Fast Acquisition and Reconstruction of Optical Coherence Tomography Images via Sparse Representation

• Published in: IEEE transactions on medical imaging Vol. 32; no. 11; pp. 2034 - 2049
• Main Authors: Leyuan Fang, Shutao Li, McNabb, Ryan P., Qing Nie, Kuo, Anthony N., Toth, Cynthia A., Izatt, Joseph A., Farsiu, Sina
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2013; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Animals; Dictionaries; Fast retina scanning; Humans; image enhancement; Image Processing, Computer-Assisted - methods; Image reconstruction; Image resolution; Imaging; Interpolation; Macular Degeneration - pathology; Mice; Noise reduction; Optic Nerve - anatomy & histology; Optical Coherence Tomography; Reconstruction; Representations; Retina - anatomy & histology; Retina - pathology; simultaneous denoising and interpolation; sparse representation; Sparsity; Tomography; Tomography, Optical Coherence - methods; Training
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2013.2271904

In this paper, we present a novel technique, based on compressive sensing principles, for reconstruction and enhancement of multi-dimensional image data. Our method is a major improvement and generalization of the multi-scale sparsity based tomographic denoising (MSBTD) algorithm we recently introduced for reducing speckle noise. Our new technique exhibits several advantages over MSBTD, including its capability to simultaneously reduce noise and interpolate missing data. Unlike MSBTD, our new method does not require an a priori high-quality image from the target imaging subject and thus offers the potential to shorten clinical imaging sessions. This novel image restoration method, which we termed sparsity based simultaneous denoising and interpolation (SBSDI), utilizes sparse representation dictionaries constructed from previously collected datasets. We tested the SBSDI algorithm on retinal spectral domain optical coherence tomography images captured in the clinic. Experiments showed that the SBSDI algorithm qualitatively and quantitatively outperforms other state-of-the-art methods.