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Abstract #2612

Variability from complexity: assessing IVIM acquisition schemes through parameter estimation uncertainty

Sean C Epstein1, Timothy J. P. Bray2, Margaret A. Hall-Craggs2, and Hui Zhang1
1Department of Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom, 2Centre for Medical Imaging, University College London, London, United Kingdom

This work uses the quantification of parameter-estimation uncertainty to assess the clinical suitability of IVIM acquisition schemes. We apply this analysis to two bone marrow classification tasks. We simulate IVIM data and show that fitting a simplified, biased, diffusion model (ADC), can, under certain clinically relevant conditions, outperform ground-truth IVIM fitting. We further show that, within the same disease, the opposite can also be true: IVIM outperforms ADC. Such results can play an important role in guiding clinical DWI practice and we show that they can be predicted by explicitly quantifying the uncertainty in parameter estimation.

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