ArticleJournal of applied clinical medical physics2021
Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study.
Article in Journal of applied clinical medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Potential radiation dose reduction in computed tomography with deep learning reconstruction: a retrospective monocentric study.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Thin-Slice Brain CT Image Quality and Lesion Detection Evaluation in Deep Learning Reconstruction Algorithm.Clinical neuroradiology · 2025Article
- Comparing two deep learning spectral reconstruction levels for abdominal evaluation using a rapid-kVp-switching dual-energy CT scanner.Abdominal radiology (New York) · 2025Article
- Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.Abdominal radiology (New York) · 2025Article
- Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects.Radiology · 2023Review
- Characteristics of the deep learning-based virtual monochromatic image with fast kilovolt-switching CT: a phantom study.Radiological physics and technology · 2023Article
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11 authors.
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Abstract
purposeIn an ultrahigh-resolution CT (U-HRCT), deep learning-based reconstruction (DLR) is expected to drastically reduce image noise without degrading spatial resolution. We assessed a new algorithm's effect on image quality at different radiation doses assuming an abdominal CT protocol.
methodsFor the normal-sized abdominal models, a Catphan 600 was scanned by U-HRCT with 100%, 50%, and 25% radiation doses. In all acquisitions, DLR was compared to model-based iterative reconstruction (MBIR), filtered back projection (FBP), and hybrid iterative reconstruction (HIR). For the quantitative assessment, we compared image noise, which was defined as the standard deviation of the CT number, and spatial resolution among all reconstruction algorithms.
resultsDeep learning-based reconstruction yielded lower image noise than FBP and HIR at each radiation dose. DLR yielded higher image noise than MBIR at the 100% and 50% radiation doses (100%, 50%, DLR: 15.4, 16.9 vs MBIR: 10.2, 15.6 Hounsfield units: HU). However, at the 25% radiation dose, the image noise in DLR was lower than that in MBIR (16.7 vs. 26.6 HU). The spatial frequency at 10% of the modulation transfer function (MTF) in DLR was 1.0 cycles/mm, slightly lower than that in MBIR (1.05 cycles/mm) at the 100% radiation dose. Even when the radiation dose decreased, the spatial frequency at 10% of the MTF of DLR did not change significantly (50% and 25% doses, 0.98 and 0.99 cycles/mm, respectively).
conclusionDeep learning-based reconstruction performs more consistently at decreasing dose in abdominal ultrahigh-resolution CT compared to all other commercially available reconstruction algorithms evaluated.
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