Yonsei University · Medicine
Professor Jin Sung Kim's research lab specializes in advanced nanoelectronics and biomedical imaging technologies, focusing on the development of two-dimensional semiconductor devices such as black phosphorus field-effect transistors for next-generation flexible and transparent electronics. The lab also pioneers AI-driven medical image reconstruction and synthetic imaging techniques, particularly in generating synthetic CT images from MRI data to enable MRI-only radiotherapy workflows. Their work bridges nanomaterials engineering with clinical applications, emphasizing device performance optimization and diagnostic image enhancement. The lab's interdisciplinary approach integrates materials science, semiconductor physics, and artificial intelligence to address challenges in both electronic devices and medical imaging.
Figures are computed from collected data and may differ slightly.
We have fabricated dual gate field effect transistors (FETs) with 12 nm-thin black phosphorus (BP) channel on glass substrate, where our BP FETs have a patterned-gate architecture with 30 nm-thick Al2O3 dielectrics on top and bottom of a BP channel. Top gate dielectric has simultaneously been used as device encapsulation layer, controlling the threshold voltage of FETs as well when FETs mainly operate under bottom gate bias. Bottom, top, and dual gate-controlling mobilities were estimated to be
Institutional review board approval was obtained. Informed patient consent was not required. Study was compliant with HIPAA. Performance of an automated pulmonary nodule detection program was evaluated on multi-detector row CT images that were acquired once but reconstructed retrospectively at different section thicknesses and reconstruction intervals. From raw CT data in 10 patients with pulmonary nodules, three sets of CT images were reconstructed separately in each patient by selecting two se
The deep spatial pyramid convolutional framework proposed here demonstrates improved performance compared to the conventional GAN framework that has been applied to the image-to-image translation task of sCT generation. Adopting the method is a first step toward an MRI-only RT workflow that enables widespread clinical applications for MR-IGRT including online adaptive therapy.
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