Three-dimensional motion correction using speckle and phase for in vivo computed optical interferometric tomography

Shemonski, Nathan D.; Ahn, Shawn S.; Liu, Yuan Zhi; South, Fredrick A.; Carney, P. Scott; Boppart, Stephen A.

doi:10.1364/boe.5.004131

Cited by 37 publications

(17 citation statements)

References 23 publications

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“…When imaging with an aberrated beam, the acquired image is blurred and does not have features at the scale required to correct the transverse motion. However, using an additional speckle-tracking imaging sub-system, the transverse motion can be measured [145].…”

Section: Hardware Methods For Phase Stabilizationmentioning

confidence: 99%

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Computational optical coherence tomography [Invited]

Liu

South

et al. 2017

Biomed. Opt. Express

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Optical coherence tomography (OCT) has become an important imaging modality with numerous biomedical applications. Challenges in high-speed, high-resolution, volumetric OCT imaging include managing dispersion, the trade-off between transverse resolution and depth-of-field, and correcting optical aberrations that are present in both the system and sample. Physics-based computational imaging techniques have proven to provide solutions to these limitations. This review aims to outline these computational imaging techniques within a general mathematical framework, summarize the historical progress, highlight the state-of-the-art achievements, and discuss the present challenges. Swanson, "Optical biopsy and imaging using optical coherence tomography," Nat. Med. 1(9), 970-972 (1995). 3. G. J. Tearney, M. E. Brezinski, B. E. Bouma, S. A. Boppart, C. Pitris, J. F. Southern, and J. G. Fujimoto, "In vivo endoscopic optical biopsy with optical coherence tomography," Science 276(5321), 2037-2039 (1997). 4. S. A. Boppart, B. E. Bouma, C. Pitris, J. F. Southern, M. E. Brezinski, and J. G. Fujimoto, "In vivo cellular optical coherence tomography imaging," Nat. Med. 4(7), 861-865 (1998). Sundaram, P. S. Ray, and S. A. Boppart, "Real-time imaging of the resection bed using a handheld probe to reduce incidence of microscopic positive margins in cancer surgery," Cancer Res. 75(18), 3706-3712 (2015). 9. J. G. Fujimoto and E. A. Swanson, "The development, commercialization, and impact of optical coherence tomography," Invest. Ophthalmol. Vis. Sci. 57(9), OCT1-OCT13 (2016). 10. A. M. Cormack, "Representation of a function by its line integrals, with some radiological applications. II," J.Appl. Phys. 35(10), 2722-2727 (1964). 11. G. N. Hounsfield, "Computerized transverse axial scanning (tomography). 1. Description of system," Br. J.Radiol. 46(552), 1016-1022 (1973). 12. P. C. Lauterbur, "Image formation by induced local interactions: examples employing nuclear magnetic resonance," Nature 242(5394), 190-191 (1973). 13. P. T. Gough and D. W. Hawkins, "Unified framework for modern synthetic aperture imaging algorithms," Int. J.Imaging Syst. Technol. 8(4), 343-358 (1997). shaping for optimal depth-selective focusing in optical coherence tomography," Opt. Express 21(3), 2890-2902 (2013 111-115 (1962). 77. L. Cutrona, E. Leith, C. Palermo, and L. Porcello, "Optical data processing and filtering systems," IRE Trans.Inf. Theory 6(3), 386-400 (1960). 78. L. J. Cutrona, E. N. Leith, L. J. Porcello, and E. W. Vivian, "On the application of coherent optical processing techniques to synthetic-aperture radar," Proc. IEEE 54(8), 1026-1032 (1966). 79. W. Brown and L. Porcello, "An introduction to synthetic-aperture radar," IEEE Spectr. 6(9), 52-62 (1969). 80. M. P. Hayes and P. T. Gough, "Broad-band synthetic aperture sonar," IEEE J. Oceanic Eng. 17(1), 80-94 (1992)

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Section: Hardware Methods For Phase Stabilizationmentioning

confidence: 99%

“…Alternatively, additional hardware can be added to track transverse motion [145]. The following processing techniques can then be used to remove residual axial motion.…”

Section: Post-processing Methods For Phase Stabilizationmentioning

confidence: 99%

Computational optical coherence tomography [Invited]

Liu

South

et al. 2017

Biomed. Opt. Express

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“…Like other phase-sensitive imaging techniques, including CAO, a certain level of phase stability is required for the reconstruction to succeed [40]. To ensure phase stable data, it is necessary to both measure and correct the phase instabilities [41, 42]. …”

Section: Interferometric Synthetic Aperture Microscopymentioning

confidence: 99%

“…The steps for measurement and correction of axial motion are described in Table 2 (see Ref. [42] for a detailed discussion of transverse motion correction.) Calculation of an en face axial motion phase map A ( x, y ) is used to negate the axial motion through complex conjugate multiplication with the complex data S ( x, y, z ).…”

Section: Interferometric Synthetic Aperture Microscopymentioning

confidence: 99%

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Computed Optical Interferometric Imaging: Methods, Achievements, and Challenges

South¹,

Liu²,

Carney³

et al. 2016

IEEE J. Select. Topics Quantum Electron.

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Three-dimensional high-resolution optical imaging systems are generally restricted by the trade-off between resolution and depth-of-field as well as imperfections in the imaging system or sample. Computed optical interferometric imaging is able to overcome these longstanding limitations using methods such as interferometric synthetic aperture microscopy (ISAM) and computational adaptive optics (CAO) which manipulate the complex interferometric data. These techniques correct for limited depth-of-field and optical aberrations without the need for additional hardware. This paper aims to outline these computational methods, making them readily available to the research community. Achievements of the techniques will be highlighted, along with past and present challenges in implementing the techniques. Challenges such as phase instability and determination of the appropriate aberration correction have been largely overcome so that imaging of living tissues using ISAM and CAO is now possible. Computed imaging in optics is becoming a mature technology poised to make a significant impact in medicine and biology.

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Non‐invasive optical assessment of viscosity of middle ear effusions in otitis media

Monroy

Pande²,

Shelton

et al. 2016

Journal of Biophotonics

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Eustachian tube dysfunction can cause fluid to collect within the middle ear cavity and form a middle ear effusion (MEE). MEEs can persist for weeks or months and cause hearing loss as well as speech and learning delays in young children. The ability of a physician to accurately identify and characterize the middle ear for signs of fluid and/or infection is crucial to provide the most appropriate treatment for the patient. Currently, middle ear infections are assessed with otoscopy, which provides limited and only qualitative diagnostic information. In this study, we propose a method utilizing cross-sectional depth-resolved optical coherence tomography to noninvasively measure the diffusion coefficient and viscosity of colloid suspensions, such as a MEE. Experimental validation of the proposed technique on simulated MEE phantoms with varying viscosity and particulate characteristics is presented, along with some preliminary results from in vivo and ex vivo samples of human MEEs.

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Three-dimensional motion correction using speckle and phase for in vivo computed optical interferometric tomography

Cited by 37 publications

References 23 publications

Computational optical coherence tomography [Invited]

Computational optical coherence tomography [Invited]

Computed Optical Interferometric Imaging: Methods, Achievements, and Challenges

Non‐invasive optical assessment of viscosity of middle ear effusions in otitis media

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