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Introduction to the Feature Issue on Adaptive Optics for Biomedical Applications

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The guest editors introduce a feature issue commemorating the 25th anniversary of adaptive optics in biomedical research.

© 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement

Adaptive optics (AO) has enabled unprecedented access to the basic building blocks of biology and medicine. Borrowing ideas and approaches from astronomy to measure and correct for optical aberrations, AO was first demonstrated in living biological tissue – the human eye – 25 years ago [1]. Since then, the use of AO in biomedical imaging has grown and expanded to applications in ophthalmology, vision science, microscopy, and neuroscience. This was made possible by technology development targeted for these specific application areas and fueled by a growing need to examine biological structures and processes at cellular and sub-cellular scales in living tissue.

AO in ophthalmology and vision science began in earnest with the first visualization of the human photoreceptor mosaic in vivo in a flood illumination retinal camera equipped with AO at the University of Rochester in 1997 [1]. Unlike microscopy, where scientists can choose an objective suited to the demands of field-of-view and resolution for their application, the focusing objective for most ophthalmic imagers is limited to the ocular cornea and lens of the eye. Although amazing enough in its beautiful complexity to give Darwin [2] pause, the eye is flawed from the standpoint of optical performance. Typically, ocular optics are well-suited in their characteristics for perceiving the external world under normal daylight conditions, but they fall short in serving as an effective imaging objective due to monochromatic and chromatic aberrations. This was noted by Helmholtz [3] as early as the 1800s when he developed an ophthalmoscope to peer into the eye, and earlier by others. Researchers have since sought methods to improve access to the retina and the visual system, unobscured by the eye’s optical aberrations. As is the case with numerous major breakthroughs, the solution was found serendipitously far afield, in astronomy [4]. It came with the realization that imaging stars and galaxies with ground-based telescopes through atmospheric turbulence posed an impediment with close parallels to those posed by the eye’s optics. In astronomy, a laser guide star is used to measure the optical aberrations introduced by the turbulence caused by the Earth’s atmosphere, and a deformable mirror is used to compensate for it, together yielding near-diffraction limited imaging of astronomical objects. The same principle was followed in AO ophthalmoscopy, wherein an imaging device could dynamically correct ocular aberrations in the anterior segment in order to visualize and stimulate the posterior segment with cellular resolution. This Biomedical Optics Express feature issue marks the 25th anniversary of this revolutionary translation of AO technology from astronomy to biomedical research.

The integration of AO into an ophthalmoscope essentially transforms the eyeball into a microscope objective. This enabled the translation of a plethora of extant ex vivo microscopy techniques to study the visual system in living organisms. The incorporation of AO into the Scanning Laser Ophthalmoscope (SLO) in 2002 [5] was one such seminal advance that catalyzed development of a whole family of ophthalmic techniques based in confocal microscopy. These included fluorescence [69](auto-, single-photon, and multi-photon), bright/dark-field microscopy [10], and phase contrast microscopy [1114]. A few years later, AO was integrated into optical coherence tomography (OCT) [1517], providing for the first time isotropic 3D resolution at a cellular scale [17,18]. In parallel, AO was also employed, not just to visualize, but also to stimulate the visual system with the desired optical characteristics [19,20]. Such vision simulation enabled the capacity to understand the fine interplay between the optics of the eye, custom vision corrective devices, and visual perception [2123]. Building on these seminal advances over the last two and a half decades, it is not an overstatement to say that AO has fundamentally changed the way we study the visual system in living organisms. To summarize its impact, the feature issue contains invited reviews that offer recent highlights and historical perspectives on the field of AO in biomedical research [24]. These include invited reviews summarizing advances of AO applied to microscopy [25], vision simulation [26], and clinical ophthalmology [27].

Whereas the wavefront sensing scheme originally developed for astronomy works well for the transparent ocular optics in ophthalmology, when AO is applied to microscopic imaging of opaque tissues, new methodology development, including wavefront sensorless algorithms, has expanded the toolset of aberration measurement. AO has been combined with a variety of microscopy modalities, which is reviewed in the invited article by Zhang et al [25].

