Computer Aided Medical Procedures, Technische Universität München, Munich, Germany.
Siemens Healthcare GmbH, Advanced Therapies, Forchheim, Germany.
Int J Comput Assist Radiol Surg. 2018 Jun;13(6):847-854. doi: 10.1007/s11548-018-1746-2. Epub 2018 Apr 10.
Clinical procedures that make use of fluoroscopy may expose patients as well as the clinical staff (throughout their career) to non-negligible doses of radiation. The potential consequences of such exposures fall under two categories, namely stochastic (mostly cancer) and deterministic risks (skin injury). According to the "as low as reasonably achievable" principle, the radiation dose can be lowered only if the necessary image quality can be maintained.
Our work improves upon the existing patch-based denoising algorithms by utilizing a more sophisticated noise model to exploit non-local self-similarity better and this in turn improves the performance of low-rank approximation. The novelty of the proposed approach lies in its properly designed and parameterized noise model and the elimination of initial estimates. This reduces the computational cost significantly.
The algorithm has been evaluated on 500 clinical images (7 patients, 20 sequences, 3 clinical sites), taken at ultra-low dose levels, i.e. 50% of the standard low dose level, during electrophysiology procedures. An average improvement in the contrast-to-noise ratio (CNR) by a factor of around 3.5 has been found. This is associated with an image quality achieved at around 12 (square of 3.5) times the ultra-low dose level. Qualitative evaluation by X-ray image quality experts suggests that the method produces denoised images that comply with the required image quality criteria.
The results are consistent with the number of patches used, and they demonstrate that it is possible to use motion estimation techniques and "recycle" photons from previous frames to improve the image quality of the current frame. Our results are comparable in terms of CNR to Video Block Matching 3D-a state-of-the-art denoising method. But qualitative analysis by experts confirms that the denoised ultra-low dose X-ray images obtained using our method are more realistic with respect to appearance.
临床中使用透视技术的程序可能会使患者以及临床工作人员（在整个职业生涯中）受到不可忽视的辐射剂量。这些暴露的潜在后果分为两类，即随机（主要是癌症）和确定性风险（皮肤损伤）。根据“尽可能低”的原则，只有在维持必要的图像质量的情况下，才能降低辐射剂量。
我们的工作通过利用更复杂的噪声模型来改进现有的基于补丁的去噪算法，从而更好地利用非局部自相似性，这反过来又提高了低秩逼近的性能。所提出方法的新颖之处在于其合理设计和参数化的噪声模型以及消除初始估计。这大大降低了计算成本。
该算法已经在 500 张临床图像（7 位患者，20 个序列，3 个临床部位）上进行了评估，这些图像是在超低剂量水平下拍摄的，即标准低剂量水平的 50%，用于电生理程序。发现对比度噪声比（CNR）平均提高了约 3.5 倍。这与在大约 12 倍（3.5 的平方）超低剂量水平下实现的图像质量相关。射线图像质量专家的定性评估表明，该方法产生的去噪图像符合所需的图像质量标准。
结果与使用的补丁数量一致，并且它们表明可以使用运动估计技术和从先前帧“回收”光子来提高当前帧的图像质量。我们的结果在 CNR 方面与 Video Block Matching 3D（一种最先进的去噪方法）相当。但是专家的定性分析证实，使用我们的方法获得的超低调射线图像去噪更加逼真。
