Qualitative and quantitative evaluation of six algorithms for correcting intensity nonuniformity effects

The desire to correct intensity nonuniformity in magnetic resonance images has led to the proliferation of nonuniformity-correction (NUC) algorithms w ith different theoretical underpinnings. In order to provide end users with a rational basis for selecting a given algorithm for a specific neuroscien tific application, we evaluated the performance of six NUC algorithms. We u sed simulated and real MRI data volumes, including six repeat scans of the same subject, in order to rank the accuracy, precision, and stability of th e nonuniformity corrections. We also compared algorithms using data volumes from different subjects and different (1.5T and 3.0T) MRI scanners in orde r to relate differences in algorithmic performance to intersubject variabil ity and/or differences in scanner performance. In phantom studies, the corr elation of the extracted with the applied nonuniformity was highest in the transaxial (left-to-right) direction and lowest in the axial. (top-to-botto m) direction. Two of the six algorithms demonstrated a high degree of stabi lity, as measured by the iterative application of the algorithm to its corr ected output. While none of the algorithms performed ideally under all circ umstances, locally adaptive methods generally outperformed nonadaptive meth ods. (C) 2001 Academic Press.