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<p>Medical imaging plays a pivotal role in modern healthcare, with Magnetic Resonance Imaging (MRI) standing out for its exceptional resolution and non-ionizing nature. Diffusion-Weighted Imaging (DWI) has further enhanced the diagnostic capability of MRI by visualizing tissue microstructure, making it invaluable for early disease detection, but it cannot assess perfusion. IntraVoxel Incoherent Motion (IVIM) MRI addresses this gap by simultaneously quantifying both diffusion and perfusion, offering deeper insights into tissue characteristics. However, its clinical adoption remains limited due to challenges such as the lack of standardized algorithms, noise-related image degradation, and variability in parameter estimation. This review explores the evolution of IVIM MRI, its physiological models for parameter estimation, the impact of noise and denoising techniques on image quality, and different fitting approaches to improve accuracy and reliability. The exploration of these areas in this work aims to enhance the reliability of IVIM and facilitate its integration into clinical practice, ultimately improving diagnostic accuracy and patient outcomes across diverse medical conditions.</p>

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Transforming Medical Imaging with IVIM MR Imaging: A Comprehensive Review of Advances and Challenges in Perfusion and Diffusion Analysis

  • 期刊论文

  • 发表日期：

    2025年4月26日

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  • Jini Raju,
  • A. Shyna,
  • Ansamma John,
  • Manu J. Pillai

摘要

Medical imaging plays a pivotal role in modern healthcare, with Magnetic Resonance Imaging (MRI) standing out for its exceptional resolution and non-ionizing nature. Diffusion-Weighted Imaging (DWI) has further enhanced the diagnostic capability of MRI by visualizing tissue microstructure, making it invaluable for early disease detection, but it cannot assess perfusion. IntraVoxel Incoherent Motion (IVIM) MRI addresses this gap by simultaneously quantifying both diffusion and perfusion, offering deeper insights into tissue characteristics. However, its clinical adoption remains limited due to challenges such as the lack of standardized algorithms, noise-related image degradation, and variability in parameter estimation. This review explores the evolution of IVIM MRI, its physiological models for parameter estimation, the impact of noise and denoising techniques on image quality, and different fitting approaches to improve accuracy and reliability. The exploration of these areas in this work aims to enhance the reliability of IVIM and facilitate its integration into clinical practice, ultimately improving diagnostic accuracy and patient outcomes across diverse medical conditions.