Pulmonary nodule detection in chest CT using a deep learning-based reconstruction algorithm
| Autor: | M. Spinhoven, Paul Deak, H El Addouli, Caro Franck, Federica Zanca, Annemiek Snoeckx, A. Van Hoyweghen, Simon Nicolay |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Chest ct
Iterative reconstruction Radiation Dosage Imaging phantom 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Deep Learning Pulmonary nodule Medicine Radiology Nuclear Medicine and imaging Biology Computer. Automation Radiation Radiological and Ultrasound Technology business.industry Phantoms Imaging Deep learning Public Health Environmental and Occupational Health Reconstruction algorithm General Medicine Chemistry 030220 oncology & carcinogenesis Radiographic Image Interpretation Computer-Assisted Dose reduction Tomography Artificial intelligence Human medicine business Nuclear medicine Tomography X-Ray Computed Engineering sciences. Technology Algorithms |
| Zdroj: | Radiation protection dosimetry |
| ISSN: | 0144-8420 |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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