MATHEMATICAL METHODS IN MEDICAL PHYSICS

Prerequisites & course information

Requisites: ( MATH 234 and 319 ), ( MATH 234 and 320 ), or MATH 376 and ( PHYSICS 202 or 208 ), graduate/professional standing, or declared in Medical Physics VISP Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Summarize the utility of signal analysis in one and several dimensions Audience: Both Grad & Undergrad 2. Identify and apply convolutions and Fourier Transforms in one and several dimensions Audience: Both Grad & Undergrad 3. Apply the properties of the Fourier Transform in medical physics and other biomedical settings Audience: Both Grad & Undergrad 4. Illustrate the limitations of the Fourier transform, and recall the advantages of alternative signal analysis tools (e.g. wavelet transform) Audience: Graduate 5. Distinguish between types of optimization problems, including convex vs non-convex, and unconstrained vs constrained Audience: Both Grad & Undergrad 6. Recognize the relative performance of basic optimization algorithms Audience: Both Grad & Undergrad 7. Formulate image reconstruction as an optimization problem Audience: Both Grad & Undergrad 8. Formulate therapy planning as an optimization problem Audience: Both Grad & Undergrad 9. Implement practical optimization algorithms using computational methods Audience: Both Grad & Undergrad Learning Outcomes: 1. Summarize the utility of signal analysis in one and several dimensions Audience: Both Grad & Undergrad 2. Identify and apply convolutions and Fourier Transforms in one and several dimensions Audience: Both Grad & Undergrad 3. Apply the properties of the Fourier Transform in medical physics and other biomedical settings Audience: Both Grad & Undergrad 4. Illustrate the limitations of the Fourier transform, and recall the advantages of alternative signal analysis tools (e.g. wavelet transform) Audience: Graduate 5. Distinguish between types of optimization problems, including convex vs non-convex, and unconstrained vs constrained Audience: Both Grad & Undergrad 6. Recognize the relative performance of basic optimization algorithms Audience: Both Grad & Undergrad 7. Formulate image reconstruction as an optimization problem Audience: Both Grad & Undergrad 8. Formulate therapy planning as an optimization problem Audience: Both Grad & Undergrad 9. Implement practical optimization algorithms using computational methods Audience: Both Grad & Undergrad