# Direct 3D Reconstruction Methods for Electrical Impedance Tomography for Stroke Imaging

> **NIH NIH R21** · MARQUETTE UNIVERSITY · 2020 · $225,182

## Abstract

Project Summary
Every four minutes an American dies from stroke, equating to approximately 1 in every 19 US deaths annually. Strokes
are classified as ischemic and hemorrhagic. Ischemic strokes make up 87% of all strokes and are caused by a blockage in
a blood vessel (or artery) resulting in a lack of blood to the brain. A hemorrhagic stroke occurs when an artery in the
brain leaks or ruptures releasing excess blood in or around the brain. Incorrect classification can have dire consequences
as treating a patient suffering from a hemorrhagic stroke (bleed) with anticoagulant drugs (used to dissolve blood clots
for ischemic strokes) can prove fatal. Early action is of the utmost importance as each passing minute that brain cells lack
the proper blood flow additional cells die. Current classification methods require tests performed at the hospital, e.g., CT
or MRI scans of the patient's brain, leading to treatment delays. These delays are particularly lengthy for patients living
in rural communities.
 Electrical Impedance Tomography (EIT) is an emerging medical imaging modality that is inexpensive, has no ionizing
radiation, and provides portable high-contrast images using harmless surface current and voltage measurements (e.g., on
the head using a flexible hat) to recover the internal point-wise electrical properties (e.g., inside the brain). EIT can recover
conductivity, a measure of how easily current flows through a material, as well as permittivity, a measure of the ability of a
material to store a charge. A hemorrhagic stroke corresponds to an area of abnormally high conductivity due to the bleed,
whereas an ischemic stroke presents as an area of lower conductivity than expected due cellular swelling from energy
failure.
 The proposed project addresses the important problem of early, fast, portable stroke classification with EIT. A critical
barrier for the use of EIT for stroke imaging has been the sensitivity of the image reconstruction algorithms to incorrect
domain modeling and noise in the data. Due to these challenges, most research has focused on monitoring applications,
not helpful for the classification task. By contrast, the D-bar reconstruction method proposed here is the only proven
noise and modeling error robust reconstruction method capable of recovering the true conductivity/permittivity using a
low-pass filtering in a nonlinear Fourier domain. D-bar methods have been successful in 2D but their development in
3D is stunted. This proposal focuses on the development of fast, robust D-bar based reconstruction methods for the 3D
partial boundary problem, critical to working with stroke EIT data. Numerical algorithms will be developed for the full
and partial boundary problems in 3D and validated on simulated and experimental data. A priori information, from
anatomical atlases, will be embedded into the methods for increased resolution and stability. As the low-pass filtering
in D-bar methods leads to blurred reconstructions, post-processing t...

## Key facts

- **NIH application ID:** 9872170
- **Project number:** 5R21EB028064-02
- **Recipient organization:** MARQUETTE UNIVERSITY
- **Principal Investigator:** Sarah J Hamilton
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $225,182
- **Award type:** 5
- **Project period:** 2019-03-01 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9872170

## Citation

> US National Institutes of Health, RePORTER application 9872170, Direct 3D Reconstruction Methods for Electrical Impedance Tomography for Stroke Imaging (5R21EB028064-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9872170. Licensed CC0.

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