The issue includes work on photoreceptor structure and function, specifically identification of their number and density, their spectral types and quantification of their topography; by Zhuo et al. [28] & Li et al. [29] on the development of deep learning approaches for identifying cones, by Wynne et al. [30] on intergrader agreement for cone identification, by Heitkotter et al [31] on spacing-derived estimates of rod density, by Bedggood et al. [32] on identifying cone spectral types via ultrafast densitometry, and by Pandiyan et al. [33] on cone spectral classification via optoretinography and its comparison to densitometry. The issue also has articles that establish normative standards & techniques for AO derived retinal biomarkers, aimed towards future clinical translation. These include calculation of the retinal magnification by Huang et al. using paraxial and non-paraxial ray tracing methods that incorporate individualized anterior eye parameters [34], the within and between session reliability and reciprocity of the cone optoretinogram in an AOSLO [35], normative characterization of the optoretinogram over an extended field using line-scan OCT [36] and an automatic processing and analysis pipeline for AO flood illumination retinal images [37]. AO multimodal imaging systems are described that provide new and complementary views of the retina. These include incorporating AO with OCT and eye-tracking described by Mozaffari et al. [38], using an ultrafast OCT engine by Liu et al. [39] and combining OCT & SLO with AO for mouse retinal imaging by Zhang et al. [40]. The use of AO for testing vision under optically manipulated conditions is demonstrated by a few articles; Lago et al. [41] study the effect of ocular aberrations on the simulated performance of a new refractive intraocular lens design and Vedhakrishnan et al. [42] evaluate the interaction of multifocal patterns with the eye’s accommodative response. The neural limits to visual performance are assessed upon bypassing the ocular optics in pseudophakic eyes by Bang et al. [43], and in the peripheral visual field by Jaisankar et al. [44]. The application of AO to enhance light sheet microscopy is described by Malivert et al. [45] and Liu et al [46] and for depth scanning in photoacoustic remote sensing microscopy by Mukhangaliyeva et al. [47]. The use of computational AO for enhancing image quality in digital holographic fluorescence microscopy is shown by Zhang et al. [48]. The breadth of articles covered in this feature issue are representative of the recent and ongoing advances in the area of AO for biomedical applications.

While AO has advanced our understanding and ability to assess biological tissue, there still remain challenges, many of which are common to both vision science and microscopy. For example, AO has not yet achieved clinical translation and widespread use, despite the best effort of commercial partners. For comparison, while OCT was invented in the late 1980s and demonstrated in earnest in 1991, clinical demonstration driving device commercialization took less than 10 years. However, AO has remained a tool suited primarily for research purposes. Part of this has to do with the fact that AO is an enabling technology that can augment imaging modalities rather than serving as a primary imaging modality. Other factors include component cost, system complexity, typically small operational fields-of-view that hinder global assays, lack of performance assessment tools, and lack of validated AO-based biomarkers that can be used as clinical endpoints. Despite these challenges, the review article by Morgan et al. [27] clearly demonstrates the power of AO when applied to studying diseases in the clinic. Importantly, with many cell-based therapies on the horizon for treating diseases, AO remains critical as a tool to assess their safety, viability and efficacy with cellular resolution.

