microCT Image

X-ray micro-computed tomography (microCT) generates three-dimensional images of small samples at high resolution, enabling detailed analysis across diverse scientific fields. Current research focuses on improving image quality by mitigating artifacts like ring distortions, often employing deep learning models such as convolutional neural networks (CNNs), including U-Net architectures, for artifact removal and image enhancement. These advancements, coupled with the development of accessible workflows for image processing and segmentation using machine learning, are accelerating data analysis and expanding the applications of microCT in areas like materials science, plant biology, and geochemistry, particularly in addressing challenging inverse problems.