Evidence map›Paper›PMID 33574477›Full record

ArticleScientific reports2021

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

Ho Lee, Jiyoung Park, Yeonho Choi, Kyung Ran Park, Byung Jun Min, Ik Jae Lee

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In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Low-dose CBCT image reconstruction: a review.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ho LeeDepartment of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Jiyoung ParkDepartment of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Yeonho ChoiDepartment of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.
Kyung Ran ParkDepartment of Radiation Oncology, Kosin University College of Medicine, Busan, Korea.
Byung Jun MinDepartment of Radiation Oncology, Chungbuk National University Hospital, Cheongju, Korea. bjmin@cbnuh.or.kr.
Ik Jae LeeDepartment of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea. ikjae412@yuhs.ac.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Identifiers

PMID33574477
PMCPMC7878746

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.