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Abstract #1408

Fitting to Magnitude Diffusion MRI Data Using a Least Squares Algorithm Gives Biased ADC Values and Is Less Able to Characterise Necrosis

Simon Walker-Samuel1, Matthew Orton1, Lesley D. McPhail1, Simon P. Robinson1

1Cancer Research UK Clinical Magnetic Resonance Research Group, Institute of Cancer Research, Sutton, Surrey, UK

The noise in magnitude MR data is Rice-distributed, and it has been shown previously that fitting using a least-squares algorithm leads to biased ADC estimates. In this study, the magnitude of this bias in orthotopic PC3 tumours is investigated and compared with a robust maximum likelihood approach. It is found that least-squares over-estimates ADC by an average of 23.4 12 %.. More significantly, regions of histologically-confirmed necrosis are not identifiable in ADC maps from the least-squares algorithm, but can be clearly observed in maximum-likelihood maps.