Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.
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...
| Publicado en: | Abdominal Radiology Vol. 51; no. 1; pp. 424 - 446 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
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Springer Nature
Jan2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191138648&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191138648 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jan2026 vid: 51 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191138648 185962009 10.1007/s00261-025-05031-6 191138648 ppf: 424 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond. aug: au: Pisuchpen, Nisanard Srinivas Rao, Shravya Noda, Yoshifumi Kongboonvijit, Sasiprang Rezaei, Abbas Kambadakone, Avinash affil: https://ror.org/03vek6s52 Abdominal Radiology Division, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, White 270, 55 Fruit Street, 02114, Boston, MA, United States sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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