Class Medical Imaging I

Medical Imaging I belongs to the health sciences area and introduces the anatomical, pathological, physical, and technological foundations required for the acquisition, reconstruction, optimization, and interpretation of medical images. The curricular unit covers Computed Tomography, Magnetic Resonance Imaging, Positron Emission Tomography, PET-CT integration, radiotherapy, and emerging artificial intelligence applications. Its relevance within the study programme derives from the need to connect biomedical knowledge, image formation principles, and computational tools, preparing students to analyse image quality, artefacts, safety, dose, protocols, and clinical applications. It therefore provides a core foundation for further training in image processing, computational biomedicine, artificial intelligence, and health decision support.

Not applicable

Imaging anatomy: radiological correlations, anatomical structures across imaging modalities, and normal variations. 2. Imaging pathology: common diseases, differentiation between benign and malignant lesions, and comparison across modalities. 3. Computed Tomography: physical foundations, acquisition, processing, reconstruction, protocols, and dose optimization. 4. Magnetic Resonance Imaging: resonance physics, sequences, planes, contrast, acquisition, reconstruction, and post-processing. 5. Magnetic Resonance optimization: sequence selection, parameter adjustment, contrast, resolution, and artefact reduction. 6. Positron Emission Tomography: foundations, acquisition, reconstruction, PET-CT integration, radiation, safety, and clinical applications. 7. Emerging technologies in medical imaging and radiotherapy, including artificial intelligence applications, techniques, and equipment.

The teaching-learning process combines interactive lectures, guided pre-class study, analysis of current scientific articles, problem solving, and projects of increasing complexity. Digital materials are provided in advance, enabling a flipped-classroom approach and evidence-based discussion. In theoretical-practical sessions, students analyse images, acquisition parameters, reconstruction, quality, dose, and artefacts, individually and in groups. Case- and project-based learning supports the integrated application of anatomy, pathology, physics, and technology. Critical discussion of emerging technologies and artificial intelligence applications promotes scientific literacy, autonomy, and continuous updating. Collaborative work, formative feedback, and presentation of solutions strengthen communication, technical reasoning, and decision-making skills.

Ahishakiye, E., Van Gijzen, M., Tumwiine, J., Wario, R., & Obungoloch, J. (2021). A survey on deep learning in medical image reconstruction. Intelligent Medicine. Tanenbaum, L. (2020). Artificial Intelligence and Medical Imaging: Image Acquisition and Reconstruction. Applied Radiology, 34-35. Brown, B. H., & et al. (2017). Medical Physics and Biomedical Engineering: Medical Science Series. CRC Press. Suetens, P. (2017). Fundamentals of Medical Imaging. Cambridge University Press. Lima, J. J. Pedroso de. (2008). Física em Medicina Nuclear: Temas e Aplicações. Imprensa da Universidade de Coimbra. Bushong (2017). Radiologic science for technologists: Physics, Biology, and protection (11th ed.). St. Louis: Elsevier.