  Quantitative ultrasound (QUS) offers a non-invasive and objective way to
quantify tissue health. We recently presented a spatially adaptive
regularization method for reconstruction of a single QUS parameter, limited to
a two dimensional region. That proof-of-concept study showed that
regularization using homogeneity prior improves the fundamental
precision-resolution trade-off in QUS estimation. Based on the weighted
regularization scheme, we now present a multiparametric 3D weighted QUS (3D
QUS)imaging system, involving the reconstruction of three QUS parameters:
attenuation coefficient estimate (ACE), integrated backscatter coefficient
(IBC) and effective scatterer diameter (ESD). With the phantom studies, we
demonstrate that our proposed method accurately reconstructs QUS parameters,
resulting in high reconstruction contrast and therefore improved diagnostic
utility. Additionally, the proposed method offers the ability to analyze the
spatial distribution of QUS parameters in 3D, which allows for superior tissue
characterization. We apply a three-dimensional total variation regularization
method for the volumetric QUS reconstruction. The 3D regularization involving N
planes results in a high QUS estimation precision, with an improvement of
standard deviation over the theoretical rate achievable by compounding N
independent realizations. In the in vivo liver study, we demonstrate the
advantage of adopting a multiparametric approach over the single parametric
counterpart, where a simple quadratic discriminant classifier using feature
combination of three QUS parameters was able to attain a perfect classification
performance to distinguish between normal and fatty liver cases.
