(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 OCTA in characterizing longitudinal changes in the retina, potentially leading to standard eye measurements for monitoring and managing MS and other neurological and eye diseases.
Monitoring the progression of multiple sclerosis (MS) has been challenging due to the inconsistency and variability of both clinical and magnetic resonance imaging assessments. This project will develop methods and software for analysis of both optical coherence tomography (OCT) and OCT angiography (OCTA) scans of the retina, both of which show correlation to disability in MS. At the conclusion of the grant, software implementing the methods will be made available to the research community in a highly portable computer language.
