Enhancing Cone-Beam CT Image Quality in TIPSS Procedures Using AI Denoising Article Swipe
Reza Dehdab
,
Andreas Brendlin
,
Gerd Grözinger
,
Haidara Almansour
,
Jan M. Brendel
,
Sebastian Gassenmaier
,
Patrick Ghibes
,
Sebastian Werner
,
Konstantin Nikolaou
,
Saif Afat
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14171989
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14171989
Purpose: This study evaluates a deep learning-based denoising algorithm to improve the trade-off between radiation dose, image noise, and motion artifacts in TIPSS procedures, aiming for shorter acquisition times and reduced radiation with maintained diagnostic quality. Methods: In this retrospective study, TIPSS patients were divided based on CBCT acquisition times of 6 s and 3 s. Traditional weighted filtered back projection (Original) and an AI denoising algorithm (AID) were used for image reconstructions. Objective assessments of image quality included contrast, noise levels, and contrast-to-noise ratios (CNRs) through place-consistent region-of-interest (ROI) measurements across various critical areas pertinent to the TIPSS procedure. Subjective assessments were conducted by two blinded radiologists who evaluated the overall image quality, sharpness, contrast, and motion artifacts for each dataset combination. Statistical significance was determined using a mixed-effects model (p ≤ 0.05). Results: From an initial cohort of 60 TIPSS patients, 44 were selected and paired. The mean dose-area product (DAP) for the 6 s acquisitions was 5138.50 ± 1325.57 µGy·m2, significantly higher than the 2514.06 ± 691.59 µGym2 obtained for the 3 s series. CNR was highest in the 6 s-AID series (p < 0.05). Both denoised and original series showed consistent contrast for 6 s and 3 s acquisitions, with no significant noise differences between the 6 s Original and 3 s AID images (p > 0.9). Subjective assessments indicated superior quality in 6 s-AID images, with no significant overall quality difference between the 6 s-Original and 3 s-AID series (p > 0.9). Conclusions: The AI denoising algorithm enhances CBCT image quality in TIPSS procedures, allowing for shorter scans that reduce radiation exposure and minimize motion artifacts.
Related Topics
Concepts
Image quality
Contrast (vision)
Artificial intelligence
Noise (video)
Noise reduction
Nuclear medicine
Image noise
Medicine
Cone beam computed tomography
Mathematics
Projection (relational algebra)
Radon transform
Radiation dose
Computer vision
Computer science
Image (mathematics)
Radiology
Algorithm
Computed tomography
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics14171989
- OA Status
- gold
- Cited By
- 2
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402377258
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402377258Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/diagnostics14171989Digital Object Identifier
- Title
-
Enhancing Cone-Beam CT Image Quality in TIPSS Procedures Using AI DenoisingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-09Full publication date if available
- Authors
-
Reza Dehdab, Andreas Brendlin, Gerd Grözinger, Haidara Almansour, Jan M. Brendel, Sebastian Gassenmaier, Patrick Ghibes, Sebastian Werner, Konstantin Nikolaou, Saif AfatList of authors in order
- Landing page
-
https://doi.org/10.3390/diagnostics14171989Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/diagnostics14171989Direct OA link when available
- Concepts
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Image quality, Contrast (vision), Artificial intelligence, Noise (video), Noise reduction, Nuclear medicine, Image noise, Medicine, Cone beam computed tomography, Mathematics, Projection (relational algebra), Radon transform, Radiation dose, Computer vision, Computer science, Image (mathematics), Radiology, Algorithm, Computed tomographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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