Bio-inspired imager improves sensitivity in near-infrared fluorescence image-guided surgery

Garcia, Missael; Edmiston, Christopher; York, Timothy; Marinov, Radoslav; Mondal, Suman B.; Zhu, Nan; Sudlow, Gail; Akers, Walter J.; Margenthaler, Julie A.; Achilefu, Samuel; Liang, Rongguang; Zayed, Mohamed A.; Pepino, Marta Yanina; Gruev, Viktor

doi:10.1364/optica.5.000413

Cited by 49 publications

(46 citation statements)

References 32 publications

(30 reference statements)

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“…The fact that disordered hyperuniform systems represent topological states of matter sharing fluid and crystal-like properties makes them the solution of choice when regular arrangements such as those of arthropod eyes are hampered by the variability of the photoreceptors (e.g., unequal sizes and numbers). Present-day bio-inspired optical devices rely on regular arrangements [5,42]. In certain instances, the combination of different types of receptors and in different numbers might be required, compromising the feasibility of regular arrays of receptors.…”

Section: Discussionmentioning

confidence: 99%

Minimal statistical-mechanical model for multihyperuniform patterns in avian retina

et al. 2020

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Birds are known for their extremely acute sense of vision. The very peculiar structural distribution of five different types of cones in the retina underlies this exquisite ability to sample light. It was recently found that each cone population as well as their total population display a disordered pattern in which long-wavelength density fluctuations vanish [Jiao et al., Phys. Rev. E 89, 022721 (2014)]. This property, known as hyperuniformity, is also present in perfect crystals. In situations like the avian retina in which both the global structure and that of each component display hyperuniformity, the system is said to be multihyperuniform. In this work, we aim at devising a minimal statistical-mechanical model that can reproduce the main features of the spatial distribution of photoreceptors in avian retina, namely the presence of disorder, multihyperuniformity, and local heterocoordination. This last feature is key to avoiding local clustering of the same type of photoreceptors, an undesirable feature for the efficient sampling of light. For this purpose, we formulate a minimal statisticalmechanical model that definitively exhibits the required structural properties: an equimolar three-component mixture (one component to sample each primary color: red, green, and blue) of nonadditive hard disks to which a long-range logarithmic repulsion is added between like particles. Interestingly, a Voronoi analysis of our idealized system of photoreceptors shows that the space-filling Voronoi polygons display a rather uniform area distribution, symmetrically centered around that of a regular lattice, a structural property also found in human retina. Disordered multihyperuniformity offers an alternative to generate photoreceptor patterns with minimal long-range concentration and density fluctuations. This is the key to overcoming the difficulties in devising an efficient visual system in which crystal-like order is absent.

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Section: Discussionmentioning

confidence: 99%

Minimal statistical-mechanical model for multihyperuniform patterns in avian retina

et al. 2020

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“…Recently, the bio-inspired imaging systems that imitate special features and functions of natural eyes, such as optical, photonic, and mechanical structures, have been reported. [34,44] For example, in the case of chambered eyes, the ball lenses and typical and c) neuromorphic vision sensor. With the pre-processing device, an image with a higher contrast and a lesser noise can be produced.…”

Section: Bio-inspired Artificial Vision Systemmentioning

confidence: 99%

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confidence: 99%

Bio‐Inspired Artificial Vision and Neuromorphic Image Processing Devices

Kim

Lee

et al. 2021

Adv Materials Technologies

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However, conventional image recognition systems using a flat image sensor array with a multilens optical system and the von-Neumann computing architecture for processing the acquired image data have several limitations such as high system-level complexity, bulky module size, large computing load, and low energy efficiency. [7] Therefore, advanced devices in both image acquisition and image data processing are required. As a result, bio-inspired imaging devices [8][9][10] (i.e., artificial vision) and neuromorphic image processing devices [11][12][13] (i.e., artificial synapse) have received considerable attention (Figure 1).Bio-inspired imaging devices (e.g., bioinspired camera) have been developed for image acquisition. [14] Conventional imaging devices require bulky and heavy optical systems to obtain high-quality visual information. [15] In contrast, natural eyes have a simple and small optical geometry and high-quality image acquisition capability. [16,17] Therefore, bio-inspired artificial vision has been developed by mimicking the unique structural and functional advantage of natural eyes [2,6] (Figure 1a). For example, the chambered eye, typically found in humans and aquatic animals, exhibits a wide field of view, low optical aberration, and facile accommodation with a simple optical system. [16,17] The compound eye has distinctive optical geometries, and such structures offer various useful visual features. [18,19] Neuromorphic computing devices that can efficiently process massive image data acquired from the imaging device have been developed for image data processing. [20][21][22] The conventional von-Neumann architecture, in which the central processing unit and memory unit are separated, is not suitable to efficiently process the massive unstructured image data. [23,24] Therefore, a novel computing device inspired by the human brain (i.e., electronic synapse) has been developed [25,26] (Figure 1b). For example, the memristor crossbar array can efficiently perform vector multiplications. [27] Such a neuromorphic device implements artificial neural networks (ANN) in the hardware and enables efficient parallel processing of image data with low energy consumption. [25] In a previous study, a device that integrates the synaptic device and photodetector in one unit has been reported. [26] Despite recent progress in the hardware of the neuromorphic image data processing devices, such devices still require Remarkable technological developments for efficient image recognition (i.e., image acquisition and image data processing) have been reported in the past decade. Such advances in imaging and image processing technologies have driven significant progress in mobile electronics and machine vision applications. In particular, for image acquisition devices, two types of natural eyes (i.e., chambered and compound eyes) have inspired the development of novel multifunctional imaging devices with unique optical geometries. For image data processing devices, novel computing devices based on memristor crossbar arrays,...

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“…In recent years, many groups have been working on this issue. In terms of hardware, various imaging systems have been developed for intraoperative FIGS, including systems designed for open and endoscopic surgeries, as well as systems utilizing NIR-II signals (optical spectrum: 1,000-1,700 nm) (46,(50)(51)(52)(53). Besides those effects, the improvement of software with new imaging algorithms can further mitigate the physical limitations in hardware systems.…”

Section: Figsmentioning

confidence: 99%

A review of the application of machine learning in molecular imaging

Yin¹,

Cao²,

Wang³

et al. 2021

Ann Transl Med

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Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI.OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural networkbased machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.

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Bio-inspired imager improves sensitivity in near-infrared fluorescence image-guided surgery

Cited by 49 publications

References 32 publications

Minimal statistical-mechanical model for multihyperuniform patterns in avian retina

Minimal statistical-mechanical model for multihyperuniform patterns in avian retina

Bio‐Inspired Artificial Vision and Neuromorphic Image Processing Devices

A review of the application of machine learning in molecular imaging

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