Evidence map›Paper›PMID 27175377›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2016

Evaluation of static and dynamic perfusion cardiac computed tomography for quantitation and classification tasks.

Michael Bindschadler, Dimple Modgil, Kelley R Branch, Patrick J La Riviere, Adam M Alessio

Abstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 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
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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Michael BindschadlerUniversity of Washington , Department of Radiology, Seattle, Washington 98195, United States.
Dimple ModgilThe University of Chicago , Department of Radiology, Chicago, Illinois 60637, United States.
Kelley R BranchUniversity of Washington , Department of Radiology, Seattle, Washington 98195, United States.
Patrick J La RiviereThe University of Chicago , Department of Radiology, Chicago, Illinois 60637, United States.
Adam M AlessioUniversity of Washington , Department of Radiology, Seattle, Washington 98195, United States.

Funding

Low-dose Myocardial Perfusion Imaging by CTR01HL109327 · NHLBI · UNIVERSITY OF WASHINGTON · PI ALESSIO, ADAM M · 2012 to 2017
$2.2M
NHLBI NIH HHS R01 HL109327
6 · The paper itself

Abstract

Cardiac computed tomography (CT) acquisitions for perfusion assessment can be performed in a dynamic or static mode. Either method may be used for a variety of clinical tasks, including (1) stratifying patients into categories of ischemia and (2) using a quantitative myocardial blood flow (MBF) estimate to evaluate disease severity. In this simulation study, we compare method performance on these classification and quantification tasks for matched radiation dose levels and for different flow states, patient sizes, and injected contrast levels. Under conditions simulated, the dynamic method has low bias in MBF estimates (0 to [Formula: see text]) compared to linearly interpreted static assessment (0.45 to [Formula: see text]), making it more suitable for quantitative estimation. At matched radiation dose levels, receiver operating characteristic analysis demonstrated that the static method, with its high bias but generally lower variance, had superior performance ([Formula: see text]) in stratifying patients, especially for larger patients and lower contrast doses [area under the curve [Formula: see text] to 96 versus 0.86]. We also demonstrate that static assessment with a correctly tuned exponential relationship between the apparent CT number and MBF has superior quantification performance to static assessment with a linear relationship and to dynamic assessment. However, tuning the exponential relationship to the patient and scan characteristics will likely prove challenging. This study demonstrates that the selection and optimization of static or dynamic acquisition modes should depend on the specific clinical task.

Indexed as

cardiac computed tomographycomputed tomography perfusiondynamic computed tomographykinetic modelingperfusion imaging

Identifiers

PMID27175377
PMCPMC4852211

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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.