Evidence map›Paper›PMID 29222954›Full record

ArticleMedical physics2018

Evaluation of motion artifact metrics for coronary CT angiography.

Hongfeng Ma, Eric Gros, Aniko Szabo, Scott G Baginski, Zachary R Laste, Naveen M Kulkarni, Darin Okerlund, Taly G Schmidt

Open access · greenAbstract read
In one paragraph

Article in Medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

12 citing papers in PubMed, 22 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

Hongfeng MaDepartment of Biomedical Engineering at, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Eric GrosGE Healthcare, Waukesha, WI, USA.
Aniko SzaboDivision of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, USA.
Scott G BaginskiDepartment of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA.
Zachary R LasteDepartment of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA.
Naveen M KulkarniDepartment of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA.
Darin OkerlundGE Healthcare, Waukesha, WI, USA.
Taly G SchmidtDepartment of Biomedical Engineering at, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Medical College of Wisconsin · USMarquette University · US

Funding

Clinical and Translational Science AwardUL1TR001436 · NCATS · MEDICAL COLLEGE OF WISCONSIN · PI FREED, JULIE K · 2015 to 2025
$47.5M
NCATS NIH HHS UL1 TR001436
6 · The paper itself

Abstract

purposeThis study quantified the performance of coronary artery motion artifact metrics relative to human observer ratings. Motion artifact metrics have been used as part of motion correction and best-phase selection algorithms for Coronary Computed Tomography Angiography (CCTA). However, the lack of ground truth makes it difficult to validate how well the metrics quantify the level of motion artifact. This study investigated five motion artifact metrics, including two novel metrics, using a dynamic phantom, clinical CCTA images, and an observer study that provided ground-truth motion artifact scores from a series of pairwise comparisons.

methodFive motion artifact metrics were calculated for the coronary artery regions on both phantom and clinical CCTA images: positivity, entropy, normalized circularity, Fold Overlap Ratio (FOR), and Low-Intensity Region Score (LIRS). CT images were acquired of a dynamic cardiac phantom that simulated cardiac motion and contained six iodine-filled vessels of varying diameter and with regions of soft plaque and calcifications. Scans were repeated with different gantry start angles. Images were reconstructed at five phases of the motion cycle. Clinical images were acquired from 14 CCTA exams with patient heart rates ranging from 52 to 82 bpm. The vessel and shading artifacts were manually segmented by three readers and combined to create ground-truth artifact regions. Motion artifact levels were also assessed by readers using a pairwise comparison method to establish a ground-truth reader score. The Kendall's Tau coefficients were calculated to evaluate the statistical agreement in ranking between the motion artifacts metrics and reader scores. Linear regression between the reader scores and the metrics was also performed.

resultsOn phantom images, the Kendall's Tau coefficients of the five motion artifact metrics were 0.50 (normalized circularity), 0.35 (entropy), 0.82 (positivity), 0.77 (FOR), 0.77(LIRS), where higher Kendall's Tau signifies higher agreement. The FOR, LIRS, and transformed positivity (the fourth root of the positivity) were further evaluated in the study of clinical images. The Kendall's Tau coefficients of the selected metrics were 0.59 (FOR), 0.53 (LIRS), and 0.21 (Transformed positivity). In the study of clinical data, a Motion Artifact Score, defined as the product of FOR and LIRS metrics, further improved agreement with reader scores, with a Kendall's Tau coefficient of 0.65.

conclusionThe metrics of FOR, LIRS, and the product of the two metrics provided the highest agreement in motion artifact ranking when compared to the readers, and the highest linear correlation to the reader scores. The validated motion artifact metrics may be useful for developing and evaluating methods to reduce motion in Coronary Computed Tomography Angiography (CCTA) images.

Indexed as

ArtifactsMovementComputed Tomography AngiographyCoronary AngiographyHumansPhantoms, Imagingcardiac imagingCCTAmotion artifacts

Identifiers

PMID29222954
PMCPMC5807165
OpenAlexW2772297088

What OpenQuestion holds

Textmetadata
LicenceTDM
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.