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Quantitative Evaluation of Image Quality and Radiation Dose Using Novel Intelligent Noise Reduction (INR) Software in
Ahmed Jibril Abdi1,2, Helle Precht3,4,5, Claus Bjørn Outzen6
1Department of Clinical Engineering, Region of Southern Denmark, Odense University Hospital, J.B. Winsløws Vej 4, 5000 Odense, Denmark.
Diagnostics (Basel, Switzerland)
|June 13, 2025
Summary
Intelligent Noise Reduction (INR) software significantly enhances chest radiography image quality. Combining INR with virtual anti-scatter grids reduces patient radiation dose by up to 82% while maintaining diagnostic quality.
Area of Science:
- Medical Imaging
- Radiography Technology
- Radiation Dose Optimization
Background:
- Evaluating novel Intelligent Noise Reduction (INR) software (NE 3.10.0.15) in chest radiography.
- Assessing performance across physical anti-scatter grid, non-grid, and virtual anti-scatter grid protocols.
- Aiming to optimize patient radiation dose and maintain image quality.
Purpose of the Study:
- Quantitatively evaluate INR software's impact on image quality and radiation dose.
- Compare different anti-scatter grid configurations (physical, non-grid, virtual).
- Determine optimal INR levels (0, 5, 8) for chest radiography protocols.
Main Methods:
- Utilized a CDRAD phantom with 20 cm PMMA for patient simulation.
- Evaluated image quality and radiation dose across PA and LAT projections.
- Estimated effective doses using PCXMC Monte Carlo simulation software.
Main Results:
- Image quality significantly improved with increasing INR levels (INR8 > INR5 > non-INR).
- Virtual or non-grid protocols reduced radiation dose by 77-82% compared to physical grids.
- Virtual grids enhanced quantitative image quality by 6-9% over non-grid configurations.
Conclusions:
- INR software, especially with virtual anti-scatter grids, effectively reduces radiation dose in chest radiography.
- This combination shows promise for improving image quality while lowering patient exposure.
- Further clinical validation with radiologist assessments is recommended for practice integration.
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