Abstract #5442

Effect of optimised coil-combinations on high-resolution laminar fMRI at 9.4T

Sriranga Kashyap^1,2, Francisco J. Fritz^1,2, Robbert L. Harms¹, Laurentius Huber³, Dimo Ivanov^1,2, Alard Roebroeck^1,2, Benedikt A. Poser^1,2, and Kâmil Uludağ^1,2

¹Department of Cognitive Neuroscience, Maastricht University, Maastricht, Netherlands, ²Maastricht Brain Imaging Centre (MBIC), Maastricht, Netherlands, ³Section on Functional Imaging Methods Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, United States

Despite the availability of more sophisticated coil-combination methods like Roemer and STARC, ultra-high field fMRI studies still use the conventional sum-of-squares (SoS) method for combining the images of the individual coils from multi-channel RF-coil arrays. Here we use a memory-efficient, CPU/GPU accelerated coil-combine toolbox written in Python to compare and characterise the effect of methods such as covariance-weighted sum-of-squares (CovSoS), Roemer and STARC on sub-millimetre resolution GE-EPI laminar fMRI data acquired at 9.4T, and demonstrate the benefit of using optimised coil-combination for UHF fMRI studies.

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