Computer Aided Medical Procedures, Technische Universität München, Munich, Germany.
Siemens Healthineers AG, Advanced Therapies, Forchheim, Germany.
Biomed Phys Eng Express. 2022 Apr 7;8(3). doi: 10.1088/2057-1976/ac3489.
Since guidance based on x-ray imaging is an integral part of interventional procedures, continuous efforts are taken towards reducing the exposure of patients and clinical staff to ionizing radiation. Even though a reduction in the x-ray dose may lower associated radiation risks, it is likely to impair the quality of the acquired images, potentially making it more difficult for physicians to carry out their procedures.We present a robust learning-based denoising strategy involving model-based simulations of low-dose x-ray images during the training phase. The method also utilizes a data-driven normalization step-based on an x-ray imaging model-to stabilize the mixed signal-dependent noise associated with x-ray images. We thoroughly analyze the method's sensitivity to a mismatch in dose levels used for training and application. We also study the impact of differing noise models used when training for low and very low-dose x-ray images on the denoising results.A quantitative and qualitative analysis based on acquired phantom and clinical data has shown that the proposed learning-based strategy is stable across different dose levels and yields excellent denoising results, if an accurate noise model is applied. We also found that there can be severe artifacts when the noise characteristics of the training images are significantly different from those in the actual images to be processed. This problem can be especially acute at very low dose levels. During a thorough analysis of our experimental results, we further discovered that viewing the results from the perspective of denoising via thresholding of sub-band coefficients can be very beneficial to get a better understanding of the proposed learning-based denoising strategy.The proposed learning-based denoising strategy provides scope for significant x-ray dose reduction without the loss of important image information if the characteristics of noise is accurately accounted for during the training phase.
由于基于 X 射线成像的指导是介入性手术的一个组成部分，因此人们一直在努力降低患者和临床医护人员接受电离辐射的暴露。虽然降低 X 射线剂量可能会降低相关的辐射风险，但它也可能会降低所获取图像的质量，从而使医生更难以进行手术。我们提出了一种稳健的基于学习的去噪策略，该策略在训练阶段涉及基于模型的低剂量 X 射线图像模拟。该方法还利用基于 X 射线成像模型的数据驱动归一化步骤来稳定与 X 射线图像相关的混合信号相关噪声。我们彻底分析了该方法对训练和应用中使用的剂量水平不匹配的敏感性。我们还研究了当训练用于低剂量和超低剂量 X 射线图像的噪声模型时，对去噪结果的影响。基于采集的体模和临床数据的定量和定性分析表明，该基于学习的策略在不同剂量水平下是稳定的，如果应用了准确的噪声模型，则可以产生出色的去噪结果。我们还发现，如果训练图像的噪声特征与要处理的实际图像的噪声特征有很大差异，可能会出现严重的伪影。在超低剂量水平下，这个问题尤其严重。在对我们的实验结果进行深入分析时，我们还发现，从子带系数阈值处理的角度来看待去噪结果可以非常有助于更好地理解所提出的基于学习的去噪策略。如果在训练阶段准确考虑噪声特性，则该基于学习的去噪策略可在不丢失重要图像信息的情况下，为大幅降低 X 射线剂量提供了可能。
