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Innovative Retinal Prosthesis: Restoring Vision with Electrical Stimulation

This research group focuses on developing an image encoder for mapping retinal electrical stimulation to restore vision in patients with retinitis pigmentosa and age-related macular degeneration. The advanced visual and perceptual display aims to provide a functional interface for medical applications, including laparoscopic images, MRI data, stress/strain fields, and more. Additionally, they work on managing massive medical image databases efficiently to support electronic medical records and measurement tools.

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Innovative Retinal Prosthesis: Restoring Vision with Electrical Stimulation

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  1. Image Processing, Perception and Display

  2. Group Members • Eyal Margalit (UNMC) • Tim Baxter (UNMC) • Balasubraniam Sajja (UNMC) • Khalid Sayood (UNL) • Carl Nelson (UNL/UNMC) • Cho Wing Solomon To (UNL) • Ashok Samal (UNL)

  3. Core Ideas • Image Encoder for Mapping Retinal Electrical Stimulation • Advanced Integrated Visual and Perceptual Display for Medical Applications • Management of Massive Medical Image Databases

  4. A new retinal prosthesis

  5. Image Encoder for Mapping Retinal Electrical Stimulation • Restore vision patients blind from retinitis pigmentosa and age related macular degeneration • Provide electrical stimulations to activate the viable cells in the diseased retina • Challenges • Find the parts of the brain that interpret the information • Find a mapping from optical pattern (image) to electrical pattern by encoding what the brain sees • Adjustable by patients • MEG Imaging could be useful • Information compression

  6. Image Encoder for Mapping Retinal Electrical Stimulation • People • Eyal Margalit (UNMC) • Sanjay Singh (UNMC) • Wallace Thorson (UNMC) • Bala Sajja (UNMC) • Ashok Samal (UNL) • Khalid Sayood (UNL) • External Resources • Naval Research Lab • College of Computing • Centers • MEG Center

  7. Advanced Integrated Visual and Perceptual Display for Medical Applications • Provide innovative and functional interface for medical applications • Include new modalities • Laparoscopic images, MRI data, stress/strain fields, pH, auditory, etc. • Multi sensor fusion • Provide analysis • Preops, real-time interpretations • Pattern analysis and anomalies • Overlay analysis with the images, augmented reality, etc.

  8. Advanced Integrated Visual and Perceptual Display for Medical Applications • People • Carl Nelson (UNL/UNMC) • Nick Stergiou (UNO) • Joseph Siu (UNMC) • Shane Farritor (UNL) • Linxia Gu (UNL) • Hani Haider (UNMC) • Ashok Samal (UNL) • External Partners • Expertise and facilities in VR • Center for Medicare and Medicaid Services • Center for Advanced Surgical Technologies (CAST)

  9. Management of Massive Medical Image Databases • Electronic Medical Records Act • Interoperability • Efficient use of transmission and storage resources for medical data • Provide tools for measurement (CAD models) from images • Interrelated images • Operationalize (measurements) • Image database management

  10. Management of Massive Medical Image Databases • People • Khalid Sayood (UNL) • Mike Hoffman (UNL) • Sina Balkir (UNL) • Rod Markin (UNMC) • Joe Sisson (UNMC) • Bala Sajja (UNMC) • Ashok Samal (UNL) • External Partners • Jerry Gibson (UCSB)

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