Evidence map›Paper›PMID 29761990›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2017

[A computer tomography assisted method for the automatic detection of region of interest in dynamic kidney images].

Xueping Jing, Xiujuan Zheng, Shaoli Song, Kai Liu

Open access · greenAbstract readEnglish Abstract
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 2 institutions in 1 country.

Xueping JingDepartment of Automation, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, P.R.China.
Xiujuan ZhengDepartment of Automation, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, P.R.China.xiujuanzheng@scu.edu.cn.
Shaoli SongDepartment of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, P.R.China.
Kai LiuDepartment of Automation, School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, P.R.China.
Sichuan University · CNRenji Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Glomerular filtration rate (GFR), which can be estimated by Gates method with dynamic kidney single photon emission computed tomography (SPECT) imaging, is a key indicator of renal function. In this paper, an automatic computer tomography (CT)-assisted detection method of kidney region of interest (ROI) is proposed to achieve the objective and accurate GFR calculation. In this method, the CT coronal projection image and the enhanced SPECT synthetic image are firstly generated and registered together. Then, the kidney ROIs are delineated using a modified level set algorithm. Meanwhile, the background ROIs are also obtained based on the kidney ROIs. Finally, the value of GFR is calculated via Gates method. Comparing with the clinical data, the GFR values estimated by the proposed method were consistent with the clinical reports. This automatic method can improve the accuracy and stability of kidney ROI detection for GFR calculation, especially when the kidney function has been severely damaged.

Indexed as

edge indicator functionGates methodglomerular filtration ratekidney region of interestlevel set

Identifiers

PMID29761990
PMCPMC9935342
OpenAlexW3009581983

What OpenQuestion holds

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Registered trials

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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.