Piyawattanametha, Wibool

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Piyawattanametha, Wibool
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wibool.pi@kmitl.ac.th

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    Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography (MSOT) imaging
    (2023-11-01)
    Juhong, Aniwat
    ;
    Li, Bo
    ;
    Liu, Yifan
    ;
    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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    Multihead Attention U-Net for Magnetic Particle Imaging–Computed Tomography Image Segmentation
    (2024-10-01)
    Juhong, Aniwat
    ;
    Li, Bo
    ;
    Liu, Yifan
    ;
    Yang, Chia Wei
    ;
    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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    Cost-Effective Near Infrared Fluorescence Wide-Field Camera for Breast Tumor Imaging
    (2023-08-01)
    Juhong, Aniwat
    ;
    Li, Bo
    ;
    Yao, Cheng You
    ;
    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.