Evidence map›Paper›PMID 29749048›Full record

ArticleJournal of applied clinical medical physics2018

Evaluation of projection- and dual-energy-based methods for metal artifact reduction in CT using a phantom study.

Zaiyang Long, Michael R Bruesewitz, David R DeLone, Jonathan M Morris, Kimberly K Amrami, Mark C Adkins, Katrina N Glazebrook, James M Kofler, Shuai Leng, Cynthia H McCollough and 3 more

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in Journal of applied clinical medical physics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

13 citing papers in PubMed, 32 citations in OpenAlex.

  1. Observational
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Comparison of quantitative measurements of four manufacturer's metal artifact reduction techniques for CT imaging with a self-made acrylic phantom.Technology and health care : official journal of the European Society for Engineering and Medicine · 2020
    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

13 authors at 2 institutions in 1 country.

Zaiyang LongDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Michael R BruesewitzDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
David R DeLoneDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Jonathan M MorrisDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Kimberly K AmramiDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Mark C AdkinsDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Katrina N GlazebrookDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
James M KoflerDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Shuai LengDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Cynthia H McColloughDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Joel G FletcherDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Ahmed F HalaweishSiemens Healthcare, Malvern, PA, USA.
Lifeng YuDepartment of Radiology, Mayo Clinic, Rochester, MN, USA.
Mayo Clinic in Arizona · USSiemens Healthcare (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesBoth projection and dual-energy (DE)-based methods have been used for metal artifact reduction (MAR) in CT. The two methods can also be combined. The purpose of this work was to evaluate these three MAR methods using phantom experiments for five types of metal implants. MATERIALS AND

methodsFive phantoms representing spine, dental, hip, shoulder, and knee were constructed with metal implants. These phantoms were scanned using both single-energy (SE) and DE protocols with matched radiation output. The SE data were processed using a projection-based MAR (iMAR, Siemens) algorithm, while the DE data were processed to generate virtual monochromatic images at high keV (Mono+, Siemens). In addition, the DE images after iMAR were used to generate Mono+ images (DE iMAR Mono+). Artifacts were quantitatively evaluated using CT numbers at different regions of interest. Iodine contrast-to-noise ratio (CNR) was evaluated in the spine phantom. Three musculoskeletal radiologists and two neuro-radiologists independently ranked the artifact reduction.

resultsThe DE Mono+ at high keV resulted in reduced artifacts but also lower iodine CNR. The iMAR method alone caused missing tissue artifacts in dental phantom. DE iMAR Mono+ caused wrong CT numbers in close proximity to the metal prostheses in knee and hip phantoms. All musculoskeletal radiologists ranked SE iMAR > DE iMAR Mono+ > DE Mono+ for knee and hip, while DE iMAR Mono+ > SE iMAR > DE Mono+ for shoulder. Both neuro-radiologists ranked DE iMAR Mono+ > DE Mono+ > SE iMAR for spine and DE Mono+ > DE iMAR Mono+ > SE iMAR for dental.

conclusionsThe SE iMAR was the best choice for the hip and knee prostheses, while DE Mono+ at high keV was best for dental implants and DE iMAR Mono+ was best for spine and shoulder prostheses. Artifacts were also introduced by MAR algorithms.

Indexed as

ArtifactsAlgorithmsHumansMetalsPhantoms, ImagingTomography, X-Ray ComputedMetalsCTdual energyiMARmetal artifactMono+

Identifiers

PMID29749048
PMCPMC6036383
OpenAlexW2800822337

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.