1. Research Funding
  2. Image Processing, Visual Perception and Display

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Image Processing, Visual Perception and Display

This program supports the design and development of algorithms for post-acquisition image processing and analysis, the development of theoretical models and analysis tools to evaluate and improve the perception of medical images, and the development of visualization tools for improved detection.

Emphasis

The emphasis is on using image data to achieve better health outcomes and smarter health care. Examples of technology development areas in this program include but are not limited to models, algorithms, software, methodologies, and other tools that will: facilitate medical imaging research;  support clinical detection, diagnosis and therapy; and improve patient healthcare. 

Program priorities and areas of interest:

  • Image segmentation, image registration, atlas generation, image fusion, morphometry measurement, and the determination of function and structure from medical images
  • Diagnostic-performance evaluation, computer-aided diagnosis, statistical models for evaluation of observer performance, and assessment of observer variability
  • Quantitative imaging and image-based biomarkers
  • Image-driven computer-aided diagnosis and decision support systems
  • Virtual reality technologies
  • Dose estimation and reduction software

Additional support

This program also supports:

  • Early-stage validation of tools for image processing, visual perception and display
  • Tools to assess image quality and observer performance
  • Tools and software that enable large-scale, longitudinal and/or multi-site imaging studies and clinical trials
  • Medical imaging mobile apps for early detection
 

Funded Projects

Grant Number Project Title Principal Investigator Institution
5-R01-EB022744-04 Computational Tools for Modeling Human and Mouse Connectome with Multi-Shell Diffusion Imaging Yonggang Shi University of Southern California
1-R03-EB028070-01 Dose Reduction in Pediatric/Adolescent Brain PET Imaging Through Artificial Neural Networks Jing Tang Oakland University
5-P41-EB019936-05 Center for Reproducible Neuroimaging Computation (CRNC) David Kennedy Univ of Massachusetts Med Sch Worcester
1-R43-EB028736-01 A Study to Validate and Improve an Automated Image Analysis Algorithm to Detect Tuberculosis in Sputum Smear Slides Cary Serif Diascopic, Llc
5-R01-EB023947-04 FluoRender: Visualization-Based and Interactive Analysis for Multichannel Microscopy Data Charles Hansen University of Utah
5-R01-EB016061-08 Causal Inference for Neuroimaging Martin Lindquist Johns Hopkins University
5-R21-EB024677-03 Development of motion-corrected 4D digital subtraction angiography and quantification of embolization related changes in hepatic arterial blood flow Paul Laeseke University of Wisconsin-Madison
5-R01-EB027586-03 CRCNS: Computational neuroimaging of the human David Ress Baylor College of Medicine
1-R21-EB029736-01 In Silico Mapping of the Heart-Brain Embolus Transport Pathway for Stroke Debanjan Mukherjee University of Colorado
5-R21-EB028330-02 Computer Vision-Based Navigation System for High-Precision Orthopedic Trauma Surgery Jeffrey Siewerdsen Johns Hopkins University

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