  Objective: Estimation of the global optima of multiple model parameters is
valuable in imaging to form a reliable diagnostic image. Given non convexity of
the objective function, it is challenging to avoid from different local minima.
Methods: We first formulate the global searching of multiply parameters to be a
k-D move in the parametric space, and convert parameters updating to be
state-action decision-making problem. We proposed a novel Deep Q-learning of
Model Parameters (DQMP) method for global optimization of model parameters by
updating the parameter configurations through actions that maximize a Q-value,
which employs a Deep Reward Network designed to learn global reward values from
both visible curve fitting errors and hidden parameter errors. Results: The
DQMP method was evaluated by viscoelastic imaging on soft matter by
Kelvin-Voigt fractional derivative (KVFD) modeling. In comparison to other
methods, imaging of parameters by DQMP yielded the smallest errors (< 2%) to
the ground truth images. DQMP was applied to viscoelastic imaging on biological
tissues, which indicated a great potential of imaging on physical parameters in
diagnostic applications. Conclusions: DQMP method is able to achieve global
optima, yielding accurate model parameter estimates in viscoelastic imaging.
Assessment of DQMP by simulation imaging and ultrasound breast imaging
demonstrated the consistency, reliability of the imaged parameters, and
powerful global searching ability of DQMP. Significance: DQMP method is
promising for imaging of multiple parameters, and can be generalized to global
optimization for many other complex nonconvex functions and imaging of physical
parameters.
