# OCT and OCTA image processing for retinal assessment of people with MS

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $454,336

## Abstract

Project Summary/Abstract (Max 30 lines of text)
Although magnetic resonance imaging (MRI) is necessary for diagnosing multiple sclerosis (MS), it has been
challenging to acquire consistent MRI measurements of MS disease burden. Spectral domain optical
coherence tomography (OCT) of the retina has emerged as a complementary source of imaging biomarkers,
wherein retinal thickness measurements have been shown to correlate well with MS disease burden. As well,
OCT angiography (OCTA)—a new imaging modality acquired with the same OCT scanner—yields multiple
new biomarkers among which macular vessel density has been shown to correlate with MS disability. There is
strong evidence that OCT and OCTA may provide much needed imaging biomarkers for MS, but there are
remaining technical challenges to overcome. Many algorithms for computation of retinal layer thicknesses
from OCT images have been developed, but measurement of longitudinal changes in individual MS subjects
remains highly challenging, especially in MS where yearly changes are small relative to intrinsic measurement
variations. We propose a novel iterative registration and deep learning segmentation algorithm for longitudinal
OCT retinal image segmentation. Development of automatic algorithms for analysis of OCTA images is in an
early stage and there are opportunities for significant improvements. We will develop a deep network for OCTA
vessel segmentation and biomarker computation that both suppresses artifacts that are common in OCTA and
provides consistent results across different scanners. As both OCT and OCTA become more widely used in
the characterization and management of MS, it is becoming increasingly important to jointly characterize these
biomarkers and relate them to disease status, which is currently characterized largely by clinical evaluations.
We will address the central question of whether OCT and OCTA can be used to predict disease progression by
developing a new disease progression score for MS based on multiple OCT and OCTA measurements as well
as clinical and MRI biomarkers, acquired in both single and multiple imaging visits. The proposed research will:
1) Develop a fast, topologically-correct longitudinal segmentation method for the macula; 2) Develop a method
for artifact-suppressed and consistent computation of OCTA features in the macula; 3) Develop a disease
progression score to jointly characterize longitudinal retinal OCT and OCTA measurements in MS; and
4) Carry out longitudinal studies of healthy controls and people with MS using OCT and OCTA measurements.
We will assess whether average features within the macula or features averaged over smaller segments yield
better estimates of progression. We will also assess whether OCT alone or OCT together with OCTA provide
better estimates of progression. Image processing and disease progression algorithms will be made freely
available to the research community. The proposed research will greatly advance the use of OCT and OCT...

## Key facts

- **NIH application ID:** 10818611
- **Project number:** 5R01EY032284-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Jerry L Prince
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $454,336
- **Award type:** 5
- **Project period:** 2021-03-01 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10818611

## Citation

> US National Institutes of Health, RePORTER application 10818611, OCT and OCTA image processing for retinal assessment of people with MS (5R01EY032284-04). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10818611. Licensed CC0.

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