Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data

Nolan K. Meyer, Daehun Kang, David F. Black, Norbert G. Campeau, Kirk M. Welker, Erin M. Gray, Myung Ho In, Yunhong Shu, John Huston, Matt A. Bernstein, Joshua D. Trzasko

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)273-288
Number of pages16
JournalNeuroradiology Journal
Issue number3
StatePublished - Jun 2023


  • fMRI
  • functional MRI, denoising
  • presurgical fMRI
  • task-based fMRI

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology


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