Scientific publication highlights
Achieving 94% Dose Reduction with improved image quality and no loss of nodule detection capability using SilverBeam and AiCE
High quality images at a low radiation dose are required to optimize the early detection of lung cancer while minimizing the downstream risks of repeated radiation exposure to yield the benefits of lung cancer screening. This study explores the use of a silver-based spectral filter (SilverBeam) and an AI reconstruction algorithm (AiCE) to achieve significant dose reduction while preserving image quality and lung nodule detection accuracy across varying dose levels.
Conclusion:
"The Silver filter and DLR (Deep Learning Reconstruction) can significantly improve image quality and nodule detection capability compared with the Copper filter and other reconstruction methods at each of radiation doses used."
"The Silver filter and DLR (Deep Learning Reconstruction) can significantly improve image quality and nodule detection capability compared with the Copper filter and other reconstruction methods at each of radiation doses used."
Oshima, Yuka et al. | Capability for dose reduction while maintaining nodule detection: Comparison of silver and copper X-ray spectrum modulation filters for chest CT using a phantom study with different reconstruction methods | European journal of radiology vol. 166 (2023)
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Schaal, M. et al. | Diagnostic Performance of Ultra-Low-Dose Computed Tomography for Detecting Asbestos-Related Pleuropulmonary Diseases: Prospective Study in a Screening Setting| PLOS One (2016)
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