Optimization of data acquisition for EPR imaging.
In electron paramagnetic resonance imaging (EPRI), long data acquisition time is one of the major problems limiting successful and useful biological EPRI experiments. Depending on the configuration (spatial distribution of paramagnetic species), information embedded in some objects can be characterized using a smaller number of projections, while others may require significantly larger number of projections to generate similar results. In order to optimize the acquisition process, it is therefore important to acquire a different number of projections for different objects. In this paper, a prediction scheme is demonstrated that can determine the number of projections required to achieve a preset reconstruction quality for a given object. After acquiring first few projections, corresponding partially filled k-space is analyzed. The complexity of data (to interpolate) in k-space is quantified and used to predict the number of required projections. All the projections are acquired using a mean-square difference-based adaptive acquisition technique that is also demonstrated in this work. The purpose of this non-uniform acquisition is to reduce redundancy in the acquired data which in turn decreases the number of projections required for the given object. It is also demonstrated that the performance of non-uniform acquisition is content dependant, and for certain configurations it may not be as effective as uniform acquisition in preserving signal from low intensity regions. The prediction scheme along with the non-uniform acquisition is tested using computer simulations, imaging of experimental phantoms, and in vivo imaging. Results indicate that the proposed method may save up to 50% of acquisition time. The techniques in this manuscript are described for 2D spatial imaging but can be extended to 3D imaging.
Ahmad
Rizwan
R
Department of Electrical and Computer Engineering, The Ohio State University, College of Engineering, Columbus, Ohio 43210, USA.
Clymer
Bradley
B
Deng
Yuanmu
Y
He
Guanglong
G
Vikram
Deepti
D
Kuppusamy
Periannan
P
Zweier
Jay L
JL
D000465:Algorithms; D000818:Animals; D004578:Electron Spin Resonance Spectroscopy / Q000379:methods*; D005354:Fibrosarcoma / Q000473:pathology*; D006614:Hindlimb; D007091:Image Processing, Computer-Assisted; D051379:Mice; D009374:Neoplasms, Experimental; D019047:Phantoms, Imaging