Aberration correction by maximizing generalized sharpness metrics

Fienup, James R.; Miller, Jason J.

doi:10.1364/josaa.20.000609

Cited by 264 publications

(164 citation statements)

References 9 publications

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“…The quality of each image was analyzed in order to select the sharpest image in the sequence, which was used as a reference image during registration of the images. The sharpness metric, described by Fienup & Miller [34], was used as a quality metric to measure the image sharpness.…”

Section: Methodsmentioning

confidence: 99%

Registration of adaptive optics corrected retinal nerve fiber layer (RNFL) images

Ramaswamy

Lombardo

Devaney

2014

Biomed. Opt. Express

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Glaucoma is the leading cause of preventable blindness in the western world. Investigation of high-resolution retinal nerve fiber layer (RNFL) images in patients may lead to new indicators of its onset. Adaptive optics (AO) can provide diffraction-limited images of the retina, providing new opportunities for earlier detection of neuroretinal pathologies. However, precise processing is required to correct for three effects in sequences of AO-assisted, flood-illumination images: uneven illumination, residual image motion and image rotation. This processing can be challenging for images of the RNFL due to their low contrast and lack of clearly noticeable features. Here we develop specific processing techniques and show that their application leads to improved image quality on the nerve fiber bundles. This in turn improves the reliability of measures of fiber texture such as the correlation of Gray-Level Co-occurrence Matrix (GLCM).

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Section: Methodsmentioning

confidence: 99%

Registration of adaptive optics corrected retinal nerve fiber layer (RNFL) images

Ramaswamy

Lombardo

Devaney

2014

Biomed. Opt. Express

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“…If desired, the image metric can be tuned to the expected image features [125]. The sub-aperture technique can be used to quickly calculate defocus in heterogeneous samples using only two sub-apertures [72].…”

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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“…The potential applications of QPCCM in industrial inspection and biomedical imaging are believed to be immediate. Since each line scan records all the information of one slice of the object including the aberrations of the system, it opens the avenues for a variety of numerical aberration compensation methods and development of a full digital adaptive optics system for biomedical imaging especially ophthalmic imaging [14,15,18,19].…”

Section: Introductionmentioning

confidence: 99%

Quantitative phase-contrast confocal microscope

Liu¹,

Marchesini²,

Kim³

2014

Opt. Express

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We present a quantitative phase-contrast confocal microscope (QPCCM) by combining a line-scanning confocal system with digital holography (DH). This combination can merge the merits of these two different imaging modalities. High-contrast intensity images with low coherent noise, and the optical sectioning capability are made available due to the confocality. Phase profiles of the samples become accessible thanks to DH. QPCCM is able to quantitatively measure the phase variations of optical sections of the opaque samples and has the potential to take highquality intensity and phase images of non-opaque samples such as many biological samples. Because each line scan is recorded by a hologram that may contain the optical aberrations of the system, it opens avenues for a variety of numerical aberration compensation methods and development of full digital adaptive optics confocal system to emulate current hardwarebased adaptive optics system for biomedical imaging, especially ophthalmic imaging. Preliminary experiments with a microscope objective of NA 0.65 and 40 × on opaque samples are presented to demonstrate this idea. The measured lateral and axial resolutions of the intensity images from the current system are ~0.64μm and ~2.70μm respectively. The noise level of the phase profile by QPCCM is ~2.4nm which is better than the result by DH.

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Aberration correction by maximizing generalized sharpness metrics

Cited by 264 publications

References 9 publications

Registration of adaptive optics corrected retinal nerve fiber layer (RNFL) images

Registration of adaptive optics corrected retinal nerve fiber layer (RNFL) images

Computational optical coherence tomography [Invited]

Quantitative phase-contrast confocal microscope

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