Evidence map›Paper›PMID 29732006›Full record

ArticleOncotarget2018

Low-dose CT with adaptive statistical iterative reconstruction for evaluation of urinary stone.

Xiaohu Li, Hongmin Shu, Yifei Zhang, Xiaoshu Li, Jian Song, Junhua Du, Yinfeng Qian, Bin Liu, Yongqiang Yu

Abstract read
In one paragraph

Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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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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

2 citing papers in PubMed.

  1. Article
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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

9 authors.

Xiaohu LiDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Hongmin ShuDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Yifei ZhangDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Xiaoshu LiDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Jian SongDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Junhua DuDepartment of Urology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Yinfeng QianDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Bin LiuDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Yongqiang YuDepartment of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo prospectively determine the diagnostic performance of low-dose CT (LDCT) with adaptive statistical iterative reconstruction (ASIR) technique for the detection of urinary stone disease.

resultsThe average DLP and ED was 408.16 ± 119.04 mGy and 6.12 ± 1.79 mSv in CDCT, and 138.19 ± 76.87 mGy and 2.07 ± 1.15 mSv in LDCT, respectively. The dose reduction rate of LDCT was nearly 66.1% for both DLP and ED ( MATERIALS AND

methods70 consetutive patients with clinically suspected urolithiasis underwent non-enhanced CT. Followed by both conventional-dose CT (CDCT) and low-dose CT (LDCT) scans. Automatic tube current modulation (ATCM) scanning was used, with a noise index setting of 13 in CDCT and 25 in LDCT. Reconstructions were performed with filtered back projection (FBP) and different settings of adaptive statistical iterative reconstruction [ASIR(40%, 60%, 80%)]. Urinary calculi (size, location, number), image quality (scale 1-5), image noise (scale 1-3) and diagnostic confidence levels (scale 1-3) were evaluated and measured by two radiologists independently. Radiation dose was recorded by calculating dose length product (DLP) and effective dose (ED). Statistical analyses included Mann-Whitney

conclusionsLDCT with ASIR can reduce the radiation dose while maintain relatively high image quality in the diagnosis of urinary stone diseases.

Indexed as

adaptive statistical iterative reconstructioncomputed tomographylow doseurinary stone

Identifiers

PMID29732006
PMCPMC5929449

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