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-EB022744-03 Computational Tools for Modeling Human and Mouse Connectome with Multi-Shell Diffusion Imaging Yonggang Shi University of Southern California
5-R01-EB023947-02 FluoRender: Visualization-Based and Interactive Analysis for Multichannel Microscopy Data Charles Hansen University of Utah
5-R01-EB019956-04 Algorithms for MR and OCT-based Architectonic and Laminar Segmentation Bruce Fischl Massachusetts General Hospital
5-R01-EB017255-05 Adaptive Large-Scale Framework for Automatic Biomedical Image Segmentation Paul Yushkevich University of Pennsylvania
5-R01-EB006733-09 Development of Robust Brain Measurement Tools Informed by Ultrahigh Field 7T MRI Pew-Thian Yap Univ of North Carolina Chapel Hill
5-R01-EB020062-03 Neurodegenerative and Neurodevelopmental Subcortical Shape Diffeomorphometry Michael Miller Johns Hopkins University
5-R01-EB016061-06 Causal Inference for Neuroimaging Martin Lindquist Johns Hopkins University
1-R01-EB026427-01 Visual Search in 3D Medical Imaging Modalities Miguel Eckstein University of California Santa Barbara
5-R21-EB022747-02 Deep-radiomics-learning for mass detection in CT colonography Janne Nappi Massachusetts General Hospital
1-R21-EB026668-01 Synergistic integration of deep learning and regularized image reconstruction for positron emission tomography Jinyi Qi University of California at Davis