Pisuchpen Nisanard, Srinivas Rao Shravya, Noda Yoshifumi, Kongboonvijit Sasiprang, Rezaei Abbas, Kambadakone Avinash
Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, Boston, 02114, MA, United States.
Department of Radiology, Faculty of Medicine, King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Chulalongkorn University, 1873, Rama 4 Road, 10330, Bangkok, Thailand.
Abdom Radiol (NY). 2025 Jun 16. doi: 10.1007/s00261-025-05031-6.
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.
计算机断层扫描（CT）是腹部成像的基石，在准确诊断、合理治疗规划和疾病监测中发挥着至关重要的作用。成像领域人工智能（AI）的发展引入了基于深度学习的重建（DLR）技术，这些技术可提高图像质量、降低辐射剂量并提高工作流程效率。传统的图像重建方法，包括滤波反投影（FBP）和迭代重建（IR），存在诸如噪声水平高和人工图像纹理等局限性。DLR通过利用卷积神经网络生成高保真图像同时保留解剖细节来克服这些挑战。特定厂商和厂商无关的DLR算法（如TrueFidelity、AiCE和Precise Image）的最新进展已证明，在包括肝脏、胰腺和肾脏在内的各种腹部器官的对比度噪声比、病变检测和诊断置信度方面有显著改善。此外，AI不仅限于图像重建，还扩展到低对比度病变检测、定量成像和工作流程优化等应用，提高了放射科医生的效率和诊断准确性。然而，在临床验证、标准化和广泛采用方面仍存在挑战。本综述探讨了AI驱动的CT图像重建的原理、进展和未来方向及其在腹部成像中不断扩大的作用。
