Abstract #2880

Towards a Geometry Factor for Projection Imaging with Non-Linear Gradient Fields

Jason P. Stockmann¹, Gigi Galiana², Robert Todd Constable³

¹Biomedical Engineering, Yale University, New Haven, CT, United States; ²Diagnostic Radiology, Yale University, New Haven, CT, United States; ³Diagnostic Radiology, Neurosurgery, and Biomedical Engineering, Yale University, New Haven, CT, United States

Conventional parallel imaging performance is assessed either by computing the analytical geometry factor or, if necessary, comparing the SNRs of fully-sampled and undersampled Monte Carlo reconstructions. The empirical g-factor is unsuitable, however, for methods such as O-Space imaging in which non-linear gradients are used to obtain projections of the object. Since O-Space point spread functions are highly variable with position, the g-factor must be corrected for voxel-size in order to distinguish intra-voxel blurring from true noise amplification. This work shows the limited utility of uncorrected empirical g-factors for O-Space imaging and discusses how to compute the PSF for this class of non-linear projection imaging methods.