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Optimal SSFP Pulse-Sequence Design for Tissue Density Estimation
Zhuo ZhengAdvanced Optimization LabMcMaster UniversityJoint Work with C. Anand, R. Sotirov, T. Terlaky
Motivation
MRI is widely used in diagnosis, treatment monitoring and research.
Quantitatively determining different tissue
types is crucial.
Exploring the applicability of optimization
in biomedical engineering research.
The Dynamic System
Magnetization is dependent on several parameters and .
The dynamic system satisfies: The system can be built up from several
components.
SSFP Pulse-Sequence
Fast scanning and good signal-to-noise ratio.
Steady-state is achieved if
Denoted as , we have
with and .
Imaging
For simplicity, we write the results of n experiments as a real 2n vector and m
tissue densities as a real m vector:
MPPI is an unbiased estimator for tissue densities if has full rank.
Objective and Formulation
Objective: choose pulse-sequence design variables such that
the error in the reconstructed densities is
minimized.
Error given by in which is the white measurement noise.
Relaxation
We replace the sines and cosines in the components by unit vectors and
and add the constraints:
Then relax the constraints to:
Trust Region Algorithm for NL-SDO
How to deal with and semidefinite constraint:
Defining a linear SDO-SOCO subproblem
by linearizing the nonlinear constraints
around the current point.
Linearizing :
and its partial derivatives for information.
A Clinical Application
Carotid artery tissue densities estimation
We reconstruct the densities based on the optimal solutions obtained by our formulation.
Concluding Remarks
Innovative method for tissue densities estimation by taking into account many parameters using optimization methods.
Iteratively solving the problem with semi-
definite and highly-nonlinear constraints.
Many interesting applications of our method,
such as brain development studies in infants.