Biomedical Imaging Systems II: Targeted Molecular Imaging in Disease
This course is designed as an introduction to the growing world of molecular imaging in medicine and research. The course is divided into five modules based on common imaging modalities (optical imaging, ultrasound methods, radiography, nuclear imaging, and magnetic resonance approaches). Within each module the fundamental physics and engineering behind each modality, corresponding methods for targeted molecular imaging including contrast mechanisms and probe design, and signal and image processing algorithms are covered. Homework assignments will utilize imaging data from either clinical or research studies in order to provide training in MATLAB based image analysis techniques.
Topics
- To expose students interested in biomedical research or clinical practice to fundamentals of modern imaging methods and interpretation Establish a foundational understanding of MRI (multi-spectral), PET/SPECT, Ultrasound (including photo-acoustic imaging), and emerging methods including MPI Discuss the design of targeted molecular contrast agents for each modality across myriad biological applications Discuss limitations to each targeted approach including non-specific binding, unbound probe clearance, signal decay, etc To learn quantitative approaches to analyze biomedical images (includes pharmacokinetic models, attenuation correction, cross modality registration, etc.) To discuss imaging ethics in the context of data interpretation Reinforce mathematical principles relevant to image analysis including linear algebra, convolution and differential equations Establish proficiency in the use of MATLAB as a tool for analyzing biomedical imaging data
- Understand the devices, techniques and protocols used for in vivo imaging in research and clinical settings Analyze imaging data derived from imaging studies using commonly utilized image processing techniques Critically evaluate scientific publications in the molecular imaging space.
Official catalogue course objectives and student learning outcomes
Sources
- Course catalogue ↗2026-2027