ArticleThe neuroradiology journal2023
Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data.
Article in The neuroradiology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Smooth optimization using global and local low-rank regularizers.SIAM journal on imaging sciences · 2026Article
- Spatiotemporal Maps for Dynamic MRI Reconstruction.IEEE transactions on computational imaging · 2026Article
- Locally Low-Rank Denoising of Multi-Echo Functional MRI Data With Application in Resting-State Analysis.Topics in magnetic resonance imaging : TMRI · 2023Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThis study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps.
methodsTask-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked
resultsfMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant (
conclusionLLR denoising affords robust increases in
Indexed as
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Registered trials
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.