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

  • Kidneys, Ureters, Bladder, Retroperitoneum
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

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.

Methods

57 patients with suspected urolithiasis underwent both non-contrast low-dose (LD) and ULD abdominopelvic CT. Raw image data of ULD CT were reconstructed using hybrid iterative reconstruction (ASIR-V 70%) and high-strength-level DLIR (DLIR-H). The performance of ULD CT for the detection of urinary stones was assessed by two readers and compared with LD CT with ASIR-V 70% as a reference standard. Image quality was assessed subjectively and objectively.

Results

266 stones were detected in 38 patients. Mean effective dose was 0.59 mSv for ULD CT and 1.96 mSv for LD CT. For diagnostic performance, sensitivity and specificity were 89% and 94%, respectively, for ULDCT with DLIR-H. There was an almost perfect intra-observer concordance on ULD CT with DLIR-H versus LDCT with ASIR-V 70% (ICC = 0.90 and 0.90 for the two readers). Image noise was significantly lower and signal-to-noise ratio significantly higher with DLIR-H compared to ASIR-V 70%. Subjective image quality was also significantly better with ULDCT with DLIR-H.

Conclusion

ULD CT with Deep Learning Image Reconstruction maintains a good diagnostic performance in urolithiasis, with better image quality than hybrid iterative reconstruction and a significant radiation dose reduction.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

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

    Sarah Prod’homme, Aurélien Delabie & Cédric Renard

  2. Biophysics and Image Processing Unit, Amiens University Hospital, Amiens, France

    Roger Bouzerar

  3. Department of Urology and Transplantation, Amiens University Hospital, Amiens, France

    Thomas Forzini

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  1. Sarah Prod’homme
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  2. Roger Bouzerar
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  4. Aurélien Delabie
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Correspondence to Cédric Renard.

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Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Ethical approval

The study was approved by the Committee for the Protection of Individuals CPP EST I (Dijon, France) on June 13, 2020 (ID-RCB: 2020-A01436-33).

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Prod’homme, S., Bouzerar, R., Forzini, T. et al. Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR). Abdom Radiol (2024). https://doi.org/10.1007/s00261-024-04223-w

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  • DOI: https://doi.org/10.1007/s00261-024-04223-w

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