Jin Sung Kim
연세대학교 영상의학교실 · 의학
김진성 교수의 연구실은 나노소재 기반의 전자소자 및 의료영상 분야에서의 응용을 중심으로 연구를 진행하고 있습니다. 블랙홀리움 등 2차원 물질을 활용한 고성능 트랜지스터 설계와 함께, 의료 영상에서의 자동 진단 알고리즘 개발 및 MRI 기반 방사선 치료 워크플로우 구축에 기여하고 있습니다. 특히, 나노소재의 전자적 특성 제어와 의료 영상의 정밀 분석을 융합한 기술 개발이 핵심 연구 방향입니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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.
