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Rapid Detection of Perfluorooctanesulfonic Acid Using Surface-Enhanced Raman Spectroscopy and Deep Learning
(2025-10-07)
Juhong, Aniwat
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Li, Bo
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Liu, Yifan
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Yao, Cheng You
;
Yang, Chia Wei
Per- and polyfluoroalkyl substances (PFAS) are a large group of human-made chemicals that have been widely used in industry and consumer products. Perfluorooctanesulfonic acid (PFOS) is a ubiquitous type of PFAS, which is extremely stable chemicals that have been persistent in the environment for many years. The accumulation of PFOS in the human body can lead to various unfavorable health issues related to the immune, metabolic, and endocrine systems. The conventional PFOS detection method utilizes liquid chromatography coupled with a mass spectroscopy system that typically involves a lengthy and complex procedure. Herein, we propose to develop a low-cost and rapid test approach based on surface-enhanced Raman spectroscopy (SERS) and deep learning for PFOS detection. The gold nanoparticle SERS substrates utilized in this study can significantly enhance the Raman signal of PFOS in solution at a low concentration. PFOS detection and quantification in water using the SERS-based substrate are carried out by measuring Raman peak intensities of PFOS in solution at a range of low concentrations and comparing them to the signal of a blank SERS substrate background. The results show that the SERS substrate can achieve a detection limit as low as 0.0005 ppb. In addition, we propose a demultiplexing deep learning model, which can generate high signal-to-noise ratio (SNR) PFOS spectra from the noisy mixture of PFOS and background Raman spectra. Average cross-correlation and mean absolute error (MAE) are utilized to evaluate the similarity between the demultiplexed and denoised PFOS Raman spectra (output of deep learning) and their ground truths. The proposed model can achieve an encouraging result with high average cross-correlation and low average MAE of 0.9622 ± 0.0667 and 0.0034 ± 0.0024, respectively.
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Monocular depth estimation based on deep learning for intraoperative guidance using surface-enhanced Raman scattering imaging
(2025-02-01)
Juhong, Aniwat
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Li, Bo
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Liu, Yifan
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Yao, Cheng You
;
Yang, Chia Wei
Imaging of surface-enhanced Raman scattering (SERS) nanoparticles (NPs) has been intensively studied for cancer detection due to its high sensitivity, unconstrained low signal-to-noise ratios, and multiplexing detection capability. Furthermore, conjugating SERS NPs with various biomarkers is straightforward, resulting in numerous successful studies on cancer detection and diagnosis. However, Raman spectroscopy only provides spectral data from an imaging area without co-registered anatomic context. This is not practical and suitable for clinical applications. Here, we propose a custom-made Raman spectrometer with computer-vision-based positional tracking and monocular depth estimation using deep learning (DL) for the visualization of 2D and 3D SERS NPs imaging, respectively. In addition, the SERS NPs used in this study (hyaluronic acid-conjugated SERS NPs) showed clear tumor targeting capabilities (target CD44 typically overexpressed in tumors) by an ex vivo experiment and immunohistochemistry. The combination of Raman spectroscopy, image processing, and SERS molecular imaging, therefore, offers a robust and feasible potential for clinical applications.
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Multihead Attention U-Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation
(2024-10-01)
Juhong, Aniwat
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Li, Bo
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Liu, Yifan
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Yang, Chia Wei
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Yao, Cheng You
Magnetic particle imaging (MPI) is an emerging noninvasive molecular imaging modality with high sensitivity and specificity, exceptional linear quantitative ability, and potential for successful applications in clinical settings. Computed tomography (CT) is typically combined with the MPI image to obtain more anatomical information. Herein, a deep learning-based approach for MPI-CT image segmentation is presented. The dataset utilized in training the proposed deep learning model is obtained from a transgenic mouse model of breast cancer following administration of indocyanine green (ICG)-conjugated superparamagnetic iron oxide nanoworms (NWs-ICG) as the tracer. The NWs-ICG particles progressively accumulate in tumors due to the enhanced permeability and retention (EPR) effect. The proposed deep learning model exploits the advantages of the multihead attention mechanism and the U-Net model to perform segmentation on the MPI-CT images, showing superb results. In addition, the model is characterized with a different number of attention heads to explore the optimal number for our custom MPI-CT dataset.
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Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography (MSOT) imaging
(2023-11-01)
Juhong, Aniwat
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Li, Bo
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Liu, Yifan
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Yao, Cheng You
;
Yang, Chia Wei
Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through-plane resolution volumetric MSOT is time-consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross-sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG-conjugated nanoworms particles (NWs-ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.
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Cost-Effective Near Infrared Fluorescence Wide-Field Camera for Breast Tumor Imaging
(2023-08-01)
Juhong, Aniwat
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Li, Bo
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Yao, Cheng You
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Yang, Chia Wei
;
Liu, Kunli
Near-infrared (NIR) fluorescence imaging has long been proven useful for numerous medical applications, especially image-guided surgery since it can deeply penetrate tissue and acquire a fluorescence image from the FDA-approved fluorescence dye known as indocyanine green (ICG). NIR imaging-guided surgery can help enhance outcomes by better defining surgical margins and minimizing operative time. However, most commercial NIR fluorescence imaging systems are bulky and expensive. In this work, we introduce a low-cost and portable NIR fluorescence imaging system. The proposed system exploits an inexpensive complementary metal-oxide-semiconductor (CMOS) based on Nyxel® technology, considerably improving quantum efficiency (QE) in the NIR range. Therefore, it can acquire a NIR fluorescence image with outstanding quality. Furthermore, we conjugated ICG with nanoworm particles (NWs-ICG) since nanoparticle-conjugated dye can increase accumulation in a tumor. We show impressive applications of our system for ex vivo tissues and in vivo image-guided surgery using NWs-ICG as a contrast agent for breast tumors from transgenic mice.
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Super-resolution and segmentation deep learning for breast cancer histopathology image analysis
(2023-01-01)
Juhong, Aniwat
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Li, Bo
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Yao, Cheng You
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Yang, Chia Wei
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Agnew, Dalen W.
Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.
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Metalens-based miniaturized optical systems
(2019-05-01)
Li, Bo
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Piyawattanametha, Wibool
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Qiu, Zhen
Metasurfaces have been studied and widely applied to optical systems. Ametasurface-based flat lens (metalens) holds promise in wave-front engineering for multiple applications. The metalens has become a breakthrough technology for miniaturized optical system development, due to its outstanding characteristics, such as ultrathinness and cost-effectiveness. Compared to conventional macro- or meso-scale optics manufacturing methods, the micro-machining process for metalenses is relatively straightforward and more suitable for mass production. Due to their remarkable abilities and superior optical performance, metalenses in refractive or diffractive mode could potentially replace traditional optics. In this review, we give a brief overview of the most recent studies on metalenses and their applications with a specific focus on miniaturized optical imaging and sensing systems. We discuss approaches for overcoming technical challenges in the bio-optics field, including a large field of view (FOV), chromatic aberration, and high-resolution imaging.

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