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<p>Dual-energy subtraction (DES) radiography generates bone-suppressed (BS) and bone-enhanced (BE) images, enhancing soft tissue and bone visualization; however, it requires specialized dual-energy imaging systems. A deep generative translation model has been established to transform high-energy (HE) images into low-energy (LE) images (HE2LE), enabling virtual DES. However, clinical standard (STD) images cannot yield virtual DES via HE2LE translation alone. Therefore, we developed a U-Net-based STD into HE translation model using a discriminator in the pix2pix framework to generate virtual HE images and compute virtual DES from STD images. The training process involved a dataset of 600 triplet chest radiographs (STD, HE, and LE) and was executed over 2000 epochs using a sixfold cross-validation approach. The HE images generated by the STD2HE model were quantitatively evaluated and revealed a peak signal-to-noise ratio (PSNR) of 34.5, structural similarity index (SSIM) of 0.979, and deep image structure and texture similarity (DISTS) of 0.0257, compared with the groundtruth HE images. The virtual LE images obtained from virtual HE inputs were compared with the groundtruth LE images obtained by processing the corresponding groundtruth HE images using the same model. These LE images demonstrated a PSNR of 35.1, SSIM of 0.963, and DISTS of 0.0455. The virtual DES images exhibited a Fréchet inception distance of 67.6 and 73.6 for the BS and BE images, indicating high structural and perceptual fidelity, respectively. Integrating the proposed framework with the preceding HE2LE model can potentially enhance diagnostic utility in radiological analysis, emphasizing the need for specialized dual-energy imaging hardware.</p>

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Deep Generative Translation of Standard Images into Virtual High-Energy Images for Facilitating Dual-Energy Chest Radiography

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Yasuyuki Ueda,
  • Riko Shimazaki,
  • Masashi Seki,
  • Takayuki Ishida

摘要

Dual-energy subtraction (DES) radiography generates bone-suppressed (BS) and bone-enhanced (BE) images, enhancing soft tissue and bone visualization; however, it requires specialized dual-energy imaging systems. A deep generative translation model has been established to transform high-energy (HE) images into low-energy (LE) images (HE2LE), enabling virtual DES. However, clinical standard (STD) images cannot yield virtual DES via HE2LE translation alone. Therefore, we developed a U-Net-based STD into HE translation model using a discriminator in the pix2pix framework to generate virtual HE images and compute virtual DES from STD images. The training process involved a dataset of 600 triplet chest radiographs (STD, HE, and LE) and was executed over 2000 epochs using a sixfold cross-validation approach. The HE images generated by the STD2HE model were quantitatively evaluated and revealed a peak signal-to-noise ratio (PSNR) of 34.5, structural similarity index (SSIM) of 0.979, and deep image structure and texture similarity (DISTS) of 0.0257, compared with the groundtruth HE images. The virtual LE images obtained from virtual HE inputs were compared with the groundtruth LE images obtained by processing the corresponding groundtruth HE images using the same model. These LE images demonstrated a PSNR of 35.1, SSIM of 0.963, and DISTS of 0.0455. The virtual DES images exhibited a Fréchet inception distance of 67.6 and 73.6 for the BS and BE images, indicating high structural and perceptual fidelity, respectively. Integrating the proposed framework with the preceding HE2LE model can potentially enhance diagnostic utility in radiological analysis, emphasizing the need for specialized dual-energy imaging hardware.