A hybrid-segmentation atlas method to construct the attenuation correction factor for human pelvic PET/MRI
Hiroshi Kawaguchi1,2, Tkayuki Obata2,3, Hiromi Sano2, Eiji Yoshida2, Mikio Suga4, Yoko Ikoma2, Yukari Tanikawa1, and Taiga Yamaya2

1Human Informatics Research Institute, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Japan, 2Molecular Imaging Center, National Institute of Radiological Sciences, Chiba, Japan, 3Research Center for Charged Ion Therapy, National Institute of Radiological Sciences, Chiba, Japan, 4Center for Frontier Medical Engineering, Chiba University, Chiba, Japan


The current attenuation correction method for human pelvic PET/MRI contains several problems such that the attenuation due to bone is not considered and a specific MR imaging, intended for attenuation correction only, is needed. In this study, we proposed a method to generate the distribution of attenuation correction factors with considering the bone attenuation using diagnostic T1-weighted MRI for pelvic PET/MRI scanning. The proposed method is the hybrid of the tissue segmentation based on Gaussian mixture model and the non-liner registration of tissue probability to a subject image. The simulation results showed that attenuation correction using the proposed hybrid method reduced the error on PET image than the conventional method.


The ability to make quantitative measurements of radioactivity is essential for human PET. Although several MR-based attenuation correction (AC) methods have been reported to estimate the spatial distribution of the attenuation coefficient (μ-map) for PET/MRI scanners, a few methods were applicable for the body trunk1. Especially, the conventional AC for pelvic PET/MRI contains several problems such that the attenuation due to bone is not considered and a specific MR imaging, intended for AC only, is needed. In this study, we proposed a method to generate theμ-map with considering the bone attenuation using the diagnostic T1-weighted MRI for the pelvic PET/MRI scan.


Fig. 1 is a schematic of PET/MRI AC with the proposed hybrid-segmentation atlas method (HSAM). The algorithm is based on two probability maps: the intensity-based probability map, P, constructed by the soft segmentation of subject’s T1-weighted image (T1WI) and the tissue probability atlas, Q. As shown in Fig .2, the pixel intensity roughly divided into 3 groups in the pelvic T1WI. The first step in making P is to determine the threshold value to distinguish very low intensity pixels from the others. The smallest local minimum on the intensity histogram was used as the threshold value. Next, the histogram of the remained voxels was fitted to three-component Gaussian mixture model and intensity-based probability maps were produced. The first step in making Q is to construct the template of the T1WI and the tissue probability map from the database of images. Next, the affine transformation was computed to register the T1WI template to individual T1WI. In addition, the demons method was applied to improve the accuracy of the registration2. Next, these transformations were applied to each Q. The complete tissue probability maps, Pair, Pbone and Psoft, were culculated from P and Q’ using equations shown in Fig. 1. Finally, the most suitable µ-value was assigned to each voxel following complete tissue probability maps. The MR-based AC methods were evaluated using MRI scans from 20 prostate cancer patients. All 3D T1WI were acquired with the Achieva 1.5T scanner (Philips Healthcare, Best, Netherlands) using the multi-slice spin echo sequence (orientation: transaxial, TR: 675 ms, TE: 10 ms, matrix size: 512x512, FOV: 350 mm, NEX: 2, slice thickness: 4 mm, no gap). The proposed method was validated by leave-one-out method, i.e. templates were constructed from 19 subjects except the subject for evaluation. In addition to HSAM, µ-maps were also generated by the conventional segmentation-based method (SBM) and atlas-based method (ABM) as described in Fig. 1. The digital phantom to simulate the PET scan was constructed by the manual segmentation of the subject’s T1WI. Jaccard index, JI=(A^B)/(AvB), was calculated between the µ-map of digital phantom (A) and each MR-based µ-map (B). The PET emission images were reconstructed with two-dimensional filtered back-projection after AC. The error (E) in the PET image was calculated with the following equation: E = ∑N|I – Iref|/N, where I is the pixel intensity in the ROI on the reconstructed image being evaluated, Iref is that on the reference image and N is number of pixels in the ROI. The reference image was taken to be the PET image corrected by µ-map of the digital phantom. The statistical difference between MR-based AC methods was evaluated by the one-way repeated measure ANOVA with Bonferroni correction (α=0.05).

Results and Discussion

Figure 3 shows the MR-based µ-maps and reconstructed PET images of a typical subject. Jaccard indices and error in PET image were summarized in Table 1. While SBM and ABM could not distinguish the bone and the intestinal gas regions in the µ-map, respectively, HSAM succeeded to segment most of these regions. Jaccard indices for the HSAM were statistically better than that for ABM in all regions and SBM in the soft tissue and the bone. In the PET image, HSAM and ABM succeeded to reconstruct the radioactivity of the prostate region, while the SBM produced underestimation due to the bone attenuation. The false positives are found on the boundary of the two different regions, which will be caused by the incorrect segmentation and the misregistration of the template.


The fully automated µ-map generation method was proposed for the pelvic PET/MRI scan using the diagnostic T1WI. The proposed HSAM provided more accurate segmentation results than conventional SBM and ABM. The AC using the µ-map from HSAM and ABM provided more accurate PET image than that by SBM, which indicates bone attenuation should be considered for the AC of the pelvic PET.


This work was partially supported by the KAKENHI grant for HK(15K15216)


1. Keereman V, Mollet P, Berker Y, et al. Challenges and current methods for attenuation correction in PET/MR. MAGMA, 2013:26(1):81-98.

2. Vercauteren T, Pennec X, Perchant A, et al. Diffeomorphic demons: efficient non-parametric image registration. Neuroimage. 2009:45(1 Suppl):S61-72.


Fig. 1. Schematic of attenuation correction with the conventional methods, SBM and ABM, and the proposed HSAM.

Fig. 2.Typical pelvic T1-weighted image (a) and the intensity histogram (b).

Fig. 3. MR-based µ-maps and PET images of a typical subject

Table 1. Jaccard index (JI) and error in PET image of each MR-based µ-map

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)