Abstract #3127

Improved pulmonary oxygen-enhanced MRI parameter precision enabled by hierarchical Bayesian inference

Josephine H Naish^1,2, Marta Tibiletti¹, Christopher Short^3,4, Tom Semple^3,4, Simon Padley^3,4, Jane C Davies^3,4, and Geoff JM Parker^1,5

¹Bioxydyn Ltd, Manchester, United Kingdom, ²MCMR, Manchester University NHS Foundation Trust, Manchester, United Kingdom, ³National Heart & Lung Institute, Imperial College London, London, United Kingdom, ⁴Royal Brompton Hospital, Guy's & St Thomas’ Trust, London, United Kingdom, ⁵Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom

Synopsis

Keywords: Data Analysis, Lung, Bayes

Lung oxygen-enhanced MRI can provide regional information relating to lung function, but voxel-wise parameter estimation is hampered by low SNR. Here we present a hierarchical Bayesian approach to voxel-wise parameter estimation implemented in R and the probabilistic programming language Stan.

In both simulations and in OE-MRI data acquired in patients with cystic fibrosis, the Bayesian approach results in substantially less noisy parameter maps compared to conventional non-linear least squares estimation.

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