Semidefinite relaxations in optimal experiment design with application to substrate injection for hyperpolarized MRI
This work provides a rigorous optimization framework for designing substrate injection profiles in hyperpolarized MRI, improving metabolic parameter mapping.
The paper addresses optimal input design for parameter estimation in linear state-space models with amplitude and l1/l2-norm constraints, using semidefinite relaxation. For l2-norm constraints, the relaxation is tight, yielding a globally optimal injection profile; for l1-norm constraints, the boxcar injection achieves at least 98.7% of the global optimum.
We consider the problem of optimal input design for estimating uncertain parameters in a discrete-time linear state space model, subject to simultaneous amplitude and l1/l2-norm constraints on the admissible inputs. We formulate this problem as the maximization of a (non-concave) quadratic function over the space of inputs, and use semidefinite relaxation techniques to efficiently find the global solution or to provide an upper bound. This investigation is motivated by a problem in medical imaging, specifically designing a substrate injection profile for in vivo metabolic parameter mapping using magnetic resonance imaging (MRI) with hyperpolarized carbon-13 pyruvate. In the l2-norm-constrained case, we show that the relaxation is tight, allowing us to efficiently compute a globally optimal injection profile. In the l1-norm-constrained case the relaxation is no longer tight, but can be used to prove that the boxcar injection currently used in practice achieves at least 98.7% of the global optimum.