Evidence map›Paper›PMID 33392037›Full record

ArticleQuantitative imaging in medicine and surgery2021

Optimization of imaging parameters in chest CT for COVID-19 patients: an experimental phantom study.

Yantao Niu, Shunxing Huang, Huan Zhang, Shuo Li, Xiaoting Li, Zhibin Lv, Shuo Yan, Wei Fan, Yanlong Zhai, Eddy Wong and 5 more

Abstract read
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Is it necessary to define new diagnostic reference levels during pandemics like the Covid19-?Radiation physics and chemistry (Oxford, England : 1993) · 2023
    Article
  4. Optimization in the Context of COVID-19 Prediction and Control: A Literature Review.IEEE access : practical innovations, open solutions · 2021
    Article
  5. Review
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

15 authors.

Yantao NiuDepartment of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Shunxing HuangDepartment of Radiology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Huan ZhangDepartment of Radiology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Shuo LiDepartment of Radiology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Xiaoting LiDepartment of Radiology, Peking University Cancer Hospital and Institute, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Beijing, China.
Zhibin LvDepartment of Radiology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Shuo YanDepartment of Radiology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Wei FanDepartment of Radiology, Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
Yanlong ZhaiDepartment of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
Eddy WongPhilips CT Global Clinical Science, Philips Healthcare, Cleveland, OH, USA.
Kexin WangDepartment of Radiology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Zongrui ZhangDepartment of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
Budong ChenDepartment of Radiology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Ruming XieDepartment of Radiology, Beijing Ditan Hospital, Capital Medical University, Beijing, China.
Junfang XianDepartment of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the global outbreak of coronavirus disease 2019 (COVID-19), chest computed tomography (CT) is vital for diagnosis and follow-up. The increasing contribution of CT to the population-collected dose has become a topic of interest. Radiation dose optimization for chest CT of COVID-19 patients is of importance in clinical practice. The present study aimed to investigate the factors affecting the detection of ground-glass nodules and exudative lesions in chest CT among COVID-19 patients and to find an appropriate combination of imaging parameters that optimize detection while effectively reducing the radiation dose.

methodsThe anthropomorphic thorax phantom, with 9 spherical nodules of different diameters and CT values of -800, -630, and 100 HU, was used to simulate the lesions of COVID-19 patients. Four custom-simulated lesions of porcine fat and ethanol were also scanned at 3 tube potentials (120, 100, and 80 kV) and corresponding milliampere-seconds (mAs) (ranging from 10 to 100). Separate scans were performed at pitches of 0.6, 0.8, 1.0, 1.15, and 1.49, and at collimations of 10, 20, 40, and 80 mm at 80 kV and 100 mAs. CT values and standard deviations of simulated nodules and lesions were measured, and radiation dose quantity (volume CT dose index; CTDIvol) was collected. Contrast-to-noise ratio (CNR) and figure of merit (FOM) were calculated. All images were subjectively evaluated by 2 radiologists to determine whether the nodules were detectable and if the overall image quality met diagnostic requirements.

resultsAll simulated lesions, except -800 HU nodules, were detected at all scanning conditions. At a fixed voltage of 120 or 100 kV, with increasing mAs, image noise tended to decrease, and the CNR tended to increase (F=9.694 and P=0.033 for 120 kV; F=9.028 and P=0.034 for 100 kV). The FOM trend was the same as that of CNR (F=2.768 and P=0.174 for 120 kV; F=1.915 and P=0.255 for 100 kV). At 80 kV, the CNRs and FOMs had no significant change with increasing mAs (F=4.522 and P=0.114 for CNRs; F=1.212 and P=0.351 for FOMs). For the 4 nodules of -800 and -630 HU, CNRs had no statistical differences at each of the 5 pitches (F=0.673, P=0.476). The CNRs and FOMs at each of the 4 collimations had no statistical differences (F=2.509 and P=0.125 for CNRs; F=1.485 and P=0.309 for FOMs) for each nodule. CNRs and subjective evaluation scores increased with increasing parameter values for each imaging iteration. The CNRs of 4 -800 HU nodules in the qualified images at the thresholds of scanning parameters of 120 kV/20 mAs, 100 kV/40 mAs, and 80 kV/80 mAs, had statistical differences (P=0.038), but the FOMs had no statistical differences (P=0.085). Under the 3 threshold conditions, the CNRs and FOMs of the 4 nodules were highest at 100 kV and 40 mAs (1.6 mGy CTDIvol).

conclusionsFor chest CT among COVID-19 patients, it is recommended that 100 kV/40 mAs is used for average patients; the radiation dose can be reduced to 1.6 mGy with qualified images to detect ground-glass nodules and exudation lesions.

Indexed as

computed tomography (CT)Coronavirus disease 2019 (COVID-19)exudation lesionsground-glass nodulesimaging parameters

Identifiers

PMID33392037
PMCPMC7719934

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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.