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Artificial Intelligence in Neuroimaging: Clinical Applications

Choi KS, Sunwoo L

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging....
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A Nationwide Web-Based Survey of Neuroradiologists’ Perceptions of Artificial Intelligence Software for Neuro-Applications in Korea

Choi H, Sunwoo L, Cho SJ, Baik SH, Bae YJ, Choi BS, Jung C, Kim JH

Objective: We aimed to investigate current expectations and clinical adoption of artificial intelligence (AI) software among neuroradiologists in Korea. Materials and Methods: In April 2022, a 30-item online survey was conducted...
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Metal Artifact Reduction for Orthopedic Implants: Brain CT Angiography in Patients with Intracranial Metallic Implants

Sunwoo L, Park SW, Rhim JH, Kang Y, Chung YS, Son YJ, Kim SC

BACKGROUND: The purpose of this study was to qualitatively and quantitatively evaluate the effects of a metal artifact reduction for orthopedic implants (O-MAR) for brain computed tomographic angiography (CTA) in...
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Differentiation of Deep Subcortical Infarction Using High-Resolution Vessel Wall MR Imaging of Middle Cerebral Artery

Bae YJ, Choi BS, Jung C, Yoon YH, Sunwoo L, Bae HJ, Kim JH

OBJECTIVE: To evaluate the utility of high-resolution vessel wall imaging (HR-VWI) of middle cerebral artery (MCA), and to compare HR-VWI findings between striatocapsular infarction (SC-I) and lenticulostriate infarction (LS-I). MATERIALS AND...
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Dichotomizing Level of Pial Collaterals on Multiphase CT Angiography for Endovascular Treatment in Acute Ischemic Stroke: Should It Be Refined for 6-Hour Time Window?

Woo HG, Jung C, Sunwoo L, Bae YJ, Choi BS, Kim JH, Kim BJ, Han MK, Bae HJ, Jung S, Cha SH

PURPOSE: Although endovascular treatment is currently thought to only be suitable for patients who have pial arterial filling scores >3 as determined by multiphase computed tomography angiography (mpCTA), a cut-off...
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Fully Automatic Segmentation of Acute Ischemic Lesions on Diffusion-Weighted Imaging Using Convolutional Neural Networks: Comparison with Conventional Algorithms

Woo I, Lee A, Jung SC, Lee H, Kim N, Cho SJ, Kim D, Lee J, Sunwoo L, Kang DW

OBJECTIVE: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. MATERIALS...
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Deep Learning-Based Automatic Classification of Ischemic Stroke Subtype Using Diffusion-Weighted Images

Ryu WS, Schellingerhout D, Lee H, Lee KJ, Kim CK, Kim BJ, Chung JW, Lim JS, Kim JT, Kim DH, Cha JK, Sunwoo L, Kim D, Suh SI, Bang OY, Bae HJ, Kim DE

Background and Purpose Accurate classification of ischemic stroke subtype is important for effective secondary prevention of stroke. We used diffusion-weighted image (DWI) and atrial fibrillation (AF) data to train a...
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Efficacy of Maximum Intensity Projection of Contrast-Enhanced 3D Turbo-Spin Echo Imaging with Improved Motion-Sensitized Driven-Equilibrium Preparation in the Detection of Brain Metastases

Bae YJ, Choi BS, Lee KM, Yoon YH, Sunwoo L, Jung C, Kim JH

OBJECTIVE: To evaluate the diagnostic benefits of 5-mm maximum intensity projection of improved motion-sensitized driven-equilibrium prepared contrast-enhanced 3D T1-weighted turbo-spin echo imaging (MIP iMSDE-TSE) in the detection of brain metastases....
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