A Robust MR-Based Attenuation Map Generation in Short-TE MR Images of the Head Employing Hybrid Spatial Fuzzy C-Means Clustering and Intensity Inhomogeneity Correction
Deriving an accurate attenuation correction map (μ-map) from magnetic resonance (MR) volumes has become an important problem in hybrid PET/MR imaging. Recently, short echo-time (STE) MR imaging technique incorporating fuzzy C-means (FCM) tissue classification and 2-point Dixon image acquisition has been introduced as a feasible approach for segmentation of the bone from air and soft tissue. However, this method imposes additional imaging and the performance of the standard FCM algorithm, suffering from the lack of spatial information, becomes impaired in the presence of inherent noise and intensity inhomogeneity. Here, we exploit a spatial fuzzy C-means (SFCM) segmentation algorithm in combination with a robust intensity inhomogeneity
correction method on single STE-MR images, to differentiate various tissue classes.