Spectral Domain Optical Coherence Tomography-Based Imaging Biomarkers and Hyperspectral Imaging

  • Surabhi Ruia
  • Sandeep Saxena


Biomarkers, are biochemical or imaging parameters, that objectively indicate the state of health of an individual. These are measurable by laboratory assay or medical imaging and are useful in prognosticating the disease and monitoring the effects of therapeutic interventions. Spectral domain optical coherence tomography has proved useful in identifying various imaging biomarkers in a range of ocular diseases. Hyperspectral imaging, a novel technology, collects information from across the electromagnetic spectrum for every pixel in an image. Hyperspectral imaging of the retina identifies materials or detects biochemical and metabolic processes within the retina. It provides a feasible method for measurement and analysis of vascular oxygen content in healthy and diseased retina.


Diabetic Retinopathy Retinal Nerve Fiber Layer Diabetic Macular Edema Hyperspectral Imaging Retinal Nerve Fiber Layer Thickness 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Andrade C, Beato J, Monteiro A et al (2016) Spectral-domain optical coherence tomography as a potential biomarker in Huntington’s disease. Mov Disord 31(3):377–383CrossRefPubMedGoogle Scholar
  2. Bittersohl D, Stemplewitz B, Keserü M et al (2015) Detection of retinal changes in idiopathic Parkinson’s disease using high-resolution optical coherence tomography and heidelberg retina tomography. Acta Ophthalmol 93:578–584CrossRefGoogle Scholar
  3. Cunha-Vaz J, Ribeiro L, Lobo C (2014) Phenotypes and biomarkers of diabetic retinopathy. Prog Retin Eye Res 41:90–111CrossRefPubMedGoogle Scholar
  4. Denniss J, Schiessl I, Nourrit V et al (2011) Relationships between visual field sensitivity and spectral absorption properties of the neuroretinal rim in glaucoma by multispectral imaging. Invest Ophthalmol Vis Sci 52:8732–8738CrossRefPubMedPubMedCentralGoogle Scholar
  5. Fawzi AA, Lee N, Acton JH et al (2011) Recovery of macular pigment spectrum in vivo using hyperspectral image analysis. J Biomed Opt 16:106008CrossRefPubMedPubMedCentralGoogle Scholar
  6. Folgar FA, Yuan EL, Sevilla MB et al, Study A.R.E.D. and Group S (2016) Drusen volume and retinal pigment epithelium abnormal thinning volume predict 2-year progression of age-related macular degeneration. Ophthalmology 123:39–50Google Scholar
  7. Godara P, Siebe C, Rha J et al (2010) Assessing the photoreceptor mosaic over drusen using adaptive optics and SD-OCT. Ophthalmic Surg Lasers Imaging Retina 41:104–108CrossRefGoogle Scholar
  8. Harvey AR, Lawlor J, McNaught AI et al (2002) Hyperspectral imaging for the detection of retinal disease. In: International symposium on optical science and technology. International Society for Optics and PhotonicsGoogle Scholar
  9. Jaime GRL, Kashani AH, Saati S et al (2012) Acute variations in retinal vascular oxygen content in a rabbit model of retinal venous occlusion. PLoS One 7:50179CrossRefGoogle Scholar
  10. Jain A, Saxena S, Khanna VK et al (2013) Status of serum VEGF and ICAM-1 and its association with external limiting membrane and inner segment-outer segment junction disruption in type 2 diabetes mellitus. Mol Vis 19:1760–1768PubMedPubMedCentralGoogle Scholar
  11. Kashani AH, Lopez Jaime GR, Saati S et al (2014) Noninvasive assessment of retinal vascular oxygen content among normal and diabetic human subjects: a study using hyperspectral computed tomographic imaging spectroscopy. Retina 34:1854–1860CrossRefPubMedPubMedCentralGoogle Scholar
  12. Koronyo Y, Salumbides BC, Black KL et al (2012) Alzheimer’s disease in the retina: imaging retinal aβ plaques for early diagnosis and therapy assessment. Neurodegener Dis 10:285–293CrossRefPubMedGoogle Scholar
  13. Liu GY, Utset TO, Bernard JT (2015) Retinal nerve fiber layer and macular thinning in systemic lupus erythematosus: an optical coherence tomography study comparing SLE and neuropsychiatric SLE. Lupus 24:1169–1176CrossRefPubMedPubMedCentralGoogle Scholar
  14. Outteryck O, Majed B, Defoort-Dhellemmes S et al (2015) A comparative optical coherence tomography study in neuromyelitis optica spectrum disorder and multiple sclerosis. Mult Scler J 21:1781–1793CrossRefGoogle Scholar
  15. Ruia S, Saxena S (2016) Targeted screening of macular edema by spectral domain optical coherence tomography for progression of diabetic retinopathy. Indian J Ocular Biol 1:102Google Scholar
  16. Ruia S, Saxena S, Prasad S et al (2016) Correlation of biomarkers thiobarbituric acid reactive substance, nitric oxide and central subfield and cube average thickness in diabetic retinopathy: a cross-sectional study. Int J Retina Vitreous 2:1CrossRefGoogle Scholar
  17. Sinha S, Saxena S, Das S et al (2016) Antimyeloperoxidase antibody is a biomarker for progression of diabetic retinopathy. Journal of diabetes and its complications 30:700–704Google Scholar
  18. de Sisternes L, Simon N, Tibshirani R et al (2014) Quantitative SD-OCT imaging biomarkers as indicators of age-related macular degeneration progression predicting AMD progression using SD-OCT features. Invest Ophthalmol Vis Sci 55:7093–7103CrossRefPubMedGoogle Scholar
  19. Smith TB, Parker M, Steinkamp PN et al (2016) Structure-function modeling of optical coherence tomography and standard automated perimetry in the retina of patients with autosomal dominant retinitis pigmentosa. PLoS One 11(2):e0148022CrossRefPubMedPubMedCentralGoogle Scholar
  20. Srivastav K, Saxena S, Mahdi AA et al (2015) Increased serum urea and creatinine levels correlate with decreased retinal nerve fibre layer thickness in diabetic retinopathy. Biomarkers 20:470–473CrossRefPubMedGoogle Scholar
  21. Strimbu K, Tavel JA (2010) What are biomarkers? Curr Opin HIV AIDS 5:463CrossRefPubMedPubMedCentralGoogle Scholar
  22. Sun JK, Lin MM, Lammer J et al (2014) Disorganization of the retinal inner layers as a predictor of visual acuity in eyes with center-involved diabetic macular edema. JAMA Ophthalmol 132:1309–1316CrossRefPubMedGoogle Scholar
  23. Uji A, Murakami T, Unoki N et al (2014a) Parallelism for quantitative image analysis of photoreceptor–retinal pigment epithelium complex alterations in diabetic macular edema parallelism in DME. Invest Ophthalmol Vis Sci 55:3361–3367CrossRefPubMedGoogle Scholar
  24. Uji A, Murakami T, Unoki N et al (2014b) Parallelism as a novel marker for structural integrity of retinal layers in optical coherence tomographic images in eyes with epiretinal membrane. Am J Ophthalmol 157:227–236CrossRefPubMedGoogle Scholar

Copyright information

© Springer India 2017

Authors and Affiliations

  1. 1.Department of OphthalmologyKing George’s Medical UniversityLucknowIndia

Personalised recommendations