Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States of America.
Thayer School of Engineering at Dartmouth, Hanover, NH, United States of America.
Phys Med Biol. 2024 Nov 12;69(22):225015. doi: 10.1088/1361-6560/ad8c93.
Cherenkov imaging during radiotherapy provides a real time visualization of beam delivery on patient tissue, which can be used dynamically for incident detection or to review a summary of the delivered surface signal for treatment verification. Very few photons form the images, and one limitation is that the noise level per frame can be quite high, and mottle in the cumulative processed images can cause mild overall noise. This work focused on removing or suppressing noise via image postprocessing.Images were analyzed for peak-signal-to-noise and spatial frequencies present, and several established noise/mottle reduction algorithms were chosen based upon these observations. These included total variation minimization (TV-L1), non-local means filter (NLM), block-matching 3D (BM3D), alpha (adaptive) trimmed mean (ATM), and bilateral filtering. Each were applied to images acquired using a BeamSite camera (DoseOptics) imaged signal from 6x photons from a TrueBeam linac delivering dose at 600 MU minincident on an anthropomorphic phantom and tissue slab phantom in various configurations and beam angles. The standard denoised images were tested for PSNR, noise power spectrum (NPS) and image sharpness.The average peak-signal-to-noise ratio (PSNR) increase was 17.4% for TV-L1. NLM denoising increased the average PSNR by 19.1%, BM3D processing increased it by12.1% and the bilateral filter increased the average PSNR by 19.0%. Lastly, the ATM filter resulted in the lowest average PSNR increase of 10.9%. Of all of these, the NLM and bilateral filters produced improved edge sharpness with, generally, the lowest NPS curve.For cumulative image Cherenkov data, NLM and the bilateral filter yielded optimal denoising with the TV-L1 algorithm giving comparable results. Single video frame Cherenkov images exhibit much higher noise levels compared to cumulative images. Noise suppression algorithms for these frame rates will likely be a different processing pipeline involving these filters incorporated with machine learning.
放射治疗中的切伦科夫成像是对患者组织中射束传输的实时可视化，可用于动态检测事件，或用于审查已传输表面信号的概要，以进行治疗验证。 图像中只有很少的光子，限制之一是每帧的噪声水平可能相当高，累积处理后的图像中的斑纹会导致轻微的整体噪声。 这项工作侧重于通过图像后处理去除或抑制噪声。 分析了图像中的峰值信号与噪声比和存在的空间频率，并根据这些观察结果选择了几种已建立的噪声/斑纹减少算法。 这些算法包括全变差最小化（TV-L1）、非局部均值滤波器（NLM）、块匹配 3D（BM3D）、α（自适应）修剪均值（ATM）和双边滤波器。 这些算法都应用于使用 BeamSite 相机（DoseOptics）采集的图像，该相机从 TrueBeam 直线加速器的 6x 光子中获取信号，以 600 MU min 的剂量照射在人体模型和组织平板模型上，配置和射束角度各不相同。 在各种配置和射束角度下，对标准去噪图像进行 PSNR、噪声功率谱（NPS）和图像锐度测试。 TV-L1 的平均峰值信号与噪声比（PSNR）提高了 17.4%。 NLM 去噪使平均 PSNR 提高了 19.1%，BM3D 处理提高了 12.1%，双边滤波器提高了 19.0%。 最后，ATM 滤波器的平均 PSNR 提高了 10.9%。 在所有这些算法中，NLM 和双边滤波器提高了边缘锐度，而一般来说，NPS 曲线最低。 对于累积图像切伦科夫数据，NLM 和双边滤波器产生了最佳的去噪效果，而 TV-L1 算法的结果相当。 与累积图像相比，单个视频帧切伦科夫图像的噪声水平要高得多。 对于这些帧率，噪声抑制算法可能是一个不同的处理管道，涉及这些滤波器和机器学习的结合。
