Cone Beam Computed Tomography Image Quality Improvement Using a Deep Convolutional Neural Network

Overview

Journal Cureus

Specialty Biomedical Engineering

Date 2018 Jul 3

PMID 29963342

Citations 61

Authors

Satoshi Kida

Takahiro Nakamoto

Masahiro Nakano

Kanabu Nawa

Akihiro Haga

Junichi Kotoku

Hideomi Yamashita

Keiichi Nakagawa

Affiliations

Soon will be listed here.

Abstract

Introduction Cone beam computed tomography (CBCT) plays an important role in image-guided radiation therapy (IGRT), while having disadvantages of severe shading artifact caused by the reconstruction using scatter contaminated and truncated projections. The purpose of this study is to develop a deep convolutional neural network (DCNN) method for improving CBCT image quality. Methods CBCT and planning computed tomography (pCT) image pairs from 20 prostate cancer patients were selected. Subsequently, each pCT volume was pre-aligned to the corresponding CBCT volume by image registration, thereby leading to registered pCT data (pCT). Next, a 39-layer DCNN model was trained to learn a direct mapping from the CBCT to the corresponding pCTimages. The trained model was applied to a new CBCT data set to obtain improved CBCT (i-CBCT) images. The resulting i-CBCT images were compared to pCT using the spatial non-uniformity (SNU), the peak-signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM). Results The image quality of the i-CBCT has shown a substantial improvement on spatial uniformity compared to that of the original CBCT, and a significant improvement on the PSNR and the SSIM compared to that of the original CBCT and the enhanced CBCT by the existing pCT-based correction method. Conclusion We have developed a DCNN method for improving CBCT image quality. The proposed method may be directly applicable to CBCT images acquired by any commercial CBCT scanner.

Citing Articles

Better Cone-Beam CT Artifact Correction via Spatial and Channel Reconstruction Convolution Based on Unsupervised Adversarial Diffusion Models.

Dong G, He Y, Liu X, Dai J, Xie Y, Liang X Bioengineering (Basel). 2025; 12(2).

PMID: 40001652 PMC: 11851389. DOI: 10.3390/bioengineering12020132.

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; .

PMID: 39633213 DOI: 10.1007/s10278-024-01346-w.

Validation of 2D lateral cephalometric analysis using artificial intelligence-processed low-dose cone beam computed tomography.

Chung E, Yang B, Kang S, Kim Y, Na J, Park S Heliyon. 2024; 10(21):e39445.

PMID: 39583802 PMC: 11584577. DOI: 10.1016/j.heliyon.2024.e39445.

Proton dose calculation on cone-beam computed tomography using unsupervised 3D deep learning networks.

Dueholm Vestergaard C, Vindelev Elstrom U, Paul Muren L, Ren J, Norrevang O, Jensen K Phys Imaging Radiat Oncol. 2024; 32:100658.

PMID: 39534276 PMC: 11554915. DOI: 10.1016/j.phro.2024.100658.

Automated Organ Segmentation for Radiation Therapy: A Comparative Analysis of AI-Based Tools Versus Manual Contouring in Korean Cancer Patients.

Choi S, Park J, Cho Y, Yang G, Yoon H Cancers (Basel). 2024; 16(21).

PMID: 39518109 PMC: 11544936. DOI: 10.3390/cancers16213670.

References

Park Y, Sharp G, Phillips J, Winey B . Proton dose calculation on scatter-corrected CBCT image: Feasibility study for adaptive proton therapy. Med Phys. 2015; 42(8):4449-59. PMC: 4517932. DOI: 10.1118/1.4923179. View

Siewerdsen J, Jaffray D . Cone-beam computed tomography with a flat-panel imager: magnitude and effects of x-ray scatter. Med Phys. 2001; 28(2):220-31. DOI: 10.1118/1.1339879. View

Siewerdsen J, Jaffray D . Optimization of x-ray imaging geometry (with specific application to flat-panel cone-beam computed tomography). Med Phys. 2000; 27(8):1903-14. DOI: 10.1118/1.1286590. View

Sun M, Star-Lack J . Improved scatter correction using adaptive scatter kernel superposition. Phys Med Biol. 2010; 55(22):6695-720. DOI: 10.1088/0031-9155/55/22/007. View

