Reconstructing images using Computed Tomography (CT) in an industrial context leads to specific challenges that differ from those encountered in other areas, such as clinical CT. Indeed, non-destructive testing with industrial CT will often involve scanning multiple similar objects while maintaining high throughput, requiring short scanning times, which is not a relevant concern in clinical CT. Under-sampling the tomographic data (sinograms) is a natural way to reduce the scanning time at the cost of image quality since the latter depends on the number of measurements. In such a scenario, post-processing techniques are required to compensate for the image artifacts induced by the sinogram sparsity. We introduce the Self-supervised Denoiser Framework (SDF), a self-supervised training method that leverages pre-training on highly sampled sinogram data to enhance the quality of images reconstructed from undersampled sinogram data. The main contribution of SDF is that it proposes to train an image denoiser in the sinogram space by setting the learning task as the prediction of one sinogram subset from another. As such, it does not require ground-truth image data, leverages the abundant data modality in CT, the sinogram, and can drastically enhance the quality of images reconstructed from a fraction of the measurements. We demonstrate that SDF produces better image quality, in terms of peak signal-to-noise ratio, than other analytical and self-supervised frameworks in both 2D fan-beam or 3D cone-beam CT settings. Moreover, we show that the enhancement provided by SDF carries over when fine-tuning the image denoiser on a few examples, making it a suitable pre-training technique in a context where there is little high-quality image data. Our results are established on experimental datasets, making SDF a strong candidate for being the building block of foundational image-enhancement models in CT.

翻译：在工业环境中使用计算机断层扫描（CT）重建图像会带来与临床CT等其他领域不同的特定挑战。实际上，工业CT的无损检测通常涉及在保持高吞吐量的同时扫描多个相似物体，这需要较短的扫描时间，而这在临床CT中并非关键考量。对断层扫描数据（正弦图）进行欠采样是减少扫描时间的自然方法，但会以牺牲图像质量为代价，因为图像质量取决于测量数量。在此情况下，需要后处理技术来补偿正弦图稀疏性引起的图像伪影。我们提出了自监督去噪器框架（SDF），这是一种自监督训练方法，利用高采样正弦图数据的预训练来提升从欠采样正弦图数据重建的图像质量。SDF的主要贡献在于提出在正弦图空间中训练图像去噪器，其学习任务设定为从一个正弦图子集预测另一个子集。因此，该方法无需真实图像数据，充分利用了CT中丰富的数据模态——正弦图，并能显著提升基于部分测量数据重建的图像质量。我们证明，在二维扇形束或三维锥形束CT设置中，SDF在峰值信噪比方面产生的图像质量优于其他解析型和自监督框架。此外，我们展示了SDF提供的增强效果在少量样本上对图像去噪器进行微调时依然保持，这使其成为在高质量图像数据稀缺场景下适用的预训练技术。我们的结果基于实验数据集建立，使得SDF成为构建CT基础图像增强模型的有力候选方案。