Influence of high-performance image-to-image translation networks on clinical visual assessment and outcome prediction: utilizing ultrasound to MRI translation in prostate cancer.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jul 19, 2025

Abstract

PURPOSE: Image-to-image (I2I) translation networks have emerged as promising tools for generating synthetic medical images; however, their clinical reliability and ability to preserve diagnostically relevant features remain underexplored. This study evaluates the performance of state-of-the-art 2D/3D I2I networks for converting ultrasound (US) images to synthetic MRI in prostate cancer (PCa) imaging. The novelty lies in combining radiomics, expert clinical evaluation, and classification performance to comprehensively benchmark these models for potential integration into real-world diagnostic workflows.

Authors

  • Mohammad R Salmanpour
    Department of Energy Engineering and Physics, Amirkabir University of Technology, Tehran, Iran.
  • Amin Mousavi
    Department of Computer, Abhar Branch, Islamic Azad University, Abhar, Iran.
  • Yixi Xu
    AI for Health, Microsoft, Redmond, WA, USA.
  • William B Weeks
    AI for Good Lab, Microsoft Corporation, Redmond, WA, United States.
  • Ilker Hacihaliloglu
    Department of Biomedical Engineering, Rutgers University, Piscataway, NJ, 08854, USA.

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