The accurate characterization of diffusion process with MRI is compromised by various artefacts including intensity related errors. If not appropriately accounted for, model estimates can become significantly biased resulting in erroneous metrics. Slicewise intensity errors, in particular, are often handled by excluding the entire image or slice information, or by voxelwise robust estimators that experience difficulties in partial volume regions. In this work, we describe a fast and accurate algorithm to detect slicewise outliers and a framework to incorporate this information as data uncertainty in model estimation algorithms.
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