ArticleJapanese journal of radiology2019
Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images.
Article in Japanese journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- Noise reduction profile: A new method for evaluation of noise reduction techniques in CT.Medical physics · 2022Article
- Sinogram-based deep learning image reconstruction technique in abdominal CT: image quality considerations.European radiology · 2021Article
- Advanced CT techniques for assessing hepatocellular carcinoma.La Radiologia medica · 2021Review
- Low-dose CT urography using deep learning image reconstruction: a prospective study for comparison with conventional CT urography.The British journal of radiology · 2021Article
- Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography.The British journal of radiology · 2021Article
- A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.Diagnostic and interventional radiology (Ankara, Turkey) · 2020Review
- Potential for dose reduction in CT emphysema densitometry with post-scan noise reduction: a phantom study.The British journal of radiology · 2020Article
- Evaluation of Low-dose Computed Tomography Images Reconstructed Using Artificial Intelligence-based Adaptive Filtering for Denoising: A Comparison with Computed Tomography Reconstructed with Iterative Reconstruction Algorithm.Journal of medical physicsArticle
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Abstract
objectiveTo investigate the effect of a deep learning-based denoising algorithm, PixelShine (PS), on the quality of 70 kVp pelvic arterial phase CT images. MATERIALS AND
methodsA retrospective analysis was performed on arterial phase pelvic CT images from 33 patients (body-mass index ≤ 20 kg/m
resultsThe subjective visual image quality scores of groups A and B, respectively, were 3.11 ± 0.30 vs. 3.82 ± 0.57; image noise was 15.79 ± 2.05 Hounsfield units (HU) vs. 11.06 ± 2.22 HU; SNRs of bladder were 0.50 ± 0.23 vs. 0.79 ± 0.39; and CNRs of bladder were 3.72 ± 0.85 vs. 5.14 ± 1.27. Group B showed better subjective image quality, lower image noise, and improved SNR and CNR, compared to group A; these differences were statistically significant (P < 0.05). The noise of group B was approximately 30% lower than that of group A; the SNR and CNR values of group B were improved by approximately 58% and 38%, respectively.
conclusionUsing 70 kVp +ASiR-V, PS can improve the image quality of pelvic arterial phase CT images, significantly reduce the image noise, and improve the SNR and CNR.
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