Comparison of threshold-based and watershed-based segmentation for the truncation compensation of PET/MR images

Recently introduced combined PET/MR scanners need to handle the specific problem that a limited MR field of view sometimes truncates arm or body contours, which prevents an accurate calculation of PET attenuation correction maps. Such maps of attenuation coefficients over body structures are required for a quantitatively correct PET image reconstruction. This paper addresses this problem by presenting a method that segments a preliminary reconstruction type of PET images, time of flight non-attenuation corrected (ToF-NAC) images, and outlining a processing pipeline that compensates the arm or body truncation with this segmentation. The impact of this truncation compensation is demonstrated together with a comparison of two segmentation methods, simple gray value threshold segmentation and a watershed algorithm on a gradient image. Our results indicate that with truncation compensation a clinically tolerable quantitative SUV error is robustly achievable.