Horenko Illia, Pospíšil Lukáš, Vecchi Edoardo, Albrecht Steffen, Gerber Alexander, Rehbock Beate, Stroh Albrecht, Gerber Susanne
Faculty of Mathematics, Technical University of Kaiserslautern, 67663 Kaiserslautern, Germany.
Department of Mathematics, VSB Ostrava, Ludvika Podeste 1875/17, 708 33 Ostrava, Czech Republic.
J Imaging. 2022 May 31;8(6):156. doi: 10.3390/jimaging8060156.
We propose a pipeline for synthetic generation of personalized Computer Tomography (CT) images, with a radiation exposure evaluation and a lifetime attributable risk (LAR) assessment. We perform a patient-specific performance evaluation for a broad range of denoising algorithms (including the most popular deep learning denoising approaches, wavelets-based methods, methods based on Mumford−Shah denoising, etc.), focusing both on accessing the capability to reduce the patient-specific CT-induced LAR and on computational cost scalability. We introduce a parallel Probabilistic Mumford−Shah denoising model (PMS) and show that it markedly-outperforms the compared common denoising methods in denoising quality and cost scaling. In particular, we show that it allows an approximately 22-fold robust patient-specific LAR reduction for infants and a 10-fold LAR reduction for adults. Using a normal laptop, the proposed algorithm for PMS allows cheap and robust (with a multiscale structural similarity index >90%) denoising of very large 2D videos and 3D images (with over 107 voxels) that are subject to ultra-strong noise (Gaussian and non-Gaussian) for signal-to-noise ratios far below 1.0. The code is provided for open access.
我们提出了一种用于合成生成个性化计算机断层扫描（CT）图像的流程，并进行辐射暴露评估和终身归因风险（LAR）评估。我们针对广泛的去噪算法（包括最流行的深度学习去噪方法、基于小波的方法、基于Mumford-Shah去噪的方法等）进行了患者特异性性能评估，重点在于评估降低患者特异性CT诱导的LAR的能力以及计算成本的可扩展性。我们引入了一种并行概率Mumford-Shah去噪模型（PMS），并表明它在去噪质量和成本扩展性方面明显优于所比较的常见去噪方法。特别是，我们表明它能使婴儿的患者特异性LAR降低约22倍，成人的LAR降低10倍。使用普通笔记本电脑，所提出的PMS算法能够对非常大的二维视频和三维图像（超过107体素）进行廉价且强大的（多尺度结构相似性指数>90%）去噪，这些图像受到极强噪声（高斯和非高斯）影响，信噪比远低于1.0。代码可供开放获取。
