Multiresolution Localization With Temporal Scanning for Super-Resolution Diffuse Optical Imaging of Fluorescence

Bentz, Brian Z.; Lin, Dergan; Patel, Justin A.; Webb, Kevin J.

doi:10.1109/tip.2019.2931080

Cited by 5 publications

(1 citation statement)

References 54 publications

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“…Among the depth-improving techniques in section 3, endoscopy and PAT (>5 cm penetration depth) may be applicable to living human brains [293], yet the former suffers from its invasive nature and limited field of view, while the latter has inadequate resolution for neurons, and have not been demonstrated to date. Near-infrared diffuse optical tomography may provide in vivo whole human brain mapping, but similar to EEG, its spatial resolution is far from resolving single neurons, despite superresolution endeavors [294,295].…”

Section: Discussionmentioning

confidence: 99%

Optical volumetric brain imaging: speed, depth, and resolution enhancement

Huang

Irawati

Chien

et al. 2021

J. Phys. D: Appl. Phys.

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Understanding how the brain functions is one of the grand challenges in modern scientific research. Similar to a computer, a functional brain is composed of hardware and software. The major bottleneck lies in the difficulty to directly observe the brain ‘software’, i.e. the rule and operating information used by the brain that might emerge from pan-neuron/synapse connectome. A recognized strategy for probing the functional connectome is to perform volumetric imaging in brains with high spatiotemporal resolution and deep brain penetration. Among various imaging technologies, optical imaging offers appealing combinations including spatial resolution of sub-micrometer to nanometer, temporal resolution of second to millisecond, penetration depth of millimeter or deeper, and molecular contrast based on the abundant choices of fluorescent indicators. Thus, it is ideal for enabling three-dimensional functional brain mapping of small animal models. In this review, we focus on recent technological advances in optical volumetric imaging, with an emphasis on the tools and methods for enhancing imaging speed, depth, and resolution. The review could serve as a quantitative reference for physicists and biologists to choose the techniques better suited for specific applications, as well as to stimulate novel technical developments to advance brain research.

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Section: Discussionmentioning

confidence: 99%

Optical volumetric brain imaging: speed, depth, and resolution enhancement

Huang

Irawati

Chien

et al. 2021

J. Phys. D: Appl. Phys.

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Localization of Fluorescent Targets in Deep Tissue With Expanded Beam Illumination for Studies of Cancer and the Brain

Bentz

Mahalingam

Ysselstein

et al. 2020

IEEE Trans. Med. Imaging

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Super-Resolution Reconstruction Based on Adaptive Weight Adjustment

Zhao¹,

Wan²

2023

Int. J. Patt. Recogn. Artif. Intell.

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In image super-resolution, the existing convolution neural network methods increase the number of network layers and filters to achieve better performance, and seldom consider the influence of different branches in feature extraction on the reconstruction effect, which leads to the problems of blurred details and unclear visual perception. Therefore, we propose an adaptive weight adjustment super-resolution (AWSR) reconstruction model in this paper. The model includes Shallow Feature Extraction (SFE), Information Extraction Enhancement Block (IDEB) and Reconstruction Block (RB). IDEB composed of Adaptive Weight Blocks (AWB) and Channel Linking Layers (CLL) learns a deeper mapping relationship between LR image and HR image by adaptively adjusting the proportions of different branches. It not only saves computational cost, but also improves the expression ability of the model. Meanwhile, the performance of the model is further improved by dimension change in the up-sample block. Especially, the image edge and texture reconstruction effects are obviously improved. Compared with SRNHARB algorithm proposed in 2021, the PSNR values are increased by 0.23[Formula: see text]dB, 0.19[Formula: see text]dB and 0.02[Formula: see text]dB at [Formula: see text] on the Set5 dataset. Moreover, the proposed model has a strong generalization ability, and the reconstructed SR images can achieve satisfactory results.

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Multiresolution Localization With Temporal Scanning for Super-Resolution Diffuse Optical Imaging of Fluorescence

Cited by 5 publications

References 54 publications

Optical volumetric brain imaging: speed, depth, and resolution enhancement

Optical volumetric brain imaging: speed, depth, and resolution enhancement

Localization of Fluorescent Targets in Deep Tissue With Expanded Beam Illumination for Studies of Cancer and the Brain

Super-Resolution Reconstruction Based on Adaptive Weight Adjustment

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