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

Nolan K. Meyer, Daehun Kang, David Black, Norbert G. Campeau, Kirk M. Welker, Erin M. Gray, Myung Ho In, Yunhong Shu, John Huston, Matthew 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)
JournalNeuroradiology Journal
StateAccepted/In press - 2022


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

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology


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