Foundations of 3D Tomography: From Data to Modeling

3D tomography is a powerful non-destructive imaging technique used across science and engineering to peer inside solid objects. Understanding how to transform raw scan data into meaningful physical insights is a highly valued skill in research and industry. This text-based course guides you through the entire tomography workflow. You will progress from foundational imaging concepts to advanced techniques, learning how to reconstruct, segment, and model 3D data to extract critical physical and mechanical properties. What you'll learn: - Understand the fundamental physics of data acquisition and the core principles of computed tomography. - Perform 3D reconstruction from raw projection data using standard mathematical algorithms. - Apply modern image segmentation techniques, including machine learning-assisted thresholding, to isolate key features. - Generate high-quality 3D meshes from segmented image volumes for computational analysis. - Extract physical parameters to model mechanical behavior and fluid flow properties within scanned materials. - Utilize Python-based libraries to automate image processing workflows and analyze volumetric datasets. The course starts with essential terminology and the physics of scanner setups before moving into practical code-based examples. You will read clear explanations and follow step-by-step programming guides to build a robust data analysis pipeline. This program is designed for beginners, students, and researchers looking to enter the field of 3D imaging, with no advanced prerequisites required. Start exploring the inner structure of materials and master the essentials of 3D tomography today.