Evidence map›Paper›PMID 40762194›Full record

ArticleMagnetic resonance in medicine2025

Optimization of deep learning-based denoising for arterial spin labeling: Effects of averaging and training strategies.

Jia Guo, Arun Sharma, Greg Zaharchuk, Hossein Rahimzadeh, Naveed Ilyas

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Magnetic resonance in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Jia GuoDepartment of Bioengineering, University of California Riverside, Riverside, California, USA.ORCID https://orcid.org/0000-0003-3371-5857
Arun SharmaDepartment of Electrical Engineering, University of California Riverside, Riverside, California, USA.
Greg ZaharchukDepartment of Radiology, Stanford University, Stanford, California, USA.
Hossein RahimzadehDepartment of Bioengineering, University of California Riverside, Riverside, California, USA.
Naveed IlyasDepartment of Bioengineering, University of California Riverside, Riverside, California, USA.

Funding

Ultra-fast cerebral blood flow imaging for quantifying brain dynamicsR01EB033210 · NIBIB · UNIVERSITY OF CALIFORNIA RIVERSIDE · PI GUO, JIA · 2022 to 2022
$623k
NIBIB NIH HHS R01 EB033210NIH HHS R01EB033210
6 · The paper itself

Abstract

purposeSystematic study of the effects of averaging and other relevant training strategies in deep learning (DL)-based denoising is required to optimize such processing pipelines for improving the quality of arterial spin labeling (ASL) images.

methodsDifferent averaging strategies, including windowed and interleaved averaging methods, and different levels of averaging before and after convolutional neural network-based and transformer-based denoising were studied. The experiments were performed on 152 single-delay ASL scans from 152 subjects, including pulsed and pseudo-continuous ASL acquisitions. Four-fold cross-validation was implemented in all experiments. The effect of including calibration scans (M

resultsIncluding M

conclusionThis study supports the use of DL-based denoising in improving the image quality of ASL and reducing scan time and provides insights to help optimize DL-denoising pipelines.

Indexed as

ArteriesBrainDeep LearningImage Processing, Computer-AssistedMagnetic Resonance ImagingSpin LabelsAdultAlgorithmsFemaleHumansMaleNeural Networks, ComputerSignal-To-Noise RatioSpin Labelsarterial spin labelingaveragingconvolutional neural networkdeep learningdenoising

Identifiers

PMID40762194
PMCPMC12501684

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.