Medical Imaging Explained: Systems, Diagnostics & Imaging Technologies

This open educational resource (OER) curriculum module provides a comprehensive, mathematically rigorous foundation in Medical Imaging, spanning the physical principles, tomographic reconstruction algorithms, and clinical instrumentation that enable non-invasive anatomical and physiological diagnostics.

Covered modalities include projection radiography, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), diagnostic ultrasound, and nuclear medicine (PET/SPECT). The module details the ALARA radiation protection framework, k-space Fourier mechanics, iterative reconstruction, and photon-counting CT (PCCT). Classical inverse problems are bridged to contemporary 2026 computational paradigms, featuring:
- Physics-Informed Neural Networks (PINNs) enforcing linear attenuation Radon transform conservation for sparse-view CT dose reduction.
- Fourier Neural Operators (FNOs) and DeepONets for zero-shot, real-time k-space MRI reconstruction.
- Generative diffusion models for ultra-low-count PET Poisson denoising.
- A multi-step analytical calculation 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 analyzing spatial resolution versus radiation burden, static magnetic field strength versus RF SAR tissue heating, and acoustic frequency versus penetration depth.
- An interactive digital simulation canvas modeling the trade-off between CT tube current (mAs), tube potential (kVp), reconstruction algorithms (FBP, IR, DLIR), and effective radiation dose.
- Three tiers of self-assessment modules covering fundamental principles, clinical scenario analyses, and quantitative medical physics calculations with complete step-by-step solutions.

Permanent web resource: https://prep4uni.online/stem/physical-technologies/biomedical-engineering/medical-imaging/