OEDIPUS: An Experiment Design Framework for Sparsity-Constrained MRI

TL;DR

OEDIPUS is a deterministic experiment design framework for MRI that optimizes sampling patterns based on sparsity constraints, improving upon random sampling methods by tailoring to specific imaging contexts.

Contribution

The paper introduces OEDIPUS, a novel framework combining the constrained Cramér-Rao bound with experiment design for MRI, enabling automatic, context-aware sampling pattern optimization.

Findings

OEDIPUS outperforms traditional random sampling in MRI reconstruction quality.

OEDIPUS adapts sampling patterns to coil geometry and anatomy.

Retrospective experiments show improved image reconstruction with OEDIPUS.

Abstract

This paper introduces a new estimation-theoretic framework for experiment design in the context of MR image reconstruction under sparsity constraints. The new framework is called OEDIPUS (Oracle-based Experiment Design for Imaging Parsimoniously Under Sparsity constraints), and is based on combining the constrained Cram\'{e}r-Rao bound with classical experiment design techniques. Compared to popular random sampling approaches, OEDIPUS is fully deterministic and automatically tailors the sampling pattern to the specific imaging context of interest (i.e., accounting for coil geometry, anatomy, image contrast, etc.). OEDIPUS-based experiment designs are evaluated using retrospectively subsampled in vivo MRI data in several different contexts. Results demonstrate that OEDIPUS-based experiment designs have some desirable characteristics relative to conventional MRI sampling approaches.