Evidence map›Paper›PMID 37092852›Full record

ArticleMagnetic resonance in medicine2023

Parametric cerebral blood flow and arterial transit time mapping using a 3D convolutional neural network.

Donghoon Kim, Megan E Lipford, Hongjian He, Qiuping Ding, Vladimir Ivanovic, Samuel N Lockhart, Suzanne Craft, Christopher T Whitlow, Youngkyoo Jung

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

9 authors.

Donghoon KimDepartment of Biomedical Engineering, University of California, Davis, California, USA.ORCID 0000-0002-5479-2707
Megan E LipfordDepartment of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Hongjian HeCenter for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhejiang, China.ORCID 0000-0001-6272-0810
Qiuping DingCenter for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Zhejiang, China.
Vladimir IvanovicDepartment of Radiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Samuel N LockhartDepartment of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Suzanne CraftDepartment of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Christopher T WhitlowDepartment of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Youngkyoo JungDepartment of Biomedical Engineering, University of California, Davis, California, USA.

Funding

Wake Forest University School of Medicine Alzheimer's Disease Research CenterP30AG072947 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI SUZANNE CRAFT · 2021 to 2026
$24.3M
Wake Forest Alzheimer's Disease Core CenterP30AG049638 · NIA · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI RAPP, STEPHEN R · 2016 to 2020
$12.2M
The relationship between early signs of small vessel disease and diffuse white matter disease through imaging biomarkers among people with risk of cognitive decline.RF1NS110043 · NINDS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI JUNG, YOUNGKYOO · 2018 to 2018
$1.9M
NIA NIH HHS P30 AG049638NIA NIH HHS P30 AG072947NIH HHS P30AG049638NIH HHS RF1NS110043NINDS NIH HHS RF1 NS110043
6 · The paper itself

Abstract

purposeTo reduce the total scan time of multiple postlabeling delay (multi-PLD) pseudo-continuous arterial spin labeling (pCASL) by developing a hierarchically structured 3D convolutional neural network (H-CNN) that estimates the arterial transit time (ATT) and cerebral blow flow (CBF) maps from the reduced number of PLDs as well as averages.

methodsA total of 48 subjects (38 females and 10 males), aged 56-80 years, compromising a training group (n = 45) and a validation group (n = 3) underwent MRI including multi-PLD pCASL. We proposed an H-CNN to estimate the ATT and CBF maps using a reduced number of PLDs and a separately reduced number of averages. The proposed method was compared with a conventional nonlinear model fitting method using the mean absolute error (MAE).

resultsThe H-CNN provided the MAEs of 32.69 ms for ATT and 3.32 mL/100 g/min for CBF estimations using a full data set that contains six PLDs and six averages in the 3 test subjects. The H-CNN also showed that the smaller number of PLDs can be used to estimate both ATT and CBF without significant discrepancy from the reference (MAEs of 231.45 ms for ATT and 9.80 mL/100 g/min for CBF using three of six PLDs).

conclusionThe proposed machine learning-based ATT and CBF mapping offers substantially reduced scan time of multi-PLD pCASL.

Indexed as

ArteriesMagnetic Resonance ImagingCerebrovascular CirculationFemaleHumansMaleNeural Networks, ComputerReproducibility of ResultsSpin LabelsSpin Labelsarterial spin labelingarterial transit timecerebral blood flowconvolutional neural networkdeep learning

Identifiers

PMID37092852
PMCPMC10847038

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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.