Yao W, Leszczynski K . An analytical approach to estimating the first order scatter in heterogeneous medium. II. A practical application. Med Phys. 2009; 36(7):3157-67. DOI: 10.1118/1.3152115. View

Bissonnette J, Moseley D, Jaffray D . A quality assurance program for image quality of cone-beam CT guidance in radiation therapy. Med Phys. 2008; 35(5):1807-15. DOI: 10.1118/1.2900110. View

Cha K, Hadjiiski L, Samala R, Chan H, Caoili E, Cohan R . Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. Med Phys. 2016; 43(4):1882. PMC: 4808067. DOI: 10.1118/1.4944498. View

Klein S, Staring M, Murphy K, Viergever M, Pluim J . elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging. 2009; 29(1):196-205. DOI: 10.1109/TMI.2009.2035616. View

Dong C, Loy C, He K, Tang X . Image Super-Resolution Using Deep Convolutional Networks. IEEE Trans Pattern Anal Mach Intell. 2016; 38(2):295-307. DOI: 10.1109/TPAMI.2015.2439281. View

10.

Cicero M, Bilbily A, Colak E, Dowdell T, Gray B, Perampaladas K . Training and Validating a Deep Convolutional Neural Network for Computer-Aided Detection and Classification of Abnormalities on Frontal Chest Radiographs. Invest Radiol. 2016; 52(5):281-287. DOI: 10.1097/RLI.0000000000000341. View

11.

Shi L, Tsui T, Wei J, Zhu L . Fast shading correction for cone beam CT in radiation therapy via sparse sampling on planning CT. Med Phys. 2017; 44(5):1796-1808. DOI: 10.1002/mp.12190. View

12.

Han X . MR-based synthetic CT generation using a deep convolutional neural network method. Med Phys. 2017; 44(4):1408-1419. DOI: 10.1002/mp.12155. View

13.

Takayama Y, Kadoya N, Yamamoto T, Ito K, Chiba M, Fujiwara K . Evaluation of the performance of deformable image registration between planning CT and CBCT images for the pelvic region: comparison between hybrid and intensity-based DIR. J Radiat Res. 2017; 58(4):567-571. PMC: 5569957. DOI: 10.1093/jrr/rrw123. View

14.

Chen H, Zhang Y, Kalra M, Lin F, Chen Y, Liao P . Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network. IEEE Trans Med Imaging. 2017; 36(12):2524-2535. PMC: 5727581. DOI: 10.1109/TMI.2017.2715284. View

15.

Siewerdsen J, Moseley D, Bakhtiar B, Richard S, Jaffray D . The influence of antiscatter grids on soft-tissue detectability in cone-beam computed tomography with flat-panel detectors. Med Phys. 2005; 31(12):3506-20. DOI: 10.1118/1.1819789. View

16.

Xu Y, Bai T, Yan H, Ouyang L, Pompos A, Wang J . A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy. Phys Med Biol. 2015; 60(9):3567-87. PMC: 4409575. DOI: 10.1088/0031-9155/60/9/3567. View

17.

Niu T, Sun M, Star-Lack J, Gao H, Fan Q, Zhu L . Shading correction for on-board cone-beam CT in radiation therapy using planning MDCT images. Med Phys. 2010; 37(10):5395-406. DOI: 10.1118/1.3483260. View

18.

Ruhrnschopf E, Klingenbeck K . A general framework and review of scatter correction methods in x-ray cone-beam computerized tomography. Part 1: Scatter compensation approaches. Med Phys. 2011; 38(7):4296-311. DOI: 10.1118/1.3599033. View

19.

Marchant T, Moore C, Rowbottom C, Mackay R, Williams P . Shading correction algorithm for improvement of cone-beam CT images in radiotherapy. Phys Med Biol. 2008; 53(20):5719-33. DOI: 10.1088/0031-9155/53/20/010. View

20.

Siewerdsen J, Daly M, Bakhtiar B, Moseley D, Richard S, Keller H . A simple, direct method for x-ray scatter estimation and correction in digital radiography and cone-beam CT. Med Phys. 2006; 33(1):187-97. DOI: 10.1118/1.2148916. View