Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging

Abstract

Attenuation correction (AC) of PET/MRI faces challenges including inter-scan motion, image artifacts such as truncation and distortion, and erroneous transformation of structural voxel-intensities to PET mu-map values. We propose a deep-learning-based method to derive synthetic CT (sCT) images from non-attenuation corrected PET (NAC PET) images for AC on whole-body PET/MRI imaging. A 3D cycle-consistent generative adversarial networks (CycleGAN) framework was employed to synthesize CT images from NAC PET. The method learns a transformation that minimizes the difference between sCT, generated from NAC PET, and true CT. It also learns an inverse transformation such that cycle NAC PET image generated from the sCT is close to true NAC PET image. A self-attention strategy was also utilized to identify the most informative component and mitigate the disturbance of noise. We conducted a retrospective study on a total of 119 sets of whole-body PET/CT, with 80 sets for training and 39 sets for testing and evaluation. The whole-body sCT images generated with proposed method demonstrate great resemblance to true CT images, and show good contrast on soft tissue, lung and bony tissues. The mean absolute error (MAE) of sCT over true CT is less than 110 HU. Using sCT for whole-body PET AC, the mean error of PET quantification is less than 1% and normalized mean square error (NMSE) is less than 1.4%. Average normalized cross correlation on whole body is close to one, and PSNR is larger than 42 dB. We proposed a deep learning-based approach to generate sCT from whole-body NAC PET for PET AC. sCT generated with proposed method shows great similarity to true CT images both qualitatively and quantitatively, and demonstrates great potential for whole-body PET AC in the absence of structural information.

Figure 1.

The schematic flow diagram of the proposed method. The upper part shows the training procedure, and the lower part shows the CT synthesis procedure.

Figure 2.

Comparison of true CT and sCT images on a female received breast implants. From left to right, the four columns are NAC PET, true CT, sCT, and difference images between CT and sCT (unit: HU). The top two rows show two transverse slices, and the last row shows one coronal slice.

Figure 3.

Qualitative results on Patient 1. Images are (a) true CT, (b) sCT, (c) AC PET and (d) sCT AC PET. The dashed line on (c) indicates the position of a sagittal cranial-caudal profile displayed in figure 4.

Figure 4.

Quantitative results on Patient 1. Images are PET image profiles (left) and joint histograms (right) of AC PET and sCT AC PET. The red line in the right figure is the line of identity.

Figure 5.

Qualitative results on Patient 2, whom is a female received breast implants. Images are (a) true CT, (b) sCT, (c) AC PET and (d) sCT AC PET. The dashed line on (c) indicates the position of a sagittal cranial-caudal profile displayed in figure 6.

Figure 6.

Quantitative results on Patient 2. Images PET image profiles (left) and joint histograms (right) of AC PET and sCT AC PET.