황인평 교수

BACKGROUND: Detection of active pulmonary tuberculosis on chest radiographs (CRs) is critical for the diagnosis and screening of tuberculosis. An automated system may help streamline the tuberculosis screening process and improve diagnostic performance. METHODS: We developed a deep learning-based automatic detection (DLAD) algorithm using 54c221 normal CRs and 6768 CRs with active pulmonary tuberculosis that were labeled and annotated by 13 board-certified radiologists. The performance of DLAD w

Background Evaluation of the glymphatic system with intrathecal contrast material injection has limited clinical use. Purpose To investigate the feasibility of using serial intravenous contrast-enhanced T1 mapping in the quantitative evaluation of putative dynamic glymphatic activity in various brain regions and to demonstrate the effect of sleep on glymphatic activity in humans. