An artifact often seen in MRI is for the signal intensity to vary smoothly across an image. Variously referred to as RF inhomogeneity, shading artifact, or intensity non-uniformity, it is usually attributed to such factors as poor radio frequency (RF) field uniformity, eddy currents driven by the switching of field gradients, and patient anatomy both inside and outside the field of view.
The perl script nu_correct implements a novel approach to correcting
for intensity non-uniformity in MR data that achieves high performance
without requiring supervision. By making relatively few assumptions
about the data, the method can be applied at an early stage in an
automated data analysis, before a tissue intensity or geometric model
is available. Described as Non-parametric Non-uniform intensity
Normalization (N3), the method is independent of pulse sequence and
insensitive to pathological data that might otherwise violate model
assumptions. To eliminate the dependence of the field estimate on
anatomy, an iterative approach is employed to estimate both the
multiplicative bias field and the distribution of the true tissue
intensities. Preprocessing of MR data using N3 has been
shown [2] to substantially improve the accuracy of
anatomical analysis techniques such as tissue classification and
cortical surface extraction.