Skull segmentation of UTE MR images by probabilistic neural network for attenuation correction in PET/MR

Aim: Due to space and technical limitations in PET/MR scanners one of the difficulties is the generation of an attenuation correction (AC) map to correct the PET image data. Different methods have been suggested that make use of the images acquired with an ultrashort echo time (UTE) sequence. However, in most of them precise thresholds need to be defined and these may depend on the sequence parameters. In this study an algorithm based on a probabilistic neural network (PNN) is presented requiring little user interaction. Material and methods: An MR UTE sequence delivering two images (UTE1 and UTE2) by using two different echo times (0.07 ms and 2.46 ms, respectively) was acquired. The input features for the PNN algorithm consist of two patches of MR intensities chosen in both the co-registered UTE1 and UTE2 images. At the end, the PNN generates an image classified into four different classes: brain+soft tissue, air, csf, and bone. CT and MR data were acquired in four subjects, whereby the CT data were used for comparison. For each patient co-classification of the different classified classes and the Dice coefficients (D) were calculated between the MR segmented image and the respective CT image. Results: An overall voxel classification accuracy (compared with CT) of 92% was obtained. Also, the resulting D with regard to the skull and calculated for the four subjects show a mean of 0.83 and a standard deviation of 0.07. Discussion: Our results show that a reliable bone segmentation of MRI images as well as the generation of a reliable attenuation map is possible. Conclusion: The developed algorithms possess several advantages over current methods using UTE sequence such as a quick and an easy optimization for different sequence parameters.