It is worth speculating what is in store for the future of AO in biomedicine. Progress in AO technology development, and AO applications for basic science and clinical research will go hand-in-hand as they have in the past, and continue to propel each other forward. Given the remarkable advances to-date, we can be confident that the aforementioned challenges to widespread use of AO will be resolved and the technology will become widely accessible to labs and clinics, beyond those with specialized AO expertise. The human subject groups and animal models that benefit from AO studies will expand in their depth and breadth. Together, this will enable highly interdisciplinary studies and cross-pollination of research expertise and personnel across labs and clinical centers. The standardization and validation of acquired data from AO devices will capitalize on the incipient era of big data and artificial intelligence, leading to an expansion of validated biomarkers in normal and diseased states that may serve as cellular-scale clinical trial outcome measures for new treatments. The increasing computational power will be leveraged to reduce the complexity of instruments by complementing hardware AO with digital aberration correction strategies. Large AO datasets will be used for generation and testing of new hypotheses linking structure and function. The area of robotics and autonomous devices will bear on the miniaturization and operator-free control of AO devices offering opportunities for telemedicine health assessment. While the past 25 years revealed the structure of various cell types in the live retina, the next 25 may be dedicated to understanding the personalized function and physiology of these cells and how they interact within retinal circuits to mediate vision. Herein, one may expect a comprehensive mapping of how light inputs to the visual system are transformed by each intermediary stage of processing; from the eye’s optics, the retina, to the cortex up to behavior. This knowledge and AO tools in general will be harnessed in applications that advance retinal cell replacement therapies as a treatment for age-related macular degeneration and other debilitating eye diseases. As an extension of the brain, the retina will provide a critical window into neurological disorders such as Alzheimer’s disease, and high-resolution AO imaging will play a key role in its cellular phenotyping. The area of AO vision simulation may merge with the expanding area of research in augmented and virtual reality devices. These devices will find application in myopia progression and control, to improve surgical treatment outcomes, to devise custom vision correction strategies for corneal disease, keratoconus, and presbyopia, and for visual training post-treatment. In essence, AO will become an essential tool in the arsenal of scientists and clinicians involved in the fine-grained assessment of the visual and nervous systems in vivo. We recognize that progress in many of these aforementioned areas are well underway and we will most likely end up severely myopic in our forecast if the trajectory of progress over the past 25 years is any indication for the next 25 years.

Finally, we wish to thank the review paper authors, contributing authors, and manuscript reviewers, without whose diligence this issue would not have been possible. We are proud to work in a field full of so many people who have devoted their intelligence, creativity, and expansive erudition throughout much of their careers to advance the field of AO.


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12. D. Scoles, Y. N. Sulai, C. S. Langlo, G. A. Fishman, C. A. Curcio, J. Carroll, and A. Dubra, “In vivo imaging of human cone photoreceptor inner segments,” Invest. Ophthalmol. Visual Sci. 55(7), 4244–4251 (2014). [CrossRef]  

13. A. Guevara-Torres, A. Joseph, and J. Schallek, “Label free measurement of retinal blood cell flux, velocity, hematocrit and capillary width in the living mouse eye,” Biomed. Opt. Express 7(10), 4228–4249 (2016). [CrossRef]  

14. E. A. Rossi, C. E. Granger, R. Sharma, Q. Yang, K. Saito, C. Schwarz, S. Walters, K. Nozato, J. Zhang, and T. Kawakami, “Imaging individual neurons in the retinal ganglion cell layer of the living eye,” Proc. Natl. Acad. Sci. 114(3), 586–591 (2017). [CrossRef]  

15. Y. Zhang, J. Rha, R. S. Jonnal, and D. T. Miller, “Adaptive optics parallel spectral domain optical coherence tomography for imaging the living retina,” Opt. Express 13(12), 4792–4811 (2005). [CrossRef]  

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17. Y. Zhang, B. Cense, J. Rha, R. S. Jonnal, W. Gao, R. J. Zawadzki, J. S. Werner, S. Jones, S. Olivier, and D. T. Miller, “High-speed volumetric imaging of cone photoreceptors with adaptive optics spectral-domain optical coherence tomography,” Opt. Express 14(10), 4380–4394 (2006). [CrossRef]  

18. Z. Liu, K. Kurokawa, F. Zhang, J. J. Lee, and D. T. Miller, “Imaging and quantifying ganglion cells and other transparent neurons in the living human retina,” Proc. Natl. Acad. Sci. 114(48), 12803–12808 (2017). [CrossRef]  

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20. P. A. Piers, E. J. Fernandez, S. Manzanera, S. Norrby, and P. Artal, “Adaptive optics simulation of intraocular lenses with modified spherical aberration,” Invest. Ophthalmol. Visual Sci. 45(12), 4601–4610 (2004). [CrossRef]  

