Imaging Science and Informatics Portfolio Program and Fellowship

The overarching theme of this program is to train "comprehensive imaging scientists" in the skills necessary to identify clinically relevant problems. Topics include: developing instrumentation, sensors, and contrast agents to form images appropriate for the problem; and analyzing the resulting imaging data using signal processing, mathematical modeling, visualizations, and informatics techniques to improve the prevention, detection, diagnosis, and treatment of human diseases.

Portfolio Program

The basic requirement to complete this portfolio program is 12 hours of coursework (4 courses), which provide the requisite core knowledge for an imaging scientist. More information on portfolio programs from Graduate School.

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Required Courses for Portfolio Completion (12 credit hours)

1. BME 381J.3 Biomedical Imaging Modalities (3 credit hours)

2. Elective in Image Processing (3 credit hours)

3. Elective in Modeling and Visualization (3 credit hours)

4. Elective in Data Mining and Informatics (3 credit hours)

Some, but not necessarily all, of these courses may count toward your degree requirements as well. Additionally, optional coursework, seminars, and externship opportunities are available to portfolio students.

If you are interested in completing the Imaging Science and Informatics Portfolio Program, please contact the Graduate Program Coordinator.

Fellowships

The Department of Biomedical Engineering was awarded a Ruth L. Kirschstein National Research Service Award (NRSA) training grant from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), an Institute within the National Institutes of Health (NIH). This grant includes competitive fellowships to support selected biomedical engineering doctoral students in the Imaging Science and Informatics Portfolio Program. The prestigious one-year fellowship includes a stipend, funds for educational and research supplies, and two semesters of tuition.

To be eligible for the fellowship, one must be an admitted doctoral student in Biomedical Engineering at The University of Texas at Austin. After admissions to the BME doctoral program, candidates will have the opportunity to request consideration for the fellowship.

This very prestigious fellowship supports interdisciplinary training through an extended support network of faculty and clinical mentors, toward the goal of becoming a "comprehensive imaging scientist." Students with research interests outside of imaging sciences and informatics have been successful at diversifying their training through this program, while having the honor of being a Ruth L. Kirschstein Fellow.

Additional Required Courses for Fellowship Recipients (6 credit hours; 2 courses):

In addition the 12 credit hours (4 courses) required to complete the Portfolio listed above, Kirschstein Fellowship Recipients must complete the following additional 6 credit hours (2 courses):

1. BME 381J.8 Functional Imaging Lab (3 credit hours)
2. Elective in Imaging Instrumentation or Contrast Mechanism (3 credit hours)

Fellowship recipients complete a total of 18 credit hours (6 courses) as described above for the Portfolio Program and Fellowship. Again, some or all of these courses may count toward your doctoral degree requirements as well. The program directors are available to help students select their elective coursework. Discuss with the Graduate Advisor and Graduate Program Coordinator.

Professional Development Opportunities

There are several professional development opportunities provided in the program that are required for fellowship recipients and recommended for portfolio students:

Summer Externship Experience between first and second year
Co-supervision and recommendations for committee structure
Professional Responsibilities in Imaging (BME 197E)
Graduate Professional Development Seminar (BME 197P)

Contact

Professor H. Grady Rylander, Training Program Co-Director
E-mail Address

Professor Mia K. Markey, Training Program Co-Director
E-mail Address

 

 

 

Contact Us

 

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Andrew Dunn, Ph.D., Graduate Advisor, Professor

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