Accelerated 4D quantitative single point EPR imaging using model‐based reconstruction

Abstract

Electron paramagnetic resonance imaging has surfaced as a promising noninvasive imaging modality that is capable of imaging tissue oxygenation. Due to extremely short spin-spin relaxation times, electron paramagnetic resonance imaging benefits from single-point imaging and inherently suffers from limited spatial and temporal resolution, preventing localization of small hypoxic tissues and differentiation of hypoxia dynamics, making accelerated imaging a crucial issue. In this study, methods for accelerated single-point imaging were developed by combining a bilateral k-space extrapolation technique with model-based reconstruction that benefits from dense sampling in the parameter domain (measurement of the T2 (*) decay of a free induction delay). In bilateral kspace extrapolation, more k-space samples are obtained in a sparsely sampled region by bilaterally extrapolating data from temporally neighboring k-spaces. To improve the accuracy of T2 (*) estimation, a principal component analysis-based method was implemented. In a computer simulation and a phantom experiment, the proposed methods showed its capability for reliable T2 (*) estimation with high acceleration (8-fold, 15-fold, and 30-fold accelerations for 61×61×61, 95×95×95, and 127×127×127 matrix, respectively). By applying bilateral k-space extrapolation and model-based reconstruction, improved scan times with higher spatial resolution can be achieved in the current single-point electron paramagnetic resonance imaging modality.