ArticleKorean journal of radiology2021
Deep Learning Algorithm for Simultaneous Noise Reduction and Edge Sharpening in Low-Dose CT Images: A Pilot Study Using Lumbar Spine CT.
Article in Korean journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 37 citations in OpenAlex.
- Artificial Intelligence in Spine Neuroimaging: Diagnostic and Prognostic Utility of Novel Biomarkers in Lower Back Pain.Journal of clinical medicine · 2026Review
- Additional Dose Reduction Potential of Vendor-Agnostic Deep Learning Model: A Phantom Study.Journal of the Korean Society of Radiology · 2026Article
- Diagnostic performance of lumbar spine CT using deep learning denoising to evaluate disc herniation and spinal stenosis.European radiology · 2025Article
- Investigations on whether image-sharpening algorithm can improve the determination of severity of diabetic retinopathy in ultra-widefield fundus photographs.Scientific reports · 2025Observational
- Deep Learning-Based Super-Resolution Reconstruction on Undersampled Brain Diffusion-Weighted MRI for Infarction Stroke: A Comparison to Conventional Iterative Reconstruction.AJNR. American journal of neuroradiology · 2025Article
- Automated Opportunistic Osteoporosis Screening Using Low-Dose Chest CT among Individuals Undergoing Lung Cancer Screening in a Korean Population.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances.Neurospine · 2024Article
- Assessment of image quality and impact of deep learning-based software in non-contrast head CT scans.Scientific reports · 2024Article
- Survey on Value Elements Provided by Artificial Intelligence and Their Eligibility for Insurance Coverage With an Emphasis on Patient-Centered Outcomes.Korean journal of radiology · 2024Article
- Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen.Diagnostics (Basel, Switzerland) · 2024Article
- Image Quality and Diagnostic Performance of Low-Dose Liver CT with Deep Learning Reconstruction versus Standard-Dose CT.Radiology. Artificial intelligence · 2024Article
- CT image denoising methods for image quality improvement and radiation dose reduction.Journal of applied clinical medical physics · 2024Review
- Deep learning-based reconstruction for acceleration of lumbar spine MRI: a prospective comparison with standard MRI.European radiology · 2023Article
- Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study.European radiology · 2023Article
- Reporting Quality of Research Studies on AI Applications in Medical Images According to the CLAIM Guidelines in a Radiology Journal With a Strong Prominence in Asia.Korean journal of radiology · 2023Review
- 4DCT is long overdue for improvement.Journal of applied clinical medical physics · 2023Article
- Impact of radiation dose reduction and iterative image reconstruction on CT-guided spine biopsies.Scientific reports · 2023Article
- Ultra-low-dose CT is feasible for torsion measurement of the lower limb in patients with metal implants.The British journal of radiology · 2023Article
- Effects of image-sharpening algorithm on surgical field visibility during 3D heads-up surgery for vitreoretinal diseases.Scientific reports · 2023Article
- Influence of a Deep Learning Noise Reduction on the CT Values, Image Noise and Characterization of Kidney and Ureter Stones.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
objectiveThe purpose of this study was to assess whether a deep learning (DL) algorithm could enable simultaneous noise reduction and edge sharpening in low-dose lumbar spine CT. MATERIALS AND
methodsThis retrospective study included 52 patients (26 male and 26 female; median age, 60.5 years) who had undergone CT-guided lumbar bone biopsy between October 2015 and April 2020. Initial 100-mAs survey images and 50-mAs intraprocedural images were reconstructed by filtered back projection. Denoising was performed using a vendor-agnostic DL model (ClariCT.AI™, ClariPI) for the 50-mAS images, and the 50-mAs, denoised 50-mAs, and 100-mAs CT images were compared. Noise, signal-to-noise ratio (SNR), and edge rise distance (ERD) for image sharpness were measured. The data were summarized as the mean ± standard deviation for these parameters. Two musculoskeletal radiologists assessed the visibility of the normal anatomical structures.
resultsNoise was lower in the denoised 50-mAs images (36.38 ± 7.03 Hounsfield unit [HU]) than the 50-mAs (93.33 ± 25.36 HU) and 100-mAs (63.33 ± 16.09 HU) images (
conclusionDL-based reconstruction may enable simultaneous noise reduction and improvement in image quality with the preservation of edge sharpness on low-dose lumbar spine CT. Investigations on further radiation dose reduction and the clinical applicability of this technique are warranted.
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