Tomographic Image

Tomographic imaging reconstructs cross-sectional views of an object from multiple projections, aiming to reveal internal structures. Current research emphasizes improving image quality and efficiency through advanced algorithms, including deep learning architectures like U-Nets and ConvNeXts, and optimization techniques for data acquisition and processing, such as trajectory optimization and self-supervised learning. These advancements are significantly impacting diverse fields, from medical diagnosis (e.g., improved COVID-19 detection and organ segmentation) to materials science (e.g., enhanced analysis of concrete cracks and bone structures) and even cosmic ray tomography. The overall goal is to achieve higher resolution, reduce noise, and minimize data acquisition time, leading to more accurate and efficient imaging across various applications.