Evidence map›Paper›PMID 26981452›Full record

ArticleQuantitative imaging in medicine and surgery2016

Arterial input function placement effect on computed tomography lung perfusion maps.

Laura Jimenez-Juan, Hatem Mehrez, Chris Dey, Shabnam Homampour, Anastasia Oikonomou, Fatima Ursani, Narinder Paul

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Quantitative imaging in medicine and surgery, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact, top 99% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Article
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

7 authors at 1 institution in 1 country.

Laura Jimenez-Juan1 Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada ; 2 Department of Medical Imaging, Sunnybrook Health Science Centre, Toronto, Ontario, Canada ; 3 Toshiba Medical Systems, Markham, Ontario, Canada ; 4 Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada ; 5 Department of Biology, University of Toronto, Toronto, Ontario, Canada.
Hatem Mehrez1 Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada ; 2 Department of Medical Imaging, Sunnybrook Health Science Centre, Toronto, Ontario, Canada ; 3 Toshiba Medical Systems, Markham, Ontario, Canada ; 4 Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada ; 5 Department of Biology, University of Toronto, Toronto, Ontario, Canada.
Chris Dey1 Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada ; 2 Department of Medical Imaging, Sunnybrook Health Science Centre, Toronto, Ontario, Canada ; 3 Toshiba Medical Systems, Markham, Ontario, Canada ; 4 Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada ; 5 Department of Biology, University of Toronto, Toronto, Ontario, Canada.
Shabnam Homampour1 Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada ; 2 Department of Medical Imaging, Sunnybrook Health Science Centre, Toronto, Ontario, Canada ; 3 Toshiba Medical Systems, Markham, Ontario, Canada ; 4 Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada ; 5 Department of Biology, University of Toronto, Toronto, Ontario, Canada.
Anastasia Oikonomou1 Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada ; 2 Department of Medical Imaging, Sunnybrook Health Science Centre, Toronto, Ontario, Canada ; 3 Toshiba Medical Systems, Markham, Ontario, Canada ; 4 Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada ; 5 Department of Biology, University of Toronto, Toronto, Ontario, Canada.
Fatima Ursani1 Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada ; 2 Department of Medical Imaging, Sunnybrook Health Science Centre, Toronto, Ontario, Canada ; 3 Toshiba Medical Systems, Markham, Ontario, Canada ; 4 Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada ; 5 Department of Biology, University of Toronto, Toronto, Ontario, Canada.
Narinder Paul1 Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada ; 2 Department of Medical Imaging, Sunnybrook Health Science Centre, Toronto, Ontario, Canada ; 3 Toshiba Medical Systems, Markham, Ontario, Canada ; 4 Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada ; 5 Department of Biology, University of Toronto, Toronto, Ontario, Canada.
University of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA critical source of variability in dynamic perfusion computed tomography (DPCT) is the arterial input function (AIF). However, the impact of the AIF location in lung DPCT has not been investigated yet. The purpose of this study is to determine whether the location of the AIF within the central pulmonary arteries influences the accuracy of lung DPCT maps.

methodsA total of 54 lung DPCT scans were performed in three pigs using different rates and volumes of iodinated contrast media. Pulmonary blood flow (PBF) perfusion maps were generated using first-pass kinetics in three different AIF locations: the main pulmonary trunk (PT), the right main (RM) and the left main (LM) pulmonary arteries. A total of 162 time density curves (TDCs) and corresponding PBF perfusion maps were generated. Linear regression and Spearman's rank correlation coefficient were used to compare the TDCs. PBF perfusion maps were compared quantitatively by taking twenty six regions of interest throughout the lung parenchyma. Analysis of variance (ANOVA) was used to compare the mean PBF values among the three AIF locations. Two chest radiologists performed qualitative assessment of the perfusion maps using a 3-point scale to determine regions of perfusion mismatch.

resultsThe linear regression of the TDCs from the RM and LM compared to the PT had a median (range) of 1.01 (0.98-1.03). The Spearman rank correlation between the TDCs was 0.88 (P<0.05). ANOVA analysis of the perfusion maps demonstrated no statistical difference (P>0.05). Qualitative comparison of the perfusion maps resulted in scores of 1 and 2, demonstrating either identical or comparable maps with no significant difference in perfusion defects between the different AIF locations.

conclusionsAccurate PBF perfusion maps can be generated with the AIF located either at the PT, RM or LM pulmonary arteries.

Indexed as

computed tomography (CT)Perfusion imagingpulmonary artery

Identifiers

PMID26981452
PMCPMC4775248
OpenAlexW2274250433

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.