Improving visibility of small anatomical details on low and ultra-low dose computed tomography with artificial intelligence-based image reconstructions.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 41345 Gothenburg, Sweden.
- Department of Radiology, Sahlgrenska University Hospital, 41346 Gothenburg, Region Västra Götaland, Sweden.
- Department of Radiology, Forth Valley Royal Hospital, NHS Forth Valley, Larbert FK5 4WR, Scotland, United Kingdom.
- South East Scotland Breast Screening Service, Edinburgh EH11 2JL, Scotland, United Kingdom.
- Department of Medical Radiation Sciences, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, 41345 Gothenburg, Sweden.
- Department of Biomedical Engineering and Medical Physics, Sahlgrenska University Hospital, 41345 Gothenburg, Region Västra Götaland, Sweden.
Abstract
To assess how computed tomography (CT) image reconstruction techniques affect perceived diagnostic image quality at varying radiation dose levels in chest imaging. A PBU-50 anthropomorphic phantom (small adult-sized model) and an air-dried human lung specimen were scanned on the same CT system (Revolution Apexâ„¢, GE Healthcare) at six dose levels (CTDIvol) from 0.07 to 2.19Â mGy for the smallest phantom size. Images were reconstructed using deep learning image reconstruction-high (DLIR-H), adaptive statistical iterative reconstruction at 40 per cent (ASiR-V), and filtered back projection (FBP). Five radiologists assessed anatomical reproduction, noise, artefacts, and diagnostic quality using ViewDEX. Descriptive statistics and visual grading characteristics analysis were used. In general, DLIR-H scored higher than ASiR-V and FBP. While maintaining image quality, DLIR-H allowed dose reduction compared to FBP. All methods were deemed acceptable for diagnosing pulmonary nodules, fibrosis, and peribronchial pathology. The results indicate that DLIR-H improves image quality in comparison to FBP and ASiR-V and may enable radiation dose reduction while maintaining clinical image quality.