Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data.
Objective: This 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. Methods: Task-based motor and language fMRI was ob...
| Publicado en: | Neuroradiology Journal Vol. 36; no. 3; pp. 273 - 289 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Jun2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=164047213&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 164047213 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19714009 6CL0 jtl: Neuroradiology Journal issn: 19714009 maglogo: Y pubinfo: dt: Jun2023 vid: 36 iid: 3 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 164047213 158899437 164047213 164047213 10.1177/19714009221122171 164047213 ppf: 273 ppct: 16 formats: tig: atl: Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data. aug: au: Meyer, Nolan K Kang, Daehun Black, David F Campeau, Norbert G Welker, Kirk M Gray, Erin M In, Myung-Ho Shu, Yunhong Huston III, John Bernstein, Matt A Trzasko, Joshua D affil: 32864 Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, USA sug: subj: Magnetic Resonance Imaging Methods Diagnostic Imaging Methods Benchmarking Brain Mapping Methods Artifacts Algorithms Methods Signal Processing, Computer Assisted Methods Image Enhancement Methods Task Performance and Analysis Functional Assessment Human Male Female Adult Middle Age Retrospective Design Test-Retest Reliability Consensus Brain Radiography Descriptive Statistics Comparative Studies Funding Source Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Objective: This 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. Methods: Task-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 t -statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject. Results: fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant (p = 4.88×10–4 to p = 0.042; one p = 0.062) increases in consensus t -statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t -statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data. Conclusion: LLR denoising affords robust increases in t -statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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