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Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients.

Publication ,  Journal Article
Tie, X; Lam, S-K; Zhang, Y; Lee, K-H; Au, K-H; Cai, J
Published in: Med Phys
April 2020

PURPOSE: To develop and evaluate a novel method for pseudo-CT generation from multi-parametric MR images using multi-channel multi-path generative adversarial network (MCMP-GAN). METHODS: Pre- and post-contrast T1-weighted (T1-w), T2-weighted (T2-w) MRI, and treatment planning CT images of 32 nasopharyngeal carcinoma (NPC) patients were employed to train a pixel-to-pixel MCMP-GAN. The network was developed based on a 5-level Residual U-Net (ResU-Net) with the channel-based independent feature extraction network to generate pseudo-CT images from multi-parametric MR images. The discriminator with five convolutional layers was added to distinguish between the real CT and pseudo-CT images, improving the nonlinearity and prediction accuracy of the model. Eightfold cross validation was implemented to validate the proposed MCMP-GAN. The pseudo-CT images were evaluated against the corresponding planning CT images based on mean absolute error (MAE), peak signal-to-noise ratio (PSNR), Dice similarity coefficient (DSC), and Structural similarity index (SSIM). Similar comparisons were also performed against the multi-channel single-path GAN (MCSP-GAN), the single-channel single-path GAN (SCSP-GAN). RESULTS: It took approximately 20 h to train the MCMP-GAN model on a Quadro P6000, and less than 10 s to generate all pseudo-CT images for the subjects in the test set. The average head MAE between pseudo-CT and planning CT was 75.7 ± 14.6 Hounsfield Units (HU) for MCMP-GAN, significantly (P-values < 0.05) lower than that for MCSP-GAN (79.2 ± 13.0 HU) and SCSP-GAN (85.8 ± 14.3 HU). For bone only, the MCMP-GAN yielded a smaller mean MAE (194.6 ± 38.9 HU) than MCSP-GAN (203.7 ± 33.1 HU), SCSP-GAN (227.0 ± 36.7 HU). The average PSNR of MCMP-GAN (29.1 ± 1.6) was found to be higher than that of MCSP-GAN (28.8 ± 1.2) and SCSP-GAN (28.2 ± 1.3). In terms of metrics for image similarity, MCMP-GAN achieved the highest SSIM (0.92 ± 0.02) but did not show significantly improved bone DSC results in comparison with MCSP-GAN. CONCLUSIONS: We developed a novel multi-channel GAN approach for generating pseudo-CT from multi-parametric MR images. Our preliminary results in NPC patients showed that the MCMP-GAN method performed apparently superior to the U-Net-GAN and SCSP-GAN, and slightly better than MCSP-GAN.

Duke Scholars

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

April 2020

Volume

47

Issue

4

Start / End Page

1750 / 1762

Location

United States

Related Subject Headings

  • Tumor Burden
  • Tomography, X-Ray Computed
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Nasopharyngeal Carcinoma
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Tie, X., Lam, S.-K., Zhang, Y., Lee, K.-H., Au, K.-H., & Cai, J. (2020). Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients. Med Phys, 47(4), 1750–1762. https://doi.org/10.1002/mp.14062
Tie, Xin, Sai-Kit Lam, Yong Zhang, Kar-Ho Lee, Kwok-Hung Au, and Jing Cai. “Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients.Med Phys 47, no. 4 (April 2020): 1750–62. https://doi.org/10.1002/mp.14062.

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

April 2020

Volume

47

Issue

4

Start / End Page

1750 / 1762

Location

United States

Related Subject Headings

  • Tumor Burden
  • Tomography, X-Ray Computed
  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Nasopharyngeal Carcinoma
  • Magnetic Resonance Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering