Events Calendar

Task-Based Optimization for Medical Imaging

Speaker: Prof. Angel Pineda, Cal-State Fullerton

Talk Title: Task-Based Optimization for Medical Imaging

Series: Medical Imaging Seminar Series

Abstract: Medical images are typically obtained with a clinical task in mind. These tasks can often be modeled as signal detection or parameter estimation. In this talk, we will define the information content of medical images based on the performance of mathematical models for clinical tasks using those images. To quantify this type of information content we need to define the task (the intended use of the images), the statistics (the sources of variability in the data), and the observer (how we intend to obtain the information from the images). This “task-based” optimization can be used in a wide variety of settings. Examples will include choosing which data to acquire in optical diffusion tomography, optimizing a tomosynthesis system for lung nodule detection and deciding whether or not to accelerate the reconstruction of x-ray CT by binning the projection data. At the end of the talk we will discuss how this type of optimization could be used in MRI

Biography: Dr. Angel Pineda is an associate professor of mathematics at California State University, Fullerton. For the 2013-14 academic year he is a visiting scholar at the Magnetic Resonance Imaging Laboratory at University of Southern California. He received his BS in chemical engineering from Lafayette College in 1995 and his PhD in applied mathematics from the University of Arizona in 2002. His postdoctoral fellowship was in the radiology department of Stanford University from 2002 to 2006. His research on statistical inverse problems in medical imaging focuses on optimization using mathematical models of clinical tasks. He is also active in mentoring undergraduate students in research and using mathematics education as a tool for international development.

Host: Professor Krishna Nayak

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

Audiences: Everyone Is Invited

Contact: Talyia Veal