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The adoption of contrast agents in medical imaging is essential for accurate diagnosis. While highly effective and characterized by an excellent safety profile, the use of contrast agents has its limitation, including rare risk of allergic reactions, potential environmental impact and economic burdens on patients and healthcare systems. This work addresses the contrast agent reduction (CAR) problem, aiming to minimize the administered dosage while preserving image quality. Unlike existing deep learning methods that simulate high-dose images from low-dose inputs via end-to-end models, we propose a learned inverse problem (LIP) approach. By learning an operator that maps high-dose to low-dose images, we reformulate CAR as an inverse problem, solved through regularized optimization to enhance data consistency. Numerical experiments on pre-clinical images demonstrate improved accuracy compared to traditional methods.

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LIP-CAR: A Learned Inverse Problem Approach for Medical Imaging with Contrast Agent Reduction

  • 会议论文

  • 发表日期：

    2026年

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  • Davide Evangelista,
  • Elena Morotti,
  • Sonia Colombo Serra,
  • Pengpeng Luo,
  • Giovanni Valbusa,
  • Davide Bianchi

摘要

The adoption of contrast agents in medical imaging is essential for accurate diagnosis. While highly effective and characterized by an excellent safety profile, the use of contrast agents has its limitation, including rare risk of allergic reactions, potential environmental impact and economic burdens on patients and healthcare systems. This work addresses the contrast agent reduction (CAR) problem, aiming to minimize the administered dosage while preserving image quality. Unlike existing deep learning methods that simulate high-dose images from low-dose inputs via end-to-end models, we propose a learned inverse problem (LIP) approach. By learning an operator that maps high-dose to low-dose images, we reformulate CAR as an inverse problem, solved through regularized optimization to enhance data consistency. Numerical experiments on pre-clinical images demonstrate improved accuracy compared to traditional methods.