1. Toward a priori noise characterization for real-time edge-aware denoising in fluoroscopic devices
- Author
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Andreozzi, Emilio, Fratini, Antonio, Esposito, Daniele, Cesarelli, Mario, Bifulco, Paolo, Andreozzi, E., Fratini, A., Esposito, D., Cesarelli, M., and Bifulco, P.
- Subjects
Real-time denoising ,Matching (statistics) ,lcsh:Medical technology ,Computer science ,Noise reduction ,Biomedical Engineering ,Noise characterization ,02 engineering and technology ,Signal-To-Noise Ratio ,030218 nuclear medicine & medical imaging ,Biomaterials ,03 medical and health sciences ,0302 clinical medicine ,Poisson noise ,0202 electrical engineering, electronic engineering, information engineering ,Radiology, Nuclear Medicine and imaging ,Computer vision ,Noise estimation ,Reliability (statistics) ,Radiological and Ultrasound Technology ,business.industry ,Phantoms, Imaging ,Research ,Quantum noise ,X-ray imaging ,Shot noise ,Reproducibility of Results ,General Medicine ,Noise ,lcsh:R855-855.5 ,Fluoroscopy ,Edge-aware denoising, Fluoroscopy, Noise characterization, Noise estimation, Poisson noise, Quantum noise, Real-time denoising, X-ray imaging ,A priori and a posteriori ,020201 artificial intelligence & image processing ,Artificial intelligence ,Enhanced Data Rates for GSM Evolution ,business ,Edge-aware denoising ,Algorithms - Abstract
Background Low-dose X-ray images have become increasingly popular in the last decades, due to the need to guarantee the lowest reasonable patient’s exposure. Dose reduction causes a substantial increase of quantum noise, which needs to be suitably suppressed. In particular, real-time denoising is required to support common interventional fluoroscopy procedures. The knowledge of noise statistics provides precious information that helps to improve denoising performances, thus making noise estimation a crucial task for effective denoising strategies. Noise statistics depend on different factors, but are mainly influenced by the X-ray tube settings, which may vary even within the same procedure. This complicates real-time denoising, because noise estimation should be repeated after any changes in tube settings, which would be hardly feasible in practice. This work investigates the feasibility of an a priori characterization of noise for a single fluoroscopic device, which would obviate the need for inferring noise statics prior to each new images acquisition. The noise estimation algorithm used in this study was tested in silico to assess its accuracy and reliability. Then, real sequences were acquired by imaging two different X-ray phantoms via a commercial fluoroscopic device at various X-ray tube settings. Finally, noise estimation was performed to assess the matching of noise statistics inferred from two different sequences, acquired independently in the same operating conditions. Results The noise estimation algorithm proved capable of retrieving noise statistics, regardless of the particular imaged scene, also achieving good results even by using only 10 frames (mean percentage error lower than 2%). The tests performed on the real fluoroscopic sequences confirmed that the estimated noise statistics are independent of the particular informational content of the scene from which they have been inferred, as they turned out to be consistent in sequences of the two different phantoms acquired independently with the same X-ray tube settings. Conclusions The encouraging results suggest that an a priori characterization of noise for a single fluoroscopic device is feasible and could improve the actual implementation of real-time denoising strategies that take advantage of noise statistics to improve the trade-off between noise reduction and details preservation.
- Published
- 2021