Literature DB >> 31153565

Effect of a novel denoising technique on image quality and diagnostic accuracy in low-dose CT in patients with suspected appendicitis.

Manuel Kolb1, Corinna Storz1, Jong Hyo Kim2, Jakob Weiss1, Saif Afat1, Konstantin Nikolaou1, Fabian Bamberg1, Ahmed E Othman3.   

Abstract

OBJECTIVES: To determine the effect of a vendor unspecific, DICOM-based denoising technique on image quality and diagnostic performance in low-dose simulated abdominal computed tomography (CT) examinations in patients with suspected appendicitis. METHODS AND MATERIALS: We included 51 patients who underwent contrast-enhanced abdominal CT with Filtered Back Projection due to suspected appendicitis. Realistic Low-Dose simulation generated low-dose datasets at 25% of the original exposition. QuantaStream Denoising, a novel DICOM-based technique denoised the simulated low-Dose datasets. Original (O100), low-dose (LD25) and denoised (DN25) datasets (n = 153) were evaluated regarding subjective image quality (5-point Likert scale; overall quality/image noise/diagnostic confidence), presence/absence of acute appendicitis, free abdominal air and abscess formation and objective image quality (Signal-to-Noise Ratio (SNR) and noise level) by two independent readers.
RESULTS: Subjective image quality was rated highest for O100, followed by DN25 and LD25 with significant differences (p = 0.001). Appendicitis was correctly identified in all datasets (n = 30). Appendicitis specific diagnostic confidence was highest for O100 (p = 0.001), followed by DN25 and LD25 without significant difference. All complications were correctly identified on O100 and DN25. On LD25, diagnostic accuracy decreased for abscess formations (sensitivity:0.714; specificity:1.0) and for free abdominal air (sensitivity:0.750; specificity:0.936). Regarding noise levels DN25 showed non-inferiority to O100. SNRs of O100 and DN25 showed no significant difference (p = 0.06).
CONCLUSION: Our findings indicate that QuantaStream Denoising allows for maintained diagnostic image quality and diagnostic accuracy of low-dose abdominal CT examinations (25% of original exposition) in patients with suspected appendicitis without the need for raw sinogram data.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Appendicitis; Diagnostic imaging; Tomography; X-Ray computed

Mesh:

Year:  2019        PMID: 31153565     DOI: 10.1016/j.ejrad.2019.04.026

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  3 in total

1.  Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study.

Authors:  Minsoo Chun; Jin Hwa Choi; Sihwan Kim; Chulkyun Ahn; Jong Hyo Kim
Journal:  PLoS One       Date:  2022-07-20       Impact factor: 3.752

2.  AI Denoising Significantly Enhances Image Quality and Diagnostic Confidence in Interventional Cone-Beam Computed Tomography.

Authors:  Andreas S Brendlin; Arne Estler; David Plajer; Adrian Lutz; Gerd Grözinger; Malte N Bongers; Ilias Tsiflikas; Saif Afat; Christoph P Artzner
Journal:  Tomography       Date:  2022-04-01

3.  AI Denoising Improves Image Quality and Radiological Workflows in Pediatric Ultra-Low-Dose Thorax Computed Tomography Scans.

Authors:  Andreas S Brendlin; Ulrich Schmid; David Plajer; Maryanna Chaika; Markus Mader; Robin Wrazidlo; Simon Männlin; Jakob Spogis; Arne Estler; Michael Esser; Jürgen Schäfer; Saif Afat; Ilias Tsiflikas
Journal:  Tomography       Date:  2022-06-24
  3 in total

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