Evidence map›Paper›PMID 42504083›Full record

ArticleMagnetic resonance in medicine2026

Reference-Superimposed Reconstruction (RS-Recon) for Arterial Spin Labeling.

Jia Guo

Abstract read
In one paragraph

Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

1 author.

Jia GuoDepartment of Bioengineering, University of California Riverside, Riverside, California, USA.ORCID https://orcid.org/0000-0003-3371-5857

Funding

Quantitative MRI to automatically delineate intraprostatic tumor for radiation therapyR01CA284172 · NCI · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI GUO, JIA, JIANG, YUN · 2024 to 2025
$1.4M
Ultra-fast cerebral blood flow imaging for quantifying brain dynamicsR01EB033210 · NIBIB · UNIVERSITY OF CALIFORNIA RIVERSIDE · PI GUO, JIA · 2022 to 2022
$623k
NIH HHS R01CA284172NIH HHS R01EB033210
6 · The paper itself

Abstract

purposeConventional arterial spin labeling (ASL) reconstruction suffers from sign-flipping errors and/or noise bias under strong background suppression (BS), limiting SNR optimization. We propose reference-superimposed reconstruction (RS-recon) to enable ideal BS and robust signal recovery.

methodsRS-recon superimposes high-SNR M

resultsRS-recon eliminated subtraction artifacts due to signal sign-flipping, recovered negative ASL signals, and enabled ideal BS for maximizing SNR. It significantly improved ASL signal and tSNR in gray/white matter across all labeling methods and different BS conditions. RS-recon corrected phase errors under low-SNR conditions, preserved signal signs, maintained Gaussian noise distribution, remained robust against misalignments between the reference and the ASL images, and integrated well with GRAPPA. Mag + RS performance matched complex-based (ComplexSub and Complex) RS pipelines and is recommended for its simplicity, compatibility, and robustness overall.

conclusionRS-recon offers a simple, robust strategy for ASL image reconstruction, resolving subtraction errors even with magnitude output and enabling optimal background suppression. It enhances signal fidelity, SNR, and phase accuracy while integrating seamlessly with existing reconstruction pipelines and parallel imaging.

Indexed as

BrainImage Processing, Computer-AssistedMagnetic Resonance ImagingAdultAlgorithmsArtifactsCerebrovascular CirculationFemaleHumansMalePerfusion Magnetic Resonance ImagingSignal-To-Noise RatioSpin LabelsSpin Labelsarterial spin labelingbackground suppressionimage reconstructionlow‐SNR signal recoveryphase estimation

Identifiers

PMID42504083
PMCPMC13619874

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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.