Abstract #3748
Prostate DWI co-registration via maximization of hybrid statistical likelihood and cross-correlation for improved ADC and computed ultra-high b-value DWI calculation
Daniel S. Cho 1 , Farzad Khalvati 2 , Alexander Wong 1 , David A Clausi 1 , and Masoom Haider 2
1
Systems Design Engineering, University of
Waterloo, Waterloo, Ontario, Canada,
2
University
of Toronto, Ontario, Canada
Diffusion weighted imaging (DWI) has gained significant
attention for prostate cancer imaging as its derived
modalities such as apparent diffusion coefficient and
computed high b-value images are commonly employed for
prostate cancer analysis. In this work, a novel
technique to register a set of DWI acquisitions across
multiple b-values was proposed. The proposed
registration adapted b-spline registration with a new
hybrid similarity metric, which utilized statistical
likelihood and cross-correlation. The DWI
co-registration showed the improved contrast-to-noise
ratio on DWI acquisitions across multiple b-values as
well as ADC map.
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