Tomographic Infrastructure

Tomographic infrastructure encompasses the techniques and technologies used to create three-dimensional images from multiple projections, with applications ranging from medical imaging to industrial quality control. Current research heavily emphasizes improving image quality and reducing scan times through the integration of deep learning methods, particularly self-supervised learning and convolutional neural networks, often combined with physics-based models to address issues like noise reduction and artifact removal in low-dose scenarios. These advancements are crucial for enhancing the speed, safety, and resolution of tomographic imaging across various fields, leading to improved diagnostics, more efficient industrial processes, and a deeper understanding of complex structures at multiple scales.