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22. S. Marcos, L. Sawides, E. Gambra, and C. Dorronsoro, “Influence of adaptive-optics ocular aberration correction on visual acuity at different luminances and contrast polarities,” J. Vis. 8(13), 1 (2008). [CrossRef]  

23. R. Sabesan and G. Yoon, “Visual performance after correcting higher order aberrations in keratoconic eyes,” J. Vis. 9(5), 6 (2009). [CrossRef]  

24. D. R Williams, S. A. Burns, D. T. Miller, and A. Roorda, “Evolution of adaptive optics retinal imaging [Invited],” Biomed. Opt. Express 14(3), 1307–1338 (2023). [CrossRef]  

25. Q. Zhang, Q. Hu, C. Berlage, P. Kner, B. Judkawitz, M. Booth, and N. Ji, “Adaptive optics for optical microscopy [Invited],” Biomed. Opt. Express 14(4), 1732–1756 (2023). [CrossRef]  

26. S. Marcos, P. Artal, D. A. Atchison, K. Hampson, R. Legras, L. Lundström, and G. Yoon, “Adaptive optics visual simulators: a review of recent optical designs and applications [Invited],” Biomed. Opt. Express 13(12), 6508–6532 (2022). [CrossRef]  

27. J. I. Morgan, T. Y. Chui, and K. Grieve, “Twenty-five years of clinical applications using adaptive optics ophthalmoscopy [Invited],” Biomed. Opt. Express 14(1), 387–428 (2023). [CrossRef]  

28. M. Zhou, N. Doble, S. S. Choi, T. Jin, C. Xu, S. Parthasarathy, and R. Ramnath, “Using deep learning for the automated identification of cone and rod photoreceptors from adaptive optics imaging of the human retina,” Biomed. Opt. Express 13(10), 5082–5097 (2022). [CrossRef]  

29. K. Li, Q. Yin, J. Ren, H. Song, and J. Zhang, “Automatic quantification of cone photoreceptors in adaptive optics scanning light ophthalmoscope images using multi-task learning,” Biomed. Opt. Express 13(10), 5187–5201 (2022). [CrossRef]  

30. N. Wynne, J. A. Cava, M. Gaffney, H. Heitkotter, A. Scheidt, J. L. Reiniger, J. Grieshop, K. Yang, W. M. Harmening, R. F. Cooper, and J. Carroll, “Intergrader agreement of foveal cone topography measured using adaptive optics scanning light ophthalmoscopy,” Biomed. Opt. Express 13(8), 4445–4454 (2022). [CrossRef]  

31. H. Heitkotter, E. J. Patterson, E. N. Woertz, J. A. Cava, M. Gaffney, I. Adhan, J. Tam, R. F. Cooper, and J. Carroll, “Extracting spacing-derived estimates of rod density in healthy retinae,” Biomed. Opt. Express 14(1), 1–17 (2023). [CrossRef]  

32. P. Bedggood, A. C. Britten-Jones, L. N. Ayton, and A. Metha, “Assessment of photoreceptor function with ultrafast retinal densitometry,” Biomed. Opt. Express 13(10), 5311–5326 (2022). [CrossRef]  

33. V. P. Pandiyan, S. Schleufer, E. Slezak, J. Fong, R. Upadhyay, A. Roorda, R. Ng, and R. Sabesan, “Characterizing cone spectral classification by optoretinography,” Biomed. Opt. Express 13(12), 6574–6594 (2022). [CrossRef]  

34. X. Huang, T. Anderson, and A. Dubra, “Retinal magnification factors at the fixation locus derived from schematic eyes with four individualized surfaces,” Biomed. Opt. Express 13(7), 3786–3808 (2022). [CrossRef]  

35. R. L. Warner, D. H. Brainard, and J. I. W. Morgan, “Repeatability and reciprocity of the cone optoretinogram,” Biomed. Opt. Express 13(12), 6561–6573 (2022). [CrossRef]  

