A Stare-Down Video-Rate High-Throughput Hyperspectral Imaging System and Its Applications in Biological Sample Sensing

Li, Yunfei; Shen, Fuzhou; Hu, Lantian; Lang, Ziyue; Liu, Qian; Cai, Fuhong; Fu, Ling

doi:10.1109/jsen.2023.3308394

Cited by 29 publications

(25 citation statements)

References 31 publications

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“…The proposed method has the potential to support the hyperspectral system (Li et al 2023 ), which integrates spatial and spectral information to achieve non-invasive diagnosis for bioinformatics. The system can analyze biological imaging samples in real time, but the high imaging rate usually compromise the resolution.…”

Section: Discussionmentioning

confidence: 99%

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling

Chang,

Peng,

Safari

et al. 2024

Phys. Med. Biol.

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High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the denoising diffusion probabilistic model (DDPM) by minimizing its unpredictability and uncertainty. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020). For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced PSNR from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970±0.019, 0.968±0.022, and 0.967±0.023 for the proposed method, CGAN, and Bicubic, respectively. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications.

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Section: Discussionmentioning

confidence: 99%

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling

Chang,

Peng,

Safari

et al. 2024

Phys. Med. Biol.

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“…Researchers use satellites such as MODIS, TM, Sentinel-3, and ETM+ to remotely sense water bodies, and then extract reflection spectra and spatial information to explore the temporal and spatial distribution of Chla in water bodies [ 7 , 8 , 9 ]. Hyperspectral technology is widely used in fields such as remote sensing reconnaissance, agricultural monitoring, environmental science, and medical diagnosis by capturing the reflected or emitted spectra of an object at different wavelengths and realizing detailed analyses of the composition and properties of materials [ 10 , 11 , 12 ]. Researchers use hyperspectral remote sensing data to predict and evaluate other indicators in water, such as concentrations of heavy metals [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, we need to consider the obstruction of vegetation and atmospheric interference, which will have a certain impact on water quality monitoring [ 16 , 17 ]. Vegetation can block light from entering water bodies, and aerosols, gases, and clouds in the atmosphere can also interfere with the propagation and reflection of light, making it relatively difficult to accurately obtain the reflection spectrum of water bodies [ 11 , 18 , 19 , 20 ]. Secondly, the reflection signal of water bodies is usually weak, especially in the visible light band.…”

Section: Introductionmentioning

confidence: 99%

A High-Frequency and Real-Time Ground Remote Sensing System for Obtaining Water Quality Based on a Micro Hyper-Spectrometer

Li,

Fu,

Lang

et al. 2024

Sensors

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The safeguarding of scarce water resources is critically dependent on continuous water quality monitoring. Traditional methods like satellite imagery and automated underwater observation have limitations in cost-efficiency and frequency. Addressing these challenges, a ground-based remote sensing system for the high-frequency, real-time monitoring of water parameters has been developed. This system is encased in a durable stainless-steel shell, suited for outdoor environments, and features a compact hyperspectral instrument with a 4 nm spectral resolution covering a 350–950 nm wavelength range. In addition, it also integrates solar power, Wi-Fi, and microcomputers, enabling the autonomous long-term monitoring of water quality. Positioned on a rotating platform near the shore, this setup allows the spectrometer to quickly capture the reflective spectrum of water within 3 s. To assess its effectiveness, an empirical method correlated the reflective spectrum with the actual chlorophyll a(Chla) concentration. Machine learning algorithms were also used to analyze the spectrum’s relationship with key water quality indicators like total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). Results indicate that the band ratio algorithm accurately determines Chla concentration (R-squared = 0.95; RMSD = 0.06 mg/L). For TP, TN, and COD, support vector machine (SVM) and linear models were highly effective, yielding R-squared values of 0.93, 0.92, and 0.88, respectively. This innovative hyperspectral water quality monitoring system is both practical and reliable, offering a new solution for effective water quality assessment.

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“…Electron transport materials (ETMs) play critical roles in organic light emitting diodes (OLEDs), 1,2 solar cells, 3 and optoelectronic devices. 4 Materials such as metal oxides, 5 organic small molecules, 6 polymer materials, 7 and fullerenes 8 are frequently utilized as ETMs. In particular, organic small materials exhibit broad application prospects due to their advantages of adjustable design, solution processing, and convenience for large-scale printing.…”

Section: Introductionmentioning

confidence: 99%

“…Electron transport materials (ETMs) play critical roles in organic light emitting diodes (OLEDs), , solar cells, and optoelectronic devices . Materials such as metal oxides, organic small molecules, polymer materials, and fullerenes are frequently utilized as ETMs.…”

Section: Introductionmentioning

confidence: 99%

Measurement and Model Evaluation of Solubility of 2-(4-tert-Butylphenyl)-5-(4-biphenyl)-1,3,4-oxadiazole in Different Organic Solvents at Various Temperatures

Piao,

Guo,

Wang

et al. 2024

J. Chem. Eng. Data

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2-(4-tert-Butylphenyl)-5-(4-biphenyl)-1,3,4-oxadiazole (PBD) is one of the most widely used electron transport layer materials in optoelectronic devices, and studies on the solubility of PBD in various solvents are critical for development of solution-processable roll-to-roll electronic devices. In this study, the isothermal saturation method is used to determine the solubility data of PBD in 12 commonly used organic solvents. The experimental solubility of PBD increases gradually with enhanced temperature for the examined temperature range of 283.15−323.15 K in nhexane, methanol, ethanol, isopropanol, tetrahydrofuran (THF), acetonitrile, ethyl acetate, N-methylpyrrolidone (NMP), toluene, and chlorobenzene, while the temperature ranges were 293.15−333.15 K and 273.15−303.15 K for dimethyl sulfoxide and dichloromethane, respectively. The obtained solubility values of PBD are correlated by three models including the modified Apelblat equation, λh equation, and Yaws equation.Comparison of the calculated Akaike information criterion values resulted in selecting the optimal model for each solvent. Of the five solvents calculated, n-hexane, methanol, toluene, THF, and NMP, the λh equation provides an optimal fit. Both the modified Apelblat equation and the Yaws equation work well in chlorobenzene. Among the other six solvents, the fit of the Yaws equation is the best. These results of solubility provide the basic data for the synthesis of PBD nanoparticles via high-gravity-assisted antisolvent precipitation processing.

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A Stare-Down Video-Rate High-Throughput Hyperspectral Imaging System and Its Applications in Biological Sample Sensing

Cited by 29 publications

References 31 publications

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling

A High-Frequency and Real-Time Ground Remote Sensing System for Obtaining Water Quality Based on a Micro Hyper-Spectrometer

Measurement and Model Evaluation of Solubility of 2-(4-tert-Butylphenyl)-5-(4-biphenyl)-1,3,4-oxadiazole in Different Organic Solvents at Various Temperatures

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