Literature DB >> 33574477

Low-dose CBCT reconstruction via joint non-local total variation denoising and cubic B-spline interpolation.

Ho Lee1, Jiyoung Park1, Yeonho Choi1, Kyung Ran Park2, Byung Jun Min3, Ik Jae Lee4.   

Abstract

This study develops an improved Feldkamp-Davis-Kress (FDK) reconstruction algorithm using non-local total variation (NLTV) denoising and a cubic B-spline interpolation-based backprojector to enhance the image quality of low-dose cone-beam computed tomography (CBCT). The NLTV objective function is minimized on all log-transformed projections using steepest gradient descent optimization with an adaptive control of the step size to augment the difference between a real structure and noise. The proposed algorithm was evaluated using a phantom data set acquired from a low-dose protocol with lower milliampere-seconds (mAs).The combination of NLTV minimization and cubic B-spline interpolation rendered the enhanced reconstruction images with significantly reduced noise compared to conventional FDK and local total variation with anisotropic penalty. The artifacts were remarkably suppressed in the reconstructed images. Quantitative analysis of reconstruction images using low-dose projections acquired from low mAs showed a contrast-to-noise ratio with spatial resolution comparable to images reconstructed using projections acquired from high mAs. The proposed approach produced the lowest RMSE and the highest correlation. These results indicate that the proposed algorithm enables application of the conventional FDK algorithm for low mAs image reconstruction in low-dose CBCT imaging, thereby eliminating the need for more computationally demanding algorithms. The substantial reductions in radiation exposure associated with the low mAs projection acquisition may facilitate wider practical applications of daily online CBCT imaging.

Entities:  

Year:  2021        PMID: 33574477     DOI: 10.1038/s41598-021-83266-1

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  2 in total

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

Authors:  Satoshi Kida; Takahiro Nakamoto; Masahiro Nakano; Kanabu Nawa; Akihiro Haga; Jun'ichi Kotoku; Hideomi Yamashita; Keiichi Nakagawa
Journal:  Cureus       Date:  2018-04-29

2.  Do CNNs Solve the CT Inverse Problem?

Authors:  Emil Y Sidky; Iris Lorente; Jovan G Brankov; Xiaochuan Pan
Journal:  IEEE Trans Biomed Eng       Date:  2021-05-21       Impact factor: 4.756

  2 in total
  1 in total

1.  Impact of Denoising on Deep-Learning-Based Automatic Segmentation Framework for Breast Cancer Radiotherapy Planning.

Authors:  Jung Ho Im; Ik Jae Lee; Yeonho Choi; Jiwon Sung; Jin Sook Ha; Ho Lee
Journal:  Cancers (Basel)       Date:  2022-07-22       Impact factor: 6.575

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.