Piyawattanametha, Wibool

Preferred name

Main Affiliation

School of Engineering

wibool.pi@kmitl.ac.th

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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
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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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3CA-FO: Budget stereoscopic 3D imaging colposcope
(2023-01-01)
Amnuayphol, Nontiwat
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Piyawattanametha, Wibool
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Piyawattanametha, Nicholas
This research aims to develop an advanced medical device designed to enhance the diagnostic quality of conventional colposcopy. The device utilizes cutting-edge technologies, including 3D image synthesis via stereoscopic imaging and polarized glasses as the primary focus of the study is to improve cervical cancer. The research scope encompasses enhancing spatial information, The research scope involves enhancing spatial information while allowing the doctor to maintain the advantage of near vision and enabling multi-angle imaging. The hardware of the colposcope is based on the design from Duke University's 2018 research. Our 3D Cervical Assessment - Fine-tuned Optics colposcope (3CA-FO) is capable of providing real-time 3D imaging with precise calibration, achieved through the utilization of the Embedded Mono Calibration for Heterogeneous Lenses technique. This ensures an instantaneous and high-quality 3D output response.
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Editorial Special Issue on OMN 2022
(2023-08-15)
Piyawattanametha, Wibool
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Ferhanoglu, Onur
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Zhang, John X.J.
We are pleased to provide readers with the newest and important technologies presented at the International Conference on Optical MEMS and Nanophotonics 2022 (OMN 2022), through this Special Issue published by the IEEE Photonics Technology Letters (PTL) journal. The Optical Micro- and Nano Systems (OMN) conferences have surged in prominence over the recent years, mirroring the rapid advancements and burgeoning interest in the realm of optics and photonics technology for diverse applications from medical imaging, environmental sensing, to autonomous driving. OMN epitomizes the amalgamation of theory and practice, facilitated by our increasing technical prowess to engineer micro- and nano-scale structures and dynamic elements purpose-built for the interaction and manipulation of light.
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Development of a Low-Cost Raman Spectrometer for Non-Invasive Molecular Mapping
(2024-01-01)
Tangkiatphaibun, Parawee
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Suttikittipong, Pasin
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Udomtanasub, Pholchanok
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Piyawattanametha, Nicholas
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Piyawattanametha, Wibool
Non-communicable diseases like diabetes continue to be major global health challenges, traditionally managed through invasive glucose monitoring methods. To address this, our research focuses on developing a DIY Raman spectrometer enhanced with a fiber laser for non-invasive glucose detection. Utilizing Surface-Enhanced Raman Spectroscopy (SERS) with a SERS S440 substrate at 500 ppm, we successfully achieved Raman mapping and demonstrated the capability to capture Raman shifts across various samples. The mapping results indicate the system's effectiveness in visualizing molecular composition, laying a strong foundation for future integration of SERS multiplexing to further enhance sensitivity and enable simultaneous detection of multiple analytes. This approach presents a cost-effective and accessible solution, showcasing the potential of advanced Raman spectroscopy in diabetes care and non-invasive monitoring technologies. Our work represents a significant step toward improving the accessibility and accuracy of glucose monitoring for patients globally.
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Picomolar Dopamine Detection using Colloidal Surface-Enhanced Raman Scattering Based on Benzene-Dithiol–Linked Gold Nanoparticles in Solution
(2026-04-01)
Sucharitakul, Waraporn
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Danvirutai, Pobporn
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Pinlaor, Somchai
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Piyawattanametha, Wibool
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Srichan, Chavis
Sensitive detection of dopamine at ultralow concentrations is essential for the development of advanced chemical and neurochemical sensing technologies. Surface-enhanced Raman scattering (SERS) offers molecular specificity and high sensitivity, but its analytical reliability is often limited by poor signal reproducibility arising from uncontrolled nanoparticle aggregation. In this work, we report a solvent-based colloidal SERS platform for picomolar dopamine detection using benzene-1,4-dithiol (BDT)–linked gold nanoparticles (AuNPs). BDT serves as a molecular linker that defines sub-nanometer plasmonic nanogaps and simultaneously acts as an intrinsic Raman reference. Dopamine molecules introduced in dilute solution are efficiently sampled within these nanogaps through noncovalent interactions, resulting in strong and reproducible SERS enhancement. The proposed platform demonstrates a detection limit in the picomolar range, a wide linear dynamic response, and excellent signal stability without the use of biological or artificial matrices. This study presents a solvent-based SERS strategy for quantitative liquid-phase dopamine detection in aqueous environments, offering a foundation for further development in sensor calibration and integration with biological matrices.
