# High Resolution Microwave Tomographic Imaging of Brain Strokes Using Low-Frequency Measurements and Deep Neural Networks

> **NIH NIH R03** · OHIO STATE UNIVERSITY · 2022 · $78,750

## Abstract

PROJECT SUMMARY / ABSTRACT
According to the CDC, a stroke occurs in the United States every 40 seconds, with a fatality every 4 minutes
and associated reduction in mobility in more than half of survivors of ages 65 and over. The ability to
differentiate ischemic/hemorrhagic strokes in the pre-hospital setting and to monitor stroke evolution by the
bedside has the great potential to improve outcomes and reduce mortality. Unfortunately, state-of-the-practice
MRI and CT systems are bulky and pricy, restricting imaging to the clinical setting and sparse intervals. CT
also uses ionizing radiation that poses safety risks and further prohibits frequent imaging. Microwave
Tomographic Imaging (MTI) is a promising alternative/complementary option to MRI and CT, but has yet to be
used in the clinical setting. This is mainly due to its poor spatial resolution as feature dimensions are
comparable to the wavelength of the electromagnetic wave. Unfortunately, reducing the wavelength (i.e.,
increasing the measurement frequency) of MTI is not viable as high frequencies are prone to noise and severe
attenuation inside tissues. Instead, our goal is to explore the feasibility of expanding the fundamental limits of
MTI resolution via innovations in estimating high-frequency data from low-frequency measurements using
Deep Neural Networks (DNNs). We target detection of strokes <1cm×1cm that meets clinical expectations for
a much needed addition to the pre-hospital setting and throughout the stroke monitoring process. Hypothesis
1: A relationship exists between the low- and high-frequency data measured around a biological imaging
domain that we can use to ‘artificially’ increase the highest usable frequency for any given low-frequency
measurements. Hypothesis 2: An ‘artificial’ increase in frequency by N times will improve image resolution by N
times, regardless of the MTI reconstruction method used. Here, N depends on the highest usable frequency (to
be determined) and is expected to be at least equal to two. The study is significant because it reveals
previously unknown knowledge for enhancing MTI resolution in biological media. In Aim 1, we will develop the
DNN using 2D/3D solvers, canonical/anatomical head models, and a new class of into-body radiating antennas
with unprecedented efficiency. Our study will validate Hypothesis 1. In Aim 2, we will validate the DNN
numerically by using the estimated high-frequency data to reconstruct the image. Our study will validate
Hypothesis 2. In Aim 3, we will validate the DNN experimentally using tissue-emulating phantoms. Successful
reconstruction will entail improved (N times higher) image resolution vs. state-of-the-art MTI reconstruction at
the same measurement frequency. A comparison of image reconstruction accuracy using actual vs. estimated
high-frequency data will further reveal the method’s efficacy. Feasibility will form the basis of future studies on
human subjects. We envision this technique to be a much needed brea...

## Key facts

- **NIH application ID:** 10429133
- **Project number:** 1R03EB032927-01
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Asimina Kiourti
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $78,750
- **Award type:** 1
- **Project period:** 2022-07-01 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10429133

## Citation

> US National Institutes of Health, RePORTER application 10429133, High Resolution Microwave Tomographic Imaging of Brain Strokes Using Low-Frequency Measurements and Deep Neural Networks (1R03EB032927-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10429133. Licensed CC0.

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