최규성 교수
Kyu Sung Choi
서울대학교 · 의학
연구실 소개
최규성 교수의 연구실은 뇌영상 영상의학과 영상유전체학 분야에서 활발히 연구를 진행하고 있습니다. 주로 동적 강화 MRI(DCE-MRI), 흐름강화 영상(DSC-MRI), 그리고 확산 영상 기반의 뇌간질환 평가 기술을 활용해 뇌종양의 유전자형(예: IDH, MGMT) 예측 및 신경퇴행성질환(다발성경화증, NMOSD)의 뇌간질환 관련 생리적 변화를 규명하고 있습니다. 특히 딥러닝 기반의 해석가능한 모델 개발을 통해 영상과 유전자 간의 관계를 정량적으로 분석하고 있으며, 정밀의료 및 조기 진단에 기여하고자 합니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15We developed an explainable recurrent neural network model based on DSC perfusion MRI to predict IDH genotypes in gliomas.
O6-methylguanine-DNA methyl transferase (MGMT) methylation prediction models were developed using only small datasets without proper external validation and achieved good diagnostic performance, which seems to indicate a promising future for radiogenomics. However, the diagnostic performance was not reproducible for numerous research teams when using a larger dataset in the RSNA-MICCAI Brain Tumor Radiogenomic Classification 2021 challenge. To our knowledge, there has been no study regarding the
Background Pharmacokinetic (PK) parameters obtained from dynamic contrast agent-enhanced (DCE) MRI evaluates the microcirculation permeability of astrocytomas, but the unreliability from arterial input function (AIF) remains a challenge. Purpose To develop a deep learning model that improves the reliability of AIF for DCE MRI and to validate the reliability and diagnostic performance of PK parameters by using improved AIF in grading astrocytomas. Materials and Methods This retrospective study in
Previous literature indicates important glymphatic system alteration in MS and NMOSD. We explore the difference between MS and NMOSD using diffusion MRI-based measures of the glymphatic system. We show support for the null hypothesis of no difference between MS and NMOSD. This suggests that glymphatic alteration associated with MS and NMOSD might be similar and common etiology.
Precise remote evaluation of both suicide risk and psychiatric disorders is critical for suicide prevention as well as for psychiatric well-being. Using questionnaires is an alternative to labor-intensive diagnostic interviews in a large general population, but previous models for predicting suicide attempts suffered from low sensitivity. We developed and validated a deep graph neural network model that increased the prediction sensitivity of suicide risk in young adults (n = 17,482 for training
The global morphologic feature derived from 3D CNN models using whole-brain MRI has independent prognostic value for diffuse gliomas. Combining clinical, molecular genetic, and imaging data yields the best performance.
The addition of DWI to conventional MRI-MRCP did not improve diagnostic performance in the characterization of perihilar strictures or in determining whether the bilateral secondary biliary confluence was involved in perihilar cholangiocarcinomas.
LE is a safe and effective procedure, and should be considered as a treatment option for pancreatic lesions that do not involve the main pancratic duct and have an outgrowing aspect with small tumor bed.
Percutaneous aspiration embolectomy is a useful tool in recanalization of embolic occlusion of the SMA in select patients.
Purpose To determine the yield of follow-up abdominopelvic computed tomography (CT) in detecting extragastric recurrence after curative endoscopic submucosal dissection (ESD) for early gastric cancers (EGCs) that meet the expanded criteria. Materials and Methods Institutional review board approval was obtained for this retrospective study, and the requirement to obtain informed consent was waived. Patients who underwent curative ESD for EGCs that met the expanded criteria between November 2005 a
The experimental blood pool agent KEG3 offers equivalent image quality for whole-heart coronary MRA at 3 T upon contrast administration and persistent better quality in the subsequent scans, compared with a traditional extracellular gadolinium-based contrast agent.
Accel-DL substantially reduced the scan time and improved the quality of brain MRI in both spin-echo and gradient-echo sequences without compromising volumetry, including lesion quantification.
Deep learning has shown its feasibility for applications in medical imaging.Deep learning-based methods are also rapidly being applied in a wide range of areas to replace traditional model-based methods, showing remarkable improvements in several MR image processing areas such as image reconstruction, image contrast conversion, and image quality improvement.With improvement of perfusion MRI techniques, various clinical applications have been also researched, which have improved tracer-kinetic mo
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