Vision is compromised in at least 55% of multiple sclerosis (MS) patients and may represent the very first manifestation of disease onset. Spectral domain optical coherence tomography (SD-OCT) enables in-vivo, high-resolution studies of the retina, and is increasingly being used as a biomarker in neurodegenerative diseases. SD-OCT has provided phenotypical measurements of the retinal nerve fiber layer, ganglion cell layer, and overall retinal thinning, indicating both axonal and neuronal retinal pathology in MS. There is tantalizing evidence that deeper retinal layers are also affected in MS, which is of particular interest since these neurons are never myelinated. Thus, SD-OCT measurements have proven useful in the development of new scientific theories about the pathophysiology of MS. These measurements are also potentially useful in early detection of disease, staging disease severity, assessing disease progression, and determining therapeutic efficacy for individual MS patients. Although advanced automated algorithms for segmentation and measurement of key retinal features are emerging in both research labs and commercial instruments, there remain several key technical limitations to full exploitation of this three-dimensional imaging technique. First, retinal segmentation methods do not presently provide subvoxel precision nor are they robust to the presence of macular edema. Second, although three-dimensional comparisons of retinas are routine in standard ophthalmologic exams, the macular registration methods that are used do not separate the rigid and deformable components for detailed population comparisons in a normalized space. Third, retinal thicknesses are generally computed extrinsically along straight lines without compensation for relative pose. Together, these deficiencies hamper 3D longitudinal and cross-sectional scientific studies and limit monitoring disease progression in specific subjects. The proposed research will: 1) Develop a subvoxel retinal layer segmentation method for the macula that is robust to edema; 2) Develop both rigid and deformable registration methods that will permit analysis of SD-OCT volumes in a normalized space; 3) Develop intrinsic retinal thickness measurement techniques and an average macular atlas space; and 4) Carry out both cross-sectional and longitudinal studies of normal subjects and MS patients in a normalized space to test the hypotheses that i) deeper retinal layers are involved in MS and ii) longitudinal atrophy is observed in deeper retinal layers in MS. We will also explore whether regional macular edema precedes thinning in the inner nuclear layer in MS subjects. These studies will also include function/structure regression using the full macular volume, longitudinally assessed, against visual acuity functional scores. Image processing and regression algorithms will be developed within the open-source Java Image Science Toolkit (JIST) framework and released as open source software.

Public Health Relevance

Vision disability in multiple sclerosis (MS) patients is common and debilitating, and direct measurement of changes in the retina of the eye might provide guidance for therapy and better understanding of MS. This project will develop methods and software for analysis of optical coherence tomography (OCT) scans of the retina, and will validate the methods in a pilot study of MS and normal subjects. At the conclusion of the grant, software implementing the methods will be made available to the research community and commercial sector as open source software written in a highly portable computer language.

Agency
National Institute of Health (NIH)
Institute
National Eye Institute (NEI)
Type
Research Project (R01)
Project #
5R01EY024655-04
Application #
9301542
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Greenwell, Thomas
Project Start
2014-08-01
Project End
2019-06-30
Budget Start
2017-07-01
Budget End
2019-06-30
Support Year
4
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Johns Hopkins University
Department
Emergency Medicine
Type
Schools of Medicine
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21205
Lang, Andrew; Carass, Aaron; Jedynak, Bruno M et al. (2018) Intensity inhomogeneity correction of SD-OCT data using macular flatspace. Med Image Anal 43:85-97
Yun, Yeyi; Carass, Aaron; Lang, Andrew et al. (2017) Collaborative SDOCT Segmentation and Analysis Software. Proc SPIE Int Soc Opt Eng 10138:
Lang, Andrew; Carass, Aaron; Bittner, Ava K et al. (2017) Improving graph-based OCT segmentation for severe pathology in Retinitis Pigmentosa patients. Proc SPIE Int Soc Opt Eng 10137:
Button, Julia; Al-Louzi, Omar; Lang, Andrew et al. (2017) Disease-modifying therapies modulate retinal atrophy in multiple sclerosis: A retrospective study. Neurology 88:525-532
Antony, Bhavna J; Carass, Aaron; Lang, Andrew et al. (2017) Longitudinal Analysis of Mouse SDOCT Volumes. Proc SPIE Int Soc Opt Eng 10137:
Antony, Bhavna J; Chen, Min; Carass, Aaron et al. (2016) Voxel Based Morphometry in Optical Coherence Tomography: Validation & Core Findings. Proc SPIE Int Soc Opt Eng 9788:
Ellingsen, Lotta M; Roy, Snehashis; Carass, Aaron et al. (2016) Segmentation and labeling of the ventricular system in normal pressure hydrocephalus using patch-based tissue classification and multi-atlas labeling. Proc SPIE Int Soc Opt Eng 9784:
Lang, Andrew; Carass, Aaron; Al-Louzi, Omar et al. (2016) Combined registration and motion correction of longitudinal retinal OCT data. Proc SPIE Int Soc Opt Eng 9784:
Antony, Bhavna J; Lang, Andrew; Swingle, Emily K et al. (2016) Simultaneous Segmentation of Retinal Surfaces and Microcystic Macular Edema in SDOCT Volumes. Proc SPIE Int Soc Opt Eng 9784:
Lang, Andrew; Carass, Aaron; Jedynak, Bruno M et al. (2016) INTENSITY INHOMOGENEITY CORRECTION OF MACULAR OCT USING N3 AND RETINAL FLATSPACE. Proc IEEE Int Symp Biomed Imaging 2016:197-200

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