Model-based iterative reconstruction for spectral-domain optical coherence tomography
| Autor: | Jonathan H. Mason, Pierre Bagnaninchi, Yvonne Reinwald, Sarah L. Waters, Ying Yang, Alicia J. El Haj |
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| Rok vydání: | 2019 |
| Předmět: |
Synthetic aperture radar
Cross-correlation medicine.diagnostic_test Computer science Image quality Acoustics Image and Video Processing (eess.IV) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Electrical Engineering and Systems Science - Image and Video Processing Displacement (vector) Displacement mapping Interferometry Optical coherence tomography FOS: Electrical engineering electronic engineering information engineering medicine Elastography |
| Zdroj: | Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI. |
| DOI: | 10.1117/12.2509424 |
| Popis: | Spectral domain optical coherence tomography (OCT) offers high resolution multidimensional imaging, but generally suffers from defocussing, intensity falloff and shot noise, causing artifacts and image degradation along the imaging depth. In this work, we develop an iterative statistical reconstruction technique, based upon the interferometric synthetic aperture microscopy (ISAM) model with additive noise, to actively compensate for these effects. For the ISAM re-sampling, we use a non uniform FFT with Kaiser-Bessel interpolation, offering efficiency and high accuracy. We then employ an accelerated gradient descent based algorithm, to minimize the negative log-likelihood of the model, and include spatial or wavelet sparsity based penalty functions, to provide appropriate regularization for given image structures. We evaluate our approach with titanium oxide micro-bead and cucumber samples with a commercial spectral domain OCT system, under various subsampling regimes, and demonstrate superior image quality over traditional reconstruction and ISAM methods. 10 pages, 3 figures, presented at SPIE BiOS 2019 |
| Databáze: | OpenAIRE |
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