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

Autor:	Meyer NK; Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, USA., Kang D; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Black DF; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Campeau NG; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Welker KM; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Gray EM; Department of Radiology, Mayo Clinic, Rochester, MN, USA., In MH; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Shu Y; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Huston Iii J; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Bernstein MA; Department of Radiology, Mayo Clinic, Rochester, MN, USA., Trzasko JD; Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Jazyk:	angličtina
Zdroj:	The neuroradiology journal [Neuroradiol J] 2023 Jun; Vol. 36 (3), pp. 273-288. Date of Electronic Publication: 2022 Sep 05.
DOI:	10.1177/19714009221122171
Abstrakt:	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.
Databáze:	MEDLINE
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