Material Detail

An open educational resource module providing a mathematically rigorous foundation in Medical Imaging across projection radiography, CT, MRI, ultrasound, and nuclear medicine (PET/SPECT). Explores the ALARA radiation protection framework, k-space Fourier transformation, iterative reconstruction, and photon-counting CT. Bridges classical inverse problems to contemporary 2026 computational paradigms, including Physics-Informed Neural Networks (PINNs) for sparse-angle Radon transform CT inversion, Fourier Neural Operators (FNOs) for real-time k-space MRI reconstruction, and generative diffusion models for low-count PET Poisson denoising. Includes an analytical multi-step derivation evaluating CT linear attenuation coefficients from Hounsfield Units, Dose-Length Product (DLP), effective dose (ICRP-103), and lifetime attributable cancer risk, a structured systems engineering trade-off matrix, an interactive radiation dose simulator canvas, and three tiers of self-assessment problems with full quantitative solutions.

This will delete the comment from the database. This operation is not reversible. Are you sure you want to do it?

Thank you for reporting a broken "Go to Material" link in MERLOT to help us maintain a collection of valuable learning materials.

If you feel this material is inappropriate for the MERLOT Collection, please click SEND REPORT, and the MERLOT Team will investigate. Thank you!

You are being taken to the material on another site. This will open a new window.

Do not show me this again