Evidence map›Paper›PMID 24614352›Full record

ArticlePhysics in medicine and biology2014

Comparison of blood flow models and acquisitions for quantitative myocardial perfusion estimation from dynamic CT.

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

Abstract readComparative Study
In one paragraph

Article in Physics in medicine and biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.

0numbers the graph read from it
0cells of the map it votes in
31citing 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

31 citing papers in PubMed.

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  18. SLIC robust (SLICR) processing for fast, robust CT myocardial blood flow quantification.Proceedings of SPIE--the International Society for Optical Engineering · 2018
    Article
  19. [Redundancy information-induced image reconstruction for low-dose myocardial perfusion computed tomography].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2018
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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

5 authors.

Michael BindschadlerDepartment of Bioengineering University of Washington, Seattle, WA 98195, US. Department of Radiology, University of Washington, Seattle, WA 98195, US.
Dimple Modgil
Kelley R Branch
Patrick J La Riviere
Adam M Alessio

Funding

The Virtual Physiological Rat ProjectP50GM094503 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI BEARD, DANIEL A · 2011 to 2015
$11.6M
Low-dose Myocardial Perfusion Imaging by CTR01HL109327 · NHLBI · UNIVERSITY OF WASHINGTON · PI ALESSIO, ADAM M · 2012 to 2017
$2.2M
Tailored Algorithms for Non-Contrast Computed Tomography Using Sinogram RestoratiR01CA134680 · NCI · UNIVERSITY OF CHICAGO · PI LA RIVIERE, PATRICK JEAN · 2008 to 2013
$1.3M
NCI NIH HHS R01 CA134680NHLBI NIH HHS R01 HL109327NHLBI NIH HHS R01HL109327NIGMS NIH HHS P50 GM094503
6 · The paper itself

Abstract

Myocardial blood flow (MBF) can be estimated from dynamic contrast enhanced (DCE) cardiac CT acquisitions, leading to quantitative assessment of regional perfusion. The need for low radiation dose and the lack of consensus on MBF estimation methods motivates this study to refine the selection of acquisition protocols and models for CT-derived MBF. DCE cardiac CT acquisitions were simulated for a range of flow states (MBF = 0.5, 1, 2, 3 ml (min g)(-1), cardiac output = 3, 5, 8 L min(-1)). Patient kinetics were generated by a mathematical model of iodine exchange incorporating numerous physiological features including heterogenenous microvascular flow, permeability and capillary contrast gradients. CT acquisitions were simulated for multiple realizations of realistic x-ray flux levels. CT acquisitions that reduce radiation exposure were implemented by varying both temporal sampling (1, 2, and 3 s sampling intervals) and tube currents (140, 70, and 25 mAs). For all acquisitions, we compared three quantitative MBF estimation methods (two-compartment model, an axially-distributed model, and the adiabatic approximation to the tissue homogeneous model) and a qualitative slope-based method. In total, over 11 000 time attenuation curves were used to evaluate MBF estimation in multiple patient and imaging scenarios. After iodine-based beam hardening correction, the slope method consistently underestimated flow by on average 47.5% and the quantitative models provided estimates with less than 6.5% average bias and increasing variance with increasing dose reductions. The three quantitative models performed equally well, offering estimates with essentially identical root mean squared error (RMSE) for matched acquisitions. MBF estimates using the qualitative slope method were inferior in terms of bias and RMSE compared to the quantitative methods. MBF estimate error was equal at matched dose reductions for all quantitative methods and range of techniques evaluated. This suggests that there is no particular advantage between quantitative estimation methods nor to performing dose reduction via tube current reduction compared to temporal sampling reduction. These data are important for optimizing implementation of cardiac dynamic CT in clinical practice and in prospective CT MBF trials.

Indexed as

Coronary CirculationModels, BiologicalMyocardial Perfusion ImagingImage Processing, Computer-AssistedRadiation DosageTime FactorsTomography, X-Ray Computed

Identifiers

PMID24614352
PMCPMC4057043

What OpenQuestion holds

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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.