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
1-R01-EB029431-01A1 Develop a large-scale library of comprehensive deformable image registration (DIR) benchmark datasets and an integrated framework for quantifying accuracy of patient-specific DIR results Deshan Yang Washington University
1-R21-EB030209-01A1 A Fully Decentralized Federated Learning Framework for Automated Image Segmentation in Cancer Radiotherapy Yading Yuan Icahn School of Medicine at Mount Sinai
1-R43-EB031655-01A1 Real-time AI-enhanced Low Dose Fluoroscopy Enhao Gong Subtle Medical, Inc.
5-R01-EB014955-08 Accelerating Community-Driven Medical Innovation with VTK Kenneth Martin Kitware, Inc.
5-R01-EB026859-10 Spatial inference methods for image analysis Armin Schwartzman University of California, San Diego
5-P41-EB015922-25 Laboratory of Neuro Imaging Resource (LONIR) Arthur Toga University of Southern California
7-R01-EB029431-02 Develop a large-scale library of comprehensive deformable image registration (DIR) benchmark datasets and an integrated framework for quantifying accuracy of patient-specific DIR results Deshan Yang Duke University
5-R01-EB027147-04 Mapping the developing infant connectome Sarah Shultz Emory University
5-R01-EB028324-04 Hybrid virtual-MRI/CBCT: A new paradigm for image guidance in liver SBRT Lei Ren University of Maryland Baltimore
1-R41-EB034186-01 An AI assistance tool to guide novice practitioners in the competent performance of flexible video laryngoscopy Dan Witte Perceptron Health, Inc.

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