1 research outputs found
Imprecise k-space sampling and central brightening
In real-world sampling of k-space data, one generally makes a stochastic
error not only in the value of the sample but in the effective position of the
drawn sample. We refer to the latter as imprecise sampling and apply this
concept to the fourier-based acquisition of magnetic resonance data. The
analysis shows that the effect of such imprecisely sampled data accounts for
contributions to noise, blurring, and intensity-bias in the image. Under
general circumstances, the blur and the bias may depend on the scanned specimen
itself. We show that for gaussian distributed imprecision of k-vector samples
the resulting intensity inhomogeneity can be explicitly computed. The presented
mechanism of imprecise k-space sampling (IKS) provides a complementary
explanation for the phenomenon of central brightening in high-field magnetic
resonance imaging. In computed experiments, we demonstrate the adequacy of the
IKS effect for explaining central brightening. Furthermore, the experiments
show that basic properties of IKS can in principle be inferred from real MRI
data by the analysis on the basis of bias fields in magnitude images and
information contained in the phase-images.Comment: 11 pages, 3 figure