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Contrast-enhanced CT (CECT) is routinely used by clinicians to diagnose metabolic diseases such as diabetes. However, a patient’s clinical indication and contrast agent hypersensitivity dictates the specific CT imaging protocol, which can result in missing contrast CT series. For retrospective research studies, it is challenging to re-scan patients with incomplete CT studies. Consequently, this pilot study explores the feasibility of synthesizing missing abdominal CECT series from the available series in a CT study. Given the non-contrast CT and desired CT phase conditioning, we propose to generate an abdominal CECT series using spatial transform control (STControl) in a pre-trained conditional diffusion model. To evaluate the CECT translation quality, a proxy pancreas segmentation task was conducted. Results on the internal Institution-A and external Vin-Dr datasets revealed that STControl improved peak signal-to-noise ratio (PSNR) by 0.42 dB and structural similarity image metric (SSIM) by \(\sim \) 1.28. The proposed technique is generalizable to translate any CT phase into another and may be of particular value when organ morphology rather than focal lesions is critical as in diabetes. Code is available at https://github.com/rsummers11/STControl .

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Synthesis of Abdominal Contrast-Enhanced CT Using Diffusion-Based Spatial Transform Control

  • 会议论文

  • 发表日期：

    2026年

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  • Xinya Wang,
  • Tejas Sudharshan Mathai,
  • Praveen T. S. Balamuralikrishna,
  • Vivek Batheja,
  • Ronald M. Summers

摘要

Contrast-enhanced CT (CECT) is routinely used by clinicians to diagnose metabolic diseases such as diabetes. However, a patient’s clinical indication and contrast agent hypersensitivity dictates the specific CT imaging protocol, which can result in missing contrast CT series. For retrospective research studies, it is challenging to re-scan patients with incomplete CT studies. Consequently, this pilot study explores the feasibility of synthesizing missing abdominal CECT series from the available series in a CT study. Given the non-contrast CT and desired CT phase conditioning, we propose to generate an abdominal CECT series using spatial transform control (STControl) in a pre-trained conditional diffusion model. To evaluate the CECT translation quality, a proxy pancreas segmentation task was conducted. Results on the internal Institution-A and external Vin-Dr datasets revealed that STControl improved peak signal-to-noise ratio (PSNR) by 0.42 dB and structural similarity image metric (SSIM) by \(\sim \) 1.28. The proposed technique is generalizable to translate any CT phase into another and may be of particular value when organ morphology rather than focal lesions is critical as in diabetes. Code is available at https://github.com/rsummers11/STControl .