Abstract #0048

Bayesian correction of bias field and Venetian blind for high resolution ex vivo MRI with clinical scanners

Juan Eugenio Iglesias¹, Pedro Manuel Paz-Alonso¹, Garikoitz Lerma-Usabiaga¹, Ricardo Insausti², Karla Miller³, and César Caballero-Gaudes¹

¹Basque Center on Cognition, Brain and Language (BCBL), Donostia - San Sebastián, Spain, ²Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Albacete, Spain, ³Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom

Multi-slab MRI enables the acquisition of ultra-high resolution ex vivo MRI of the whole human brain with clinical scanners, by overcoming their hardware limitations (e.g., memory size). However, multi-slab MRI produces slab boundary artifacts (SBA) that degrade the image quality and bias subsequent image analyses. Here we propose a Bayesian method that corrects for SBA and intensity inhomogeneities / bias field (BF) simultaneously. The method, which combines a probabilistic brain atlas and the Expectation Maximization algorithm, takes advantage of the interplay between the two artifacts to outperform state-of-the-art SBA and BF correction algorithms (even when used in combination).

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