Toward a priori noise characterization for real-time edge-aware denoising in fluoroscopic devices

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.

Automatic landmark detection and mapping for 2D/3D registration with BoneNet

“…Moreover, the current noise simulation considers neither X-ray source model nor detector responses. Although the preliminary results indicate a high robustness to noise, a further study is necessary to train and to evaluate the performance of BoneNet at the noise level of a real X-ray fluoroscopy system ( 46 ). As an alternative, specialized denoising methods could be applied directly to the acquired fluoroscopy images prior to the 2D landmark inference.…”

Contactless Electrocatheter Tracing within Human Body via Magnetic Sensing: A Feasibility Study

“…To try to reduce quantum noise while maintaining a low radiation dose, various image processing techniques have been proposed, and some of these are implemented by modern fluoroscopic systems. In particular, some studies present filtering in space and time that guarantee a conspicuous reduction of quantum noise and also the preservation of contrasting image details such as, for example, those related to a lead [ 10 , 11 , 12 ].…”