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Abstract #2346

Voxel And Vicinity-based algOrithm for nOise reductiOn of fMri data sets (VAVOOOM).

Ana-Maria Oros-Peusquens*1, Patricia Pais-Roldan*1, Seong Dae Yun1, and N. Jon Shah1,2,3,4,5
1Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Jülich, Germany, 2JARA - BRAIN - Translational Medicine, Aachen, Germany, 3Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Jülich, Germany, 4JARA - BRAIN - Translational Medicine, Forschungszentrum Jülich, Aachen, Germany, 5Department of Neurology, RWTH Aachen University, Aachen, Germany

Synopsis

Keywords: fMRI Analysis, fMRI Analysis, Denoising

Motivation: Improve the low SNR in high-resolution fMRI and show the benefit of denoising strategies.

Goal(s): Use a combined Hankel-PCA denoising method to remove thermal noise at the voxel as well as vicinity level, in contrast to existing methods exploiting vicinity-based PCA algorithms.

Approach: High resolution task fMRI data were obtained using two protocols: 65 slices/2s TR and 5 slices/0.5s TR. Data were denoised with 7 different settings (voxel and vicinity-based combinations) and tSNR, task-related activation and temporal dynamics were evaluated.

Results: tSNR improved with all denoising strategies. VAVOOOM, the proposed denoising method, shows promise for high-resolution applications and laminar fMRI.

Impact: The presented denoising method (VAVOOOM) can be implemented in pre-processing pipelines to improve the tSNR and facilitate accurate characterization of fMRI metrics in high-resolution data such as laminar resolution fMRI at ultra-high fields.

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Keywords