36. X. Jiang, T. Liu, V. P. Pandiyan, E. Slezak, and R. Sabesan, “Coarse-scale optoretinography (CoORG) with extended field-of-view for normative characterization,” Biomed. Opt. Express 13(11), 5989–6002 (2022). [CrossRef]  

37. E. Valterova, J. D. Unterlauft, M. Francke, T. Kirsten, R. Kolar, and F. G. Rauscher, “Comprehensive automatic processing and analysis of adaptive optics flood illumination retinal images on healthy subjects,” Biomed. Opt. Express 14(2), 945–970 (2023). [CrossRef]  

38. S. Mozaffari, F. Feroldi, F. LaRocca, P. Tiruveedhula, P. D. Gregory, B. H. Park, and A. Roorda, “Retinal imaging using adaptive optics optical coherence tomography with fast and accurate real-time tracking,” Biomed. Opt. Express 13(11), 5909–5925 (2022). [CrossRef]  

39. Z. Liu, F. Zhang, K. Zucca, A. Agrawal, and D. X. Hammer, “Ultrahigh-speed multimodal adaptive optics system for microscopic structural and functional imaging of the human retina,” Biomed. Opt. Express 13(11), 5860–5878 (2022). [CrossRef]  

40. P. Zhang, D. J. Wahl, J. Mocci, E. B. Miller, S. Bonora, M. V. Sarunic, and R. J. Zawadzki, “Adaptive optics scanning laser ophthalmoscopy and optical coherence tomography (AO-SLO-OCT) system for in vivo mouse retina imaging,” Biomed. Opt. Express 14(1), 299–314 (2023). [CrossRef]  

41. C. M. Lago, A. de Castro, C. Benedí-García, S. Aissati, and S. Marcos, “Evaluating the effect of ocular aberrations on the simulated performance of a new refractive IOL design using adaptive optics,” Biomed. Opt. Express 13(12), 6682–6694 (2022). [CrossRef]  

42. S. Vedhakrishnan, A. de Castro, M. Vinas, S. Aissati, and S. Marcos, “Accommodation through simulated multifocal optics,” Biomed. Opt. Express 13(12), 6695–6710 (2022). [CrossRef]  

43. S. P. Bang, J. D. Aaker, R. Sabesan, and G. Yoon, “Improvement of neural contrast sensitivity after long-term adaptation in pseudophakic eyes,” Biomed. Opt. Express 13(9), 4528–4538 (2022). [CrossRef]  

44. D. Jaisankar, M. Suheimat, R. Rosén, and D. A. Atchison, “Peripheral detection acuity for interference fringes and screen-based Gabor gratings,” Biomed. Opt. Express 13(12), 6645–6658 (2022). [CrossRef]  

45. M. Malivert, F. Harms, C. Veilly, J. Legrand, Z. Li, E. Bayer, D. Choquet, and M. Ducros, “Active image optimization for lattice light sheet microscopy in thick samples,” Biomed. Opt. Express 13(12), 6211–6228 (2022). [CrossRef]  

46. Y. Liu, B. Liu, J. Green, C. Duffy, M. Song, J. D. Lauderdale, and P. Kner, “Volumetric light sheet imaging with adaptive optics correction,” Biomed. Opt. Express 14(4), 1757–1771 (2023). [CrossRef]  

47. L. Mukhangaliyeva, S. Kocer, A. Warren, K. Bell, M. Boktor, M. Yavuz, E. Abdel-Rahman, and P. Haji Reza, “Deformable mirror-based photoacoustic remote sensing (PARS) microscopy for depth scanning,” Biomed. Opt. Express 13(11), 5643–5653 (2022). [CrossRef]  

48. W. Zhang, T. Man, M. Zhang, L. Zhang, and Y. Wan, “Computational adaptive holographic fluorescence microscopy based on the stochastic parallel gradient descent algorithm,” Biomed. Opt. Express 13(12), 6431–6442 (2022). [CrossRef]  

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