A coil-uniformity correction based on the proton density image was applied prior to segmentation of water-based and fat-based tissues.
Since, in the first subjects examined in this study, the IDEAL sequence was not available on the scanner used in the present study, we based the quantification of fat on the segmentation of water-saturated FGRE images, and validated our approach against IDEAL measurements after this sequence had been installed on our scanner.
The present work concerns a method that, starting from these constraints, defines and optimizes some of the main design drivers for the selection of the segmentation of the Water Coolant Lithium Lead Breeding Blanket.
This sequence enables segmentation of fat and water tissue, lungs and air, which form the basis for creation of the MR-based attenuation map.
Attenuation maps (AM) were generated from MR data (standard Dixon sequence-based segmentation of air, lungs, fat-, and water-like tissue).
The misclassification results are very low, from 0%to2%2%; see Figures 8 and 10; and we detect a minimum of 25% of the object; see Figure 14. Figure 8 Segmentation of a mine in basin without water.
In some cases the segmentation into water- and fat-like tissues fails, leading to fat-water inversion of attenuation values, again potentially resulting in incorrect PET image values (Rausch et al., 2016).
Typically, these involve the usage of two-point 3D Dixon sequences to acquire in-phase and opposed-phase images, allowing the segmentation of the body in fat- and water-like tissues (Martinez-Möller et al., 2009).
It is based on the water reservoir concept for segmentation of characters from stylistic documents without any skew correction.
Representative cross-sectional water-saturated FGRE (used for the segmentation of IMAT), UTE (for IMCT) and fat-saturated FGRE images with the superimposed results of the segmentation process (showing the amount and spatial distribution of IMAT and IMCT within the Triceps Surae complex) are displayed in Figure 2.
In brief, water-containing tissues were identified by interactive segmentation of tissues anterior to the pectoral muscle on the basis of signal intensity in the pre-contrast T1 weighted images.
