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Title
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Pulmonary nodule detection in chest CT using a deep learning-based reconstruction algorithm
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Author
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Abstract
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| This study’s aim was to assess whether deep learning image reconstruction (DLIR) techniques are non-inferior to ASIR-V for the clinical task of pulmonary nodule detection in chest computed tomography. Up to 6 (range 3–6, mean 4.2) artificial lung nodules (diameter: 3, 5, 8 mm; density: −800, −630, +100 HU) were inserted at different locations in the Kyoto Kagaku Lungman phantom. In total, 16 configurations (10 abnormal, 6 normal) were scanned at 7.6, 3, 1.6 and 0.38 mGy CTDIvol (respectively 0, 60, 80 and 95% dose reduction). Images were reconstructed using 50% ASIR-V and a deep learning-based algorithm with low (DL-L), medium (DL-M) and high (DL-H) strength. Four chest radiologists evaluated 256 series by locating and scoring nodules on a five-point scale. No statistically significant difference was found among the reconstruction algorithms (p = 0.987, average across readers AUC: 0.555, 0.561, 0.557, 0.558 for ASIR-V, DL-L, DL-M, DL-H). |
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Language
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English
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Source (journal)
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Radiation protection dosimetry. - Ashford
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Publication
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Ashford
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2021
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ISSN
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0144-8420
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DOI
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10.1093/RPD/NCAB025
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Volume/pages
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195
:3-4
(2021)
, p. 158-163
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ISI
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000711245400006
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Pubmed ID
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33723584
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Full text (Publisher's DOI)
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Full text (open access)
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