Johns Hopkins University Receives a 2014 NIH Grant for 3D Segmentation and Registration of Macular SD-OCT for Applications in Multiple Sclerosis
Johns Hopkins University Receives a 2014 NIH Grant for $387,284 for 3D Segmentation and Registration of Macular SD-OCT for Applications in Multiple Sclerosis. The principal investigator is Jerry Prince. Below is a summary of the proposed work.
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.