GPU-based computational adaptive optics for volumetric optical coherence microscopy

Tang, Han; Mulligan, Jeffrey A.; Untracht, Gavrielle R.; Zhang, Xihao; Adie, Steven G.

doi:10.1117/12.2213949

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…The increased computing power from GPUs enables some of the hardware design compromises to be offset through computational techniques. 44 , 45 For example, Tang and colleagues 46 employed three GPU units for real-time computational adaptive optics (AO) system, and recently Maloca and colleagues 47 employed GPUs for volumetric OCT in virtual reality environment for enhanced visualization in medical education.…”

Section: Embedded Ophthalmic Devicesmentioning

confidence: 99%

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

et al. 2019

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Deep learning has recently gained high interest in ophthalmology due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for ‘active acquisition’–embedded deep learning, leading to as high-quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning–based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog, and cloud layers, the former being performed at a device level. Improved egde-layer performance via ‘active acquisition’ serves as an automatic data curation operator translating to better quality data in electronic health records, as well as on the cloud layer, for improved deep learning–based clinical data mining.

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Section: Embedded Ophthalmic Devicesmentioning

confidence: 99%

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

et al. 2019

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“…Additionally, full integration of HAO and CAO for clinical use would require a near real-time implementation of the automated CAO optimization procedure. Manual CAO correction has previously been implemented for real-time display using a graphics processing unit [50]. It is anticipated that parallelization will enable optimization of multiple depth layers simultaneously to produce HAO + CAO images at an acceptable speed for clinical use.…”

Section: Conclusiomentioning

confidence: 99%

Combined hardware and computational optical wavefront correction

South

Kurokawa

Liu

et al. 2018

Biomed. Opt. Express

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In many optical imaging applications, it is necessary to overcome aberrations to obtain high-resolution images. Aberration correction can be performed by either physically modifying the optical wavefront using hardware components, or by modifying the wavefront during image reconstruction using computational imaging. Here we address a longstanding issue in computational imaging: photons that are not collected cannot be corrected. This severely restricts the applications of computational wavefront correction. Additionally, performance limitations of hardware wavefront correction leave many aberrations uncorrected. We combine hardware and computational correction to address the shortcomings of each method. Coherent optical backscattering data is collected using high-speed optical coherence tomography, with aberrations corrected at the time of acquisition using a wavefront sensor and deformable mirror to maximize photon collection. Remaining aberrations are corrected by digitally modifying the coherently-measured wavefront during imaging reconstruction. This strategy obtains high-resolution images with improved signal-to-noise ratio of human photoreceptor cells with more complete correction of ocular aberrations, and increased flexibility to image at multiple retinal depths, field locations, and time points. While our approach is not restricted to retinal imaging, this application is one of the most challenging for computational imaging due to the large aberrations of the dilated pupil, time-varying aberrations, and unavoidable eye motion. In contrast with previous computational imaging work, we have imaged single photoreceptors and their waveguide modes in fully dilated eyes with a single acquisition. Combined hardware and computational wavefront correction improves the image sharpness of existing adaptive optics systems, and broadens the potential applications of computational imaging methods.

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“…This approach was realized by manually adjusting the Zernike coefficients when CAO was first developed [17,119]. GPU-based implementation has also been achieved [120]. Recently, several iterative optimization algorithms have been independently proposed by Yang et al [121], Pande and Liu et al [122], and Hillmann et al [52], for automatic aberration correction.…”

Section: Implementation Of Caomentioning

confidence: 99%

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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GPU-based computational adaptive optics for volumetric optical coherence microscopy

Cited by 4 publications

References 20 publications

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

Embedded deep learning in ophthalmology: making ophthalmic imaging smarter

Combined hardware and computational optical wavefront correction

Computational optical coherence tomography [Invited]

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