Evidence map›Paper›PMID 26486938›Full record

ArticleEuropean radiology2016

Comparison of the image qualities of filtered back-projection, adaptive statistical iterative reconstruction, and model-based iterative reconstruction for CT venography at 80 kVp.

Jin Hyeok Kim, Ki Seok Choo, Tae Yong Moon, Jun Woo Lee, Ung Bae Jeon, Tae Un Kim, Jae Yeon Hwang, Myeong-Ja Yun, Dong Wook Jeong, Soo Jin Lim

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Article in European radiology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.5field-weighted citation impact, top 8% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 23 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 2 institutions in 1 country.

Jin Hyeok KimDepartment of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Pusan, Korea.
Ki Seok ChooDepartment of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Pusan, Korea. kschoo0618@naver.com.
Tae Yong MoonDepartment of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Pusan, Korea.
Jun Woo LeeDepartment of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Pusan, Korea.
Ung Bae JeonDepartment of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Pusan, Korea.
Tae Un KimDepartment of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Pusan, Korea.
Jae Yeon HwangDepartment of Radiology, Research Institute for Convergence of Biomedical Science and Technology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Pusan, Korea.
Myeong-Ja YunDepartment of Preventive and Occupational Medicine, School of Medicine, Pusan National University, Pusan, Korea.
Dong Wook JeongDepartment of Family Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Pusan, Korea.
Soo Jin LimDepartment of Cardiology, Kimhae Jungang Hospital, Gyeongsangnam-do, Korea.
Pusan National University Yangsan Hospital · KRPusan National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo evaluate the subjective and objective qualities of computed tomography (CT) venography images at 80 kVp using model-based iterative reconstruction (MBIR) and to compare these with those of filtered back projection (FBP) and adaptive statistical iterative reconstruction (ASIR) using the same CT data sets. MATERIALS AND

methodsForty-four patients (mean age: 56.1 ± 18.1) who underwent 80 kVp CT venography (CTV) for the evaluation of deep vein thrombosis (DVT) during 4 months were enrolled in this retrospective study. The same raw data were reconstructed using FBP, ASIR, and MBIR. Objective and subjective image analysis were performed at the inferior vena cava (IVC), femoral vein, and popliteal vein.

resultsThe mean CNR of MBIR was significantly greater than those of FBP and ASIR and images reconstructed using MBIR had significantly lower objective image noise (p < .001). Subjective image quality and confidence of detecting DVT by MBIR group were significantly greater than those of FBP and ASIR (p < .005), and MBIR had the lowest score for subjective image noise (p < .001).

conclusionCTV at 80 kVp with MBIR was superior to FBP and ASIR regarding subjective and objective image qualities. KEY POINTS: • MBIR provides superior image quality compared with FBP and ASIR • CTV at 80kVp with MBIR improves diagnostic confidence in diagnosing DVT • CTV at 80kVp with MBIR presents better image quality with low radiation.

Indexed as

Radiation DosageAdultAgedAlgorithmsFemaleFemoral VeinHumansMaleMiddle AgedPhlebographyPopliteal VeinRadiographic Image EnhancementRadiographic Image Interpretation, Computer-AssistedReproducibility of ResultsRetrospective StudiesTomography, X-Ray ComputedDiagnosisLower extremityMultidetector computed tomographyRadiographyVeins

Identifiers

PMID26486938
OpenAlexW1762057641

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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.