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Multiplexed Surface-Enhanced Raman Mapping via DIY Spectrometer for High-Specificity Molecular Detection
(2025-01-01)
Tangkiatphaibun, Parawee
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Suttikittipong, Pasin
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Udomtanasub, Pholchanok
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Piyawattanametha, Aaron
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Piyawattanametha, Nicholas
In this research, we aim to build a low-cost Do-It-Yourself(DIY) Raman spectrometer system. Due to the previous limitation, the surface-enhanced Raman spectroscopy(SERS) multiplexing for enhance the Raman fingerprint of the benchmark substrate. The previous research was done at a single point, then 3×2 multiplex mapping only. This study enables the 4×4 multiplex mapping method using SERS substrates. Expanding beyond previous work on single-point detection, this study demonstrates a 4×4 multiplex mapping method using SERS substrates (S440) nanoparticle-based substrate. This allows a deeper range of the DIY Raman to get multiple Raman fingerprints from the same substrate and leads to the future artificial intelligence(AI) identification of the substrate complexity and the quantitative analysis. The system is based on a 785nm fiber laser and a custom optics setup.
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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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Non-invasive techniques with vital signs for glucose monitoring
(2025-01-01)
Mahittikorn, Pawarit
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Tangkiatphaibun, Parawee
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Thitathan, Thitisart
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Udomtanasub, Pholchanok
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Piyawattanametha, Wibool
This study is focusing on integrating finger sleeves for machine-learning with Near-infrared (NIR) spectroscopy and additional sensors techniques. It applied light emitting diodes (LEDs) at 660 nm, 880 nm, 940 nm wavelengths and photodetectors and a galvanic skin response (GSR) and a temperature sensor to read the signal from patients’ fingers. These sensors are attached to the finger sleeves to make it easy to wear for this continuous glucose monitoring. After the data was collected from the NIR spectroscopy and multiple sensors it has used in the machine learning models to predict the blood sugar level. For the machine learnings that was selected in this study are Linear regression, and Random forest model. Which the R<sup>2</sup> result was 0.07 and -0.27 respectively.
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Theoretical modeling and experimental investigation of in-phase resonant MEMS mirrors with cascaded structures
(2024-05-01)
Chen, Wenhao
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Luo, Huahuang
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Tavakkoli, Hadi
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Duan, Mingzheng
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Piyawattanametha, Wibool
This paper proposes an efficient nonlinear one-dimensional (1D) compact mass-damper-spring model to predict the dynamic response of electrostatic resonant micro-electro-mechanical system (MEMS) mirrors with cascaded structures. The time-dependent damping moment due to viscous shear and pressure drag is computed using semi-empirical analytical equations for comb-drive structures and device frames. Nonlinear electrostatic force induced by the comb drives is efficiently acquired based on the hybrid method. The optimized device is fabricated using MEMS fabrication processes based on a 4-inch silicon-on-insulator wafer. The proposed compact model with the measured key parameters from the fabricated device shows excellent capability to accurately predict nonlinear dynamic responses of the fabricated device, including parametric excitation and hysteretic frequency response, with an average error of less than 5%. In particular, our 1D model is three orders of magnitude faster than the conventional finite element method model (0.8 s versus 1 h), enabling efficient system-level optimization of the critical design parameters. Based on the parametric study, electrode gap distance and torsion spring width are found to be two critical design parameters and dimensional analysis is conducted for design optimization with scan angle enhancing from 16.8° to 24° compared with the first design.
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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%.

Piyawattanametha, Wibool

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