AI-Assisted Open-Source Textbook on Medical Imaging: A Complete Learning Path from Physical Principles to Systems Engineering

This project is an open medical imaging textbook built on GitHub Pages, covering the physical principles and mathematical foundations of core technologies like X-ray, CT, PET, MRI, and ultrasound, with continuous iteration via collaborative editing. As a research project on AI-assisted educational content creation, it explores the potential of large language models in academic content generation and knowledge organization, aiming to fill the gap in interdisciplinary systematic learning resources for medical imaging.

Medical imaging is a core technology in modern medical diagnosis, but its deep integration across physics, engineering, mathematics, and clinical medicine has led to a relative scarcity of systematic learning resources. The medical-imaging-book project developed by NikhilaRao1 uses GitHub Pages as a platform, is written in Markdown format, and builds a modular, collaboratively editable online textbook covering a complete knowledge system from physical principles to system concepts, while exploring the possibilities of AI-assisted educational content creation.

Currently, the textbook has completed the core content of the first two chapters:

Chapter 1: Introduction to Medical Imaging Starting from the history of Roentgen's discovery of X-rays, it explains the principles and limitations of X-ray photography, then expands to the working principles of technologies like PET/SPECT (nuclear medicine imaging), MRI (non-ionizing radiation soft tissue imaging), and ultrasound (real-time, portable, safe), comparing the characteristics of each technology.

Chapter 2: Fundamentals of Signals and Systems Starting with functions and mappings, it delves into linear system theory (superposition, homogeneity), demonstrating properties through circuit component examples; it emphasizes the fundamental role of time-invariance in convolution, filtering, and reconstruction algorithms, and introduces mathematical tools like Taylor expansion and Fourier analysis to lay a theoretical foundation for subsequent content.

Systematic Integration of Multimodal Imaging: Placing various imaging technologies in the perspective of the electromagnetic spectrum (from gamma rays to radio waves, visible light, and mechanical waves), comparing the interaction mechanisms between different energies and biological tissues, and establishing a unified theoretical framework.

Deep Integration of Mathematics and Physics: Not shying away from mathematical tools, combining derivations with physical intuition (e.g., the relationship between CT's Radon transform and the Fourier slice theorem, mathematical inversion in PET reconstruction), ensuring rigor while enhancing readability.

AI-Assisted Content Generation: Large language models participate in multiple links from chapter planning to detailed elaboration, accelerating the writing progress. All content is manually reviewed to ensure accuracy, providing practical experience for intelligent educational content development.

The project uses the GitHub branch-merge workflow: the main branch stores official content, experimental branches collect community contributions, and modifications need to be reviewed and merged via Pull Request, balancing openness and content stability.

Illustration Strategy: Using AI-generated original images or clearly marking external sources, solving the image matching problem while respecting intellectual property rights.

Learning Value: Provides biomedical engineering students with a complete path from basics to cutting-edge; helps clinicians understand imaging principles to choose diagnostic methods rationally; offers AI/computer science researchers an entry point into the field of medical imaging computing.

Future Plans: Expand chapters on optical imaging, image processing and analysis, genotype-phenotype association, etc., and include emerging applications such as AI-assisted diagnosis, multimodal fusion imaging, and augmented reality-guided surgery.

Medical imaging is undergoing a transformation from anatomy to function, single to multimodal, and manual to intelligent. This project is not only a learning resource but also an open collaborative knowledge co-construction experiment, demonstrating the huge potential of combining open-source communities with AI technology in the education field, and exploring a feasible path for the democratic dissemination of medical imaging knowledge.