Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis.

Journal: Acta radiologica (Stockholm, Sweden : 1987)

PMID: 34365803

Abstract

BACKGROUND: Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction.

Authors

Aurélien Delabie

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

Medical Image Processing Unit, Amiens University Hospital, Amiens, France.
Raphaël Pichois

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

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

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

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

Keywords

Algorithms Deep Learning Humans Image Processing, Computer-Assisted Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Retrospective Studies Tomography, X-Ray Computed Urinary Calculi Urolithiasis

External Resources

View on PubMed Access via DOI PubMed (34365803)

Diagnostic performance and image quality of deep learning image reconstruction (DLIR) on unenhanced low-dose abdominal CT for urolithiasis.

Abstract

Authors

Keywords

External Resources

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