Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy.
Biomedical Engineering, School of Life and Health Sciences, Aston University, Birmingham, B4 7ET, UK.
Biomed Eng Online. 2021 Apr 7;20(1):36. doi: 10.1186/s12938-021-00874-8.
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.
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.
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.
由于需要保证患者的最低合理辐射暴露，低剂量 X 射线图像在过去几十年中变得越来越流行。剂量减少会导致量子噪声大幅增加，需要对其进行适当抑制。特别是，需要实时降噪来支持常见的介入透视检查程序。噪声统计知识提供了宝贵的信息，有助于提高降噪性能，因此噪声估计成为有效降噪策略的关键任务。噪声统计数据取决于不同的因素，但主要受 X 射线管设置的影响，即使在同一程序中，这些设置也可能会发生变化。这使得实时降噪变得复杂，因为在管设置发生任何变化后，都需要重复噪声估计，这在实际中几乎是不可行的。本研究旨在探讨对单个透视设备进行噪声先验特征描述的可行性，从而避免在每次新图像采集之前推断噪声统计信息的需要。本研究中使用的噪声估计算法在计算机上进行了测试，以评估其准确性和可靠性。然后，使用商业透视设备在不同的 X 射线管设置下对两个不同的 X 射线体模进行成像，获取真实序列。最后，进行噪声估计，以评估在相同操作条件下独立获取的两个不同序列中推断的噪声统计信息的匹配程度。
无论所成像的场景如何，噪声估计算法都能够检索到噪声统计数据，即使仅使用 10 帧（平均百分比误差低于 2%）也能取得良好的结果。在真实透视序列上进行的测试证实，所估计的噪声统计数据与它们推断的场景的特定信息内容无关，因为它们在使用相同的 X 射线管设置独立获取的两个不同体模的序列中是一致的。
令人鼓舞的结果表明，对单个透视设备进行噪声先验特征描述是可行的，这可能会改善实时降噪策略的实际实施，这些策略利用噪声统计数据来改善降噪和细节保留之间的权衡。
