Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration—method and clinical examples

Jørgensen, Thomas Martini; Thomadsen, J.; Christensen, Ulrik; Soliman, Wael; Sander, Birgit

doi:10.1117/1.2772879

Cited by 82 publications

(66 citation statements)

References 26 publications

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“…Despite this rich history, the development of registration techniques in OCT has been relatively limited. Existing works are restricted almost exclusively to rigid registrations [23][24][25][26][27][28], where only rigid body transformations are allowed in the alignment. Such transformation models are generally inadequate for the retina, since the differences between retinal layers from different subjects (or even the same subject over time) cannot be fully represented by such a low-dimensional model.…”

Section: Introductionmentioning

confidence: 99%

Analysis of macular OCT images using deformable registration

Chen¹,

Lang²,

Ying³

et al. 2014

Biomed. Opt. Express

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Optical coherence tomography (OCT) of the macula has become increasingly important in the investigation of retinal pathology. However, deformable image registration, which is used for aligning subjects for pairwise comparisons, population averaging, and atlas label transfer, has not been well-developed and demonstrated on OCT images. In this paper, we present a deformable image registration approach designed specifically for macular OCT images. The approach begins with an initial translation to align the fovea of each subject, followed by a linear rescaling to align the top and bottom retinal boundaries. Finally, the layers within the retina are aligned by a deformable registration using one-dimensional radial basis functions. The algorithm was validated using manual delineations of retinal layers in OCT images from a cohort consisting of healthy controls and patients diagnosed with multiple sclerosis (MS). We show that the algorithm overcomes the shortcomings of existing generic registration methods, which cannot be readily applied to OCT images. A successful deformable image registration algorithm for macular OCT opens up a variety of population based analysis techniques that are regularly used in other imaging modalities, such as spatial normalization, statistical atlas creation, and voxel based morphometry. Examples of these applications are provided to demonstrate the potential benefits such techniques can have on our understanding of retinal disease. In particular, included is a pilot study of localized volumetric changes between healthy controls and MS patients using the proposed registration algorithm.

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Section: Introductionmentioning

confidence: 99%

Analysis of macular OCT images using deformable registration

Chen¹,

Lang²,

Ying³

et al. 2014

Biomed. Opt. Express

View full text Add to dashboard Cite

show abstract

“…Numerous speckle reduction methods have therefore been developed and applied to OCT [54]. Some are based on incoherent addition of several signals from the same location under varying conditions, e. g. angular compounding [57], spatial compounding [58,59], and frequency compounding [60]. Where compounding is not feasible different image processing techniques can be applied to suppress speckle, e. g. various types of smoothing filters, deconvolution [61,62], wavelet analysis [63], rotating kernel transformation [64], and anisotropic diffusion [65].…”

Section: Optical Properties Of the Samplementioning

confidence: 99%

Optical coherence tomography—current technology and applications in clinical and biomedical research

et al. 2011

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Optical coherence tomography (OCT) is a non-invasive imaging technique providing real-time twoand three-dimensional images of scattering samples with micrometer resolution. Mapping the local reflectivity, OCT visualizes the morphology of the sample. In addition, functional properties such as birefringence, motion or the distribution of certain substances can be detected with high spatial resolution. The main field of application is bio-medical imaging and diagnostics. In ophthalmology, OCT is accepted as a clinical standard for diagnosing and monitoring treatment of a number of retinal diseases, and OCT is becoming an important instrument for clinical cardiology. New applications are emerging in various medical fields, e. g. early-stage cancer detection, surgical guidance, and early diagnosis of musculoskeletal diseases. OCT has also proven its value as a tool for developmental biology. The number of companies involved in manufacturing OCT systems has increased substantially during the last years-especially due to its success in opthalmology-and this technology can be expected to continue to spread into various fields of application.

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“…To overcome this issue, in one previous OCT study, a regularized dynamic programming algorithm was first utilized to align a set of OCT images, which were then averaged together to form the final OCT image. Applying this method increased both the SNR and the Contrast to Noise Ratio (CNR) of the processed OCT images [25].…”

Section: Introductionmentioning

confidence: 99%

Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images

Boroomand

Wong

et al. 2013

Biomed. Opt. Express

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Improving the spatial resolution of Optical Coherence Tomography (OCT) images is important for the visualization and analysis of small morphological features in biological tissue such as blood vessels, membranes, cellular layers, etc. In this paper, we propose a novel reconstruction approach to obtaining super-resolved OCT tomograms from multiple lower resolution images. The proposed Multi-Penalty Conditional Random Field (MPCRF) method combines four different penalty factors (spatial proximity, first and second order intensity variations, as well as a spline-based smoothness of fit) into the prior model within a Maximum A Posteriori (MAP) estimation framework. Test carried out in retinal OCT images illustrate the effectiveness of the proposed MPCRF reconstruction approach in terms of spatial resolution enhancement, as compared to previously published super resolved image reconstruction methods. Visual assessment of the MPCRF results demonstrate the potential of this method in better preservation of fine details and structures of the imaged sample, as well as retaining the sharpness of biological tissue boundaries while reducing the effects of speckle noise inherent to OCT. Quantitative evaluation using imaging metrics such as Signal-to-Noise Ratio (SNR), Contrast to Noise Ratio (CNR), Equivalent Number of Looks (ENL), and Edge Preservation Parameter show significant visual quality improvement with the MPCRF approach. Therefore, the proposed MPCRF reconstruction approach is an effective tool for enhancing the spatial resolution of OCT images without the necessity for significant imaging hardware modifications.

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Enhancing the signal-to-noise ratio in ophthalmic optical coherence tomography by image registration—method and clinical examples

Cited by 82 publications

References 26 publications

Analysis of macular OCT images using deformable registration

Analysis of macular OCT images using deformable registration

Optical coherence tomography—current technology and applications in clinical and biomedical research

Multi-penalty conditional random field approach to super-resolved reconstruction of optical coherence tomography images

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