Evidence map›Paper›PMID 32492074›Full record

ArticlePloS one2020

The impact of atlas-based MR attenuation correction on the diagnosis of FDG-PET/MR for Alzheimer's diseases- A simulation study combining multi-center data and ADNI-data.

Tetsuro Sekine, Alfred Buck, Gaspar Delso, Bradley Kemp, Edwin E G W Ter Voert, Martin Huellner, Patrick Veit-Haibach, Sandeep Kaushik, Florian Wiesinger, Geoffrey Warnock and 1 more

Abstract readEvaluation Study
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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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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

4 citing papers in PubMed.

  1. Review
  2. DeepDixon synthetic CT for [Frontiers in neuroscience · 2023
    Article
  3. Attenuation correction for human PET/MRI studies.Physics in medicine and biology · 2020
    Article
  4. 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

11 authors.

Tetsuro SekineDepartment of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.ORCID 0000-0003-1547-6696
Alfred BuckDepartment of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.
Gaspar DelsoGE Healthcare, Waukesha, WI, United States of America.
Bradley KempDepartment of Radiology, Mayo Clinic, Rochester, MN, United States of America.
Edwin E G W Ter VoertDepartment of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.
Martin HuellnerDepartment of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.ORCID 0000-0002-4849-3292
Patrick Veit-HaibachDepartment of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.
Sandeep KaushikGE Healthcare, Waukesha, WI, United States of America.
Florian WiesingerGE Healthcare, Waukesha, WI, United States of America.
Geoffrey WarnockInstitute of Pharmacology & Toxicology, University of Zurich, Zurich, Switzerland.
Alzheimer’s Disease Neuroimaging Initiative

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

backgroundThe purpose of this study was to assess the impact of vendor-provided atlas-based MRAC on FDG PET/MR for the evaluation of Alzheimer's disease (AD) by using simulated images.

methodsWe recruited 47 patients, from two institutions, who underwent PET/CT and PET/MR (GE SIGNA) examination for oncological staging. From the PET raw data acquired on PET/MR, two FDG-PET series were generated, using vendor-provided MRAC (atlas-based) and CTAC. The following simulation steps were performed in MNI space: After spatial normalization and smoothing of the PET datasets, we calculated the error map for each patient, PETMRAC/PETCTAC. We multiplied each of these 47 error maps with each of the 203 Alzheimer's Disease Neuroimaging Initiative (ADNI) cases after the identical normalization and smoothing. This resulted in 203*47 = 9541 datasets. To evaluate the probability of AD in each resulting image, a cumulative t-value was calculated automatically using commercially-available software (PMOD PALZ) which has been used in multiple large cohort studies. The diagnostic accuracy for the discrimination of AD and predicting progression from mild cognitive impairment (MCI) to AD were evaluated in simulated images compared with ADNI original images.

resultsThe accuracy and specificity for the discrimination of AD-patients from normal controls were not substantially impaired, but sensitivity was slightly impaired in 5 out of 47 datasets (original vs. error; 83.2% [CI 75.0%-89.0%], 83.3% [CI 74.2%-89.8%] and 83.1% [CI 75.6%-88.3%] vs. 82.7% [range 80.4-85.0%], 78.5% [range 72.9-83.3%,] and 86.1% [range 81.4-89.8%]). The accuracy, sensitivity and specificity for predicting progression from MCI to AD during 2-year follow-up was not impaired (original vs. error; 62.5% [CI 53.3%-69.3%], 78.8% [CI 65.4%-88.6%] and 54.0% [CI 47.0%-69.1%] vs. 64.8% [range 61.5-66.7%], 75.7% [range 66.7-81.8%,] and 59.0% [range 50.8-63.5%]). The worst 3 error maps show a tendency towards underestimation of PET scores.

conclusionFDG-PET/MR based on atlas-based MR attenuation correction showed similar diagnostic accuracy to the CT-based method for the diagnosis of AD and the prediction of progression of MCI to AD using commercially-available software, although with a minor reduction in sensitivity.

Indexed as

Magnetic Resonance ImagingPositron Emission Tomography Computed TomographyAgedAged, 80 and overAlzheimer DiseaseBrainCognitive DysfunctionComputer SimulationDatasets as TopicDiagnosis, DifferentialDisease ProgressionFemaleFluorodeoxyglucose F18Follow-Up StudiesHumansImage Processing, Computer-AssistedFluorodeoxyglucose F18Radiopharmaceuticals

Identifiers

PMID32492074
PMCPMC7269241

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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.