Bias atlases for segmentation-based PET attenuation correction using PET-CT and MR

Abstract

This study was to obtain voxel-wise PET accuracy and precision using tissue-segmentation for attenuation correction. We applied multiple thresholds to the CTs of 23 patients to classify tissues. For six of the 23 patients, MR images were also acquired. The MR fat/in-phase ratio images were used for fat segmentation. Segmented tissue classes were used to create attenuation maps, which were used for attenuation correction in PET reconstruction. PET bias images were then computed using the PET reconstructed with the original CT as the reference. We registered the CTs for all the patients and transformed the corresponding bias images accordingly. We then obtained the mean and standard deviation bias atlas using all the registered bias images. Our CT-based study shows that four-class segmentation (air, lungs, fat, other tissues), which is available on most PET-MR scanners, yields 15.1%, 4.1%, 6.6%, and 12.9% RMSE bias in lungs, fat, non-fat soft-tissues, and bones, respectively. An accurate fat identification is achievable using fat/in-phase MR images. Furthermore, we have found that three-class segmentation (air, lungs, other tissues) yields less than 5% standard deviation of bias within the heart, liver, and kidneys. This implies that three-class segmentation can be sufficient to achieve small variation of bias for imaging these three organs. Finally, we have found that inter- and intra-patient lung density variations contribute almost equally to the overall standard deviation of bias within the lungs.

Keywords: PET-MR; attenuation correction.

Fig. 1

Histograms of CT intensities and attenuation coefficients at 511 keV for all the 23 patient CT scans. (A) Histogram of CT intensities. (B) Histogram of attenuation coefficients at 511 kev.

Fig. 2

Histograms of voxel values in the MR fat images and the MR fat/in-phase ratio images for all the 6 patient MR scans. (A) Histogram of voxel intensity values in the MR fat images. (B) Histogram of voxel values in the MR fat/in-phase ratio images.

Fig. 3

Flow chart for the CT-based study. The notations ref and i represent the reference and ith patients, respectively.

Fig. 4

Histograms of voxel-wise bias for different tissue classes for the CT-based study, which includes all the 23 patients.

Fig. 5

Histograms of voxel-wise bias for the heart, liver, and kidneys for the CT-based study, which includes all the 23 patients.

Fig. 6

One coronal slice for the CT-based study. (A) The CTs for the reference patient. (B) The bias images for the reference patient.

Fig. 7

One coronal slice for the MR-based study. (A) The in-phase and fat MR images for the reference patient. (B) The bias images for the reference patient. (C) The mean bias calculated using all the 6 patient data sets.

Fig. 8

Bias profiles. The profile lines are shown in Figures 6 (B) and 7 (B).

Fig. 9

One coronal atlas slice in the back of the body for the CT-based study. (A) The mean SUV bias atlas calculated using all the 23 patient data sets. (B) The standard deviation atlas of SUV bias. (C) The RMSE SUV bias atlas.

Fig. 10

One coronal atlas slice in the front of the body for the CT-based study. (A) The mean SUV bias atlas calculated using all the 23 patient data sets. (B) The standard deviation atlas of SUV bias. (C) The RMSE atlas of SUV bias.

Fig. 11

The standard deviation atlas of SUV bias and profiles in three different views through the heart for CT-based 3C.

Fig. 12

The standard deviation atlas of SUV bias and profiles in three different views through the liver for CT-based 3C.

Fig. 13

The standard deviation atlas of SUV bias and profiles in three different views through the kidneys for CT-based 3C. The black arrows point to the kidneys.