DCE-MRI enables the estimation of clinically useful parameters of tissue microvasculature, and is frequently used in trials of anti-angiogenic drugs. However tissue movement can lead to inaccurate parameter estimation. Rapidly changing contrast and limited spatial structure within tumours makes DCE-registration a challenging task. We present a novel algorithm that estimates a model of local tumour motion from the most stable part of the time-series and uses this to constrain registration of the whole series. We demonstrate statistically significant improved extended Kety-model fits and improved parameter repeatability for a set of 59 liver tumours in 40 patients, at two baseline scans.
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