Valentin Hamy1, Simon Walker-Samuel2, David Atkinson1, Shonit Punwani1
1Centre for Medical Imaging, UCL, London, United Kingdom; 2Centre for Advanced Biomedical Imaging, UCL, London, United Kingdom
The problem of apparent diffusion coefficient (ADC) estimation from Rician distributed diffusion-weighted magnetic resonance (DW-MR) data is addressed. The least squares (LS) algorithm, widely used in clinical practice, is known to produce biased estimates as it considers the noise as normally distributed. Maximum likelihood (ML) can provide a more robust alternative. In this study based on prostate cancer DW-MR, we compared LS and ML efficiency, for signal to noise ratios typical of the different types of tissue. The ML approach provided significantly less biased estimates than the LS, potentially allowing better accuracy in prostate cancer grading from MR images.