Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR).

Journal: Abdominal radiology (New York)

PMID: 38470506

Abstract

PURPOSE: Urolithiasis is a chronic condition that leads to repeated CT scans throughout the patient's life. The goal was to assess the diagnostic performance and image quality of submillisievert abdominopelvic computed tomography (CT) using deep learning-based image reconstruction (DLIR) in urolithiasis.

Authors

Sarah Prod'homme

Department of Radiology, Amiens University Hospital, 1 Rond-Point du Professeur Christian Cabrol, 80054, Amiens Cedex 01, France.
Roger Bouzerar

Medical Image Processing Unit, Amiens University Hospital, Amiens, France.
Thomas Forzini

Department of Urology and Transplantation, Amiens University Hospital, Amiens, France.
Aurélien Delabie

Department of Radiology, Amiens University Hospital, Amiens Cedex, France.
Cédric Renard

Department of Radiology, Amiens University Hospital, Amiens Cedex, France.

Keywords

Adult Aged Aged, 80 and over Algorithms Deep Learning Female Humans Male Middle Aged Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Radiography, Abdominal Retrospective Studies Sensitivity and Specificity Tomography, X-Ray Computed Urinary Calculi Urolithiasis

External Resources

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Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR).

Abstract

Authors

Keywords

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