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Feasibility Study of Synthetic DW-MR Images with Different b Values Compared with Real DW-MR Images: Quantitative Assessment of Three Models Based-Deep Learning Including CycleGAN, Pix2PiX, and DC2Anet

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Abstract

This study aimed to assess the clinical feasibility of employing synthetic diffusion-weighted (DW) images with different b values (50, 400, 800 s/mm2) for the prostate cancer patients with the help of three models, namely CycleGAN, Pix2PiX, and DC2Anet. DW images of 170 prostate cancer patients were used to train and test models. Here, 119 patients were assigned to the training set and 51 patients to the testing set according to a ratio of 7:3. To generate synthetic b value DW images based on CycleGAN, Pix2Pix, and DC2Anet networks, three experiments were performed as follows: generating synthetic DW images with b values of 400 and 800 s/mm2 from acquired DW images with b value of 50 s/mm2; generating synthetic DW images with b value of 800 8 s/mm2 from acquired DW images with b value of 400 s/mm2. Five metrics were used to compare real and synthetic b values. These metrics included Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Pearson’s Correlation Coefficient (PCC), Peak-Signal-to-Noise-Ratio (PSNR), and Structural Similarity Index Measure (SSIM). As well as, ADC values for different b values were computed using the mono-exponentially mode. The whole prostate volume was manually segmented by drawing regions of interest (ROIs) in each slice of the ADC maps. P values less than 0.05 were considered statistically significant. Based on the quantitative evaluation and for all metrics, especially for generating b values of 400 and 800 s/mm2 from a b value of 50 s/mm2, the DC2Anet model was found accurate and it outperformed CycleGAN and Pix2Pix models (P < 0.05). It is necessary to mention that the agreement between synthetic ADC (sADC) and real ADC (rADC) was satisfactory. No significant difference was observed in the one-way ANOVA between sADC and rADC in the whole prostate volume (P > 0.05). Our results showed the significant potential of the three used models for generating images with different b values in the case of prostate cancer. The results demonstrated that the used three models were accurate and robust for generating DW images and also, they outperformed other methods mentioned in the literature review.

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Availability of data and code

The dataset and code used and/or analyzed during the current study are available from the corresponding author on reasonable request only for personal uses, this dataset can be downloaded from a public repository: https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=23691656

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Acknowledgements

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Funding

This work was supported by the Ahvaz Jundishapur University of Medical Sciences under Grant [CRC-0024].

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Authors and Affiliations

  1. Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

    Seyed Masoud Rezaeijo, Nahid Chegeni & Amir Danyaei

  2. School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

    Hossein Entezari Zarch & Hesam Mojtahedi

Authors
  1. Seyed Masoud Rezaeijo
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  2. Hossein Entezari Zarch
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  3. Hesam Mojtahedi
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  4. Nahid Chegeni
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Correspondence to Seyed Masoud Rezaeijo.

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Rezaeijo, S.M., Entezari Zarch, H., Mojtahedi, H. et al. Feasibility Study of Synthetic DW-MR Images with Different b Values Compared with Real DW-MR Images: Quantitative Assessment of Three Models Based-Deep Learning Including CycleGAN, Pix2PiX, and DC2Anet. Appl Magn Reson 53, 1407–1429 (2022). https://doi.org/10.1007/s00723-022-01482-y

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