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

ABSTRACT:

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

SUBMITTER: Meyer NK 

PROVIDER: S-EPMC10268095 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature