Evidence map›Paper›PMID 25698471›Full record

ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2015

Automatic registration of misaligned CT attenuation correction maps in Rb-82 PET/CT improves detection of angiographically significant coronary artery disease.

Piotr J Slomka, Mariana Diaz-Zamudio, Damini Dey, Manish Motwani, Yafim Brodov, David Choi, Sean Hayes, Louise Thomson, John Friedman, Guido Germano and 1 more

Abstract read
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In one paragraph

Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing 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

21 citing papers in PubMed.

  1. Review
  2. Article
  3. Casting aside the creep: harnessing cardiorespiratory dynamics to optimize myocardial flow assessment in cardiac PET.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023
    Article
  4. Moving forward with motion reduction, detection and correction in cardiac PET.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022
    Article
  5. Effect of PET-CT misalignment on the quantitative accuracy of cardiacJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022
    Article
  6. Review
  7. Respiration-averaged CT versus standard CT attenuation map for correction ofJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2022
    Article
  8. Article
  9. Myocardial blood flow: Is motion correction necessary?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021
    Article
  10. Short-term repeatability of myocardial blood flow usingJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021
    Article
  11. Review
  12. 3D PET/CTEuropean journal of nuclear medicine and molecular imaging · 2020
    Article
  13. The continual innovation of commercial PET/CT solutions in nuclear cardiology: Siemens Healthineers.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2018
    Article
  14. Review
  15. Review
  16. Novel SPECT Technologies and Approaches in Cardiac Imaging.Cardiovascular innovations and applications · 2016
    Article
  17. Simultaneous reconstruction of attenuation and activity in cardiac PET can remove CT misalignment artifacts.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2016
    Article
  18. Review
  19. Article
  20. Automatic registration of misaligned CT attenuation correction maps in Rb-82 PET/CT improves detection of angiographically significant coronary artery disease.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2015
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Piotr J SlomkaArtificial Intelligence in Medicine Program, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste A047 N, Los Angeles, CA, 90048, USA. slomkap@cshs.org.
Mariana Diaz-ZamudioArtificial Intelligence in Medicine Program, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste A047 N, Los Angeles, CA, 90048, USA.
Damini DeyDavid Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
Manish MotwaniArtificial Intelligence in Medicine Program, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste A047 N, Los Angeles, CA, 90048, USA.
Yafim BrodovArtificial Intelligence in Medicine Program, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste A047 N, Los Angeles, CA, 90048, USA.
David ChoiArtificial Intelligence in Medicine Program, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste A047 N, Los Angeles, CA, 90048, USA.
Sean HayesArtificial Intelligence in Medicine Program, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste A047 N, Los Angeles, CA, 90048, USA.
Louise ThomsonArtificial Intelligence in Medicine Program, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste A047 N, Los Angeles, CA, 90048, USA.
John FriedmanArtificial Intelligence in Medicine Program, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste A047 N, Los Angeles, CA, 90048, USA.
Guido GermanoArtificial Intelligence in Medicine Program, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste A047 N, Los Angeles, CA, 90048, USA.
Daniel BermanArtificial Intelligence in Medicine Program, Departments of Imaging and Medicine, and Cedars-Sinai Heart Institute, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Ste A047 N, Los Angeles, CA, 90048, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe aimed to evaluate the utility of fully automated software registration intended to improve CT attenuation correction (CTAC) map misalignments during cardiac (82)Rb PET/CT myocardial perfusion imaging (MPI).

methods171 consecutive patients (108 males, mean age 69 years), undergoing both rest-stress (82)Rb PET/CT MPI and invasive coronary angiography within 6 months (mean 14 days, range 0-170), were studied. List mode data were automatically processed in batch mode to generate transaxial attenuation corrected slices with four different CTAC alignment correction strategies: (i) no alignment correction (NONE); (ii) manual correction (MANUAL); (iii) automated 6-parameter rigid correction (AUTO); and (iv) targeted use of automated correction only where PET-CTAC alignment was initially judged as incorrect on either stress or rest scan (AUTO for misalignment only). Initial and final registration quality was graded (1-3) by an experienced radiologist (1: satisfactory alignment (<2 mm misalignment), 2: slight misalignment (2-5 mm in any direction), or 3: poor (>5 mm misalignment in any direction). Total perfusion deficit (TPD) and ischemic TPD (ITPD) were computed automatically, and their diagnostic accuracy to detect significant coronary artery disease with each realignment technique was assessed using receiver operating characteristic analysis.

resultsThe diagnostic accuracy of ITPD, expressed as area under curve, was .81 ± .03 with no alignment correction (NONE), .83 ± .03 with MANUAL correction, .85 ± .03 with AUTO correction (P < .05 vs. NONE and MANUAL), and .87 ± .03 with the targeted use of AUTO correction (P < .05 vs. NONE, MANUAL and AUTO). Both manual and software corrections increased the percentage of cases with satisfactory PET-CTAC map alignment (P < .05 for all) at rest (from 55% for NONE to 80% for MANUAL and 92% for AUTO) and at stress (from 51% for NONE to 78% for MANUAL and 84% for AUTO).

conclusionThe diagnostic accuracy of (82)Rb PET/CT MPI with automated rigid alignment is improved compared to data with no CTAC scan alignment or with manual alignment. The optimal strategy for diagnostic performance is to apply automatic alignment only in cases which are visually identified as misaligned.

Indexed as

ArtifactsSubtraction TechniqueAgedCoronary Artery DiseaseFemaleHumansImage EnhancementMaleMultimodal ImagingPattern Recognition, AutomatedPositron-Emission TomographyReproducibility of ResultsSensitivity and SpecificityTomography, X-Ray Computedattenuation correctioncoronary artery diseaseimage registrationmyocardial perfusion imagingPET/CT

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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.