# Comprehensive Training program in Imaging Science and Informatics

> **NIH NIH T32** · UNIVERSITY OF TEXAS AT AUSTIN · 2020 · $157,424

## Abstract

7. Project Summary/Abstract
The over-arching theme of this proposal is to train “comprehensive imaging scientists” in the skills
necessary to identify clinically relevant problems; develop instrumentation, sensors, and contrast
agents to form images appropriate for the problem; and analyze the resulting imaging data using
signal processing, mathematical modeling, visualization, and informatics techniques to improve the
prevention, detection, diagnosis, and treatment of human diseases. The program spans from molecular to
cellular to tissue to organ. In order for imaging scientists to be knowledgeable of the full trajectory from image
formation to analysis and decision-making, they must be trained in four core areas: Instrumentation, Devices,
and Contrast Agents; Image processing; Modeling and Visualization; and Data Mining and Informatics.
All students in the program are trained in the core concepts of these areas. The current training program is a
two-year pre-doctoral portfolio program. A total of 41 students have been admitted to the program. The
proposed renewal will train another 20 students. The program includes off-campus externship research
experiences; in-depth clinical engagement; and a wide-ranging professional development component.
Imaging Science is an integral element of basic science research and clinical medicine. Imaging cell trafficking
and receptor pharmacology in vivo have already led to targeted drug and gene therapies and an understanding
of cellular biochemical pathways will contribute to new advances in medicine. Individualized medicine relies
heavily on imaging techniques to select the best therapies and monitor progress. Although structural in situ
human imaging is already a critical component of clinical medicine, many advances are needed in functional
imaging of the brain and other organs to improve healthcare. Brain mapping which is a core focus of NIH
research relies heavily on imaging. We have identified a critical need for imaging scientists to develop new
imaging instrumentation and apply that instrumentation with appropriate methods from image processing;
modeling and visualization; and informatics and data mining. In recognition of the potential of artificial
intelligence to transform medical imaging, our program emphasizes applications of machine learning.
This training program fills a critical niche by providing highly skilled scientists who are trained in the broad
trajectory of imaging science. Understanding the interplay between instrumentation and image analysis,
including machine learning methods, is important for designing the next generation of hardware and software
tools for quantifying complex biological systems and providing robust clinical tools. A key outcome of the
program is that trainees gain the skills necessary to identify clinically relevant problems.

## Key facts

- **NIH application ID:** 9934889
- **Project number:** 2T32EB007507-11A1
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Mia K Markey
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $157,424
- **Award type:** 2
- **Project period:** 2009-08-01 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9934889

## Citation

> US National Institutes of Health, RePORTER application 9934889, Comprehensive Training program in Imaging Science and Informatics (2T32EB007507-11A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9934889. Licensed CC0.

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