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Dana Peters, PhD

Professor of Radiology and Biomedical Imaging

Contact Information

Dana Peters, PhD

Mailing Address

  • Radiology & Biomedical Imaging

    PO Box 20843, 300 Cedar Street, (office phone) 203 737 6360

    New Haven, CT 06519

    United States

Magnetic Resonance Research Center
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Research Summary

Peters' research is motivated by the desire to generate new imaging methods to improve current diagnostic methods for cardiovascular disease, and improve effectiveness of therapies. She focuses on magnetic resonance imaging. Her group has developed technology for electrophysiologists who treat patients with electrical abnormalities in the heart. These MRI methods allow electrophysiologists to see tissue characteristics responsible for electrical changes. They have also developed new methods for rapid imaging, using undersampled radial MR acquisition strategies. Most importantly, is to develop MRI methods in close collaboration with cardiologists and radiologists to detect early disease and monitor therapy.

Peters is also committed to training a new generation of biomedical imaging scientists. She believes in providing an environment with excellent MRI resources, knowledge about state of the art methods, and important questions in collaboration with clinicians and scientists, to generate creative new solution in medical imaging.

Specialized Terms: Magnetic Resonance Imaging; Atrial fibrillation; Radial imaging; Late gadolinium enhancement; Cardiac imaging

Extensive Research Description

  • Liver imaging--new techniques
  • Imaging of scar in patients with ventricular tachycardia and atrial fibrillation.
    • New techniques for improving spatial resolution, motion, and contrast
    • application to patients.
  • Radial imaging with new reconstruction algorithms
  • Development of new cardiac MR methods for diastolic dysfunction.



Coauthors

Research Interests

Atrial Fibrillation; Diagnostic Imaging; Gadolinium; Liver; Magnetic Resonance Imaging; Radiology; Imaging, Three-Dimensional

Research Images

Machine learning enabled valve-tracking for measurement of LV function.
With machine learning, valves can be automatically tracked, enabling slice-following cine MRI.

Selected Publications

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