radiation therapy. Int J Radiat Oncol Biol Phys. 2002; 53(5):1337-49. DOI: 10.1016/s0360-3016(02)02884-5. View