Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.

Journal: Abdominal radiology (New York)
Published Date: Jun 16, 2025

Abstract

Computed tomography (CT) is a cornerstone of abdominal imaging, playing a vital role in accurate diagnosis, appropriate treatment planning, and disease monitoring. The evolution of artificial intelligence (AI) in imaging has introduced deep learning-based reconstruction (DLR) techniques that enhance image quality, reduce radiation dose, and improve workflow efficiency. Traditional image reconstruction methods, including filtered back projection (FBP) and iterative reconstruction (IR), have limitations such as high noise levels and artificial image texture. DLR overcomes these challenges by leveraging convolutional neural networks to generate high-fidelity images while preserving anatomical details. Recent advances in vendor-specific and vendor-agnostic DLR algorithms, such as TrueFidelity, AiCE, and Precise Image, have demonstrated significant improvements in contrast-to-noise ratio, lesion detection, and diagnostic confidence across various abdominal organs, including the liver, pancreas, and kidneys. Furthermore, AI extends beyond image reconstruction to applications such as low contrast lesion detection, quantitative imaging, and workflow optimization, augmenting radiologists' efficiency and diagnostic accuracy. However, challenges remain in clinical validation, standardization, and widespread adoption. This review explores the principles, advancements, and future directions of AI-driven CT image reconstruction and its expanding role in abdominal imaging.

Authors

  • Nisanard Pisuchpen
    Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA.
  • Shravya Srinivas Rao
    Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA.
  • Yoshifumi Noda
    Department of Radiology, Gifu University, 1-1 Yanagido, Gifu 501-1194, Japan.
  • Sasiprang Kongboonvijit
    Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 55 Fruit Street, White 270, Boston, MA, 02114-2696, USA.
  • Abbas Rezaei
    Department of Electrical Engineering, Kermanshah University of Technology, Kermanshah, Iran.
  • Avinash Kambadakone
    Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts. Electronic address: akambadakone@mgh.harvard.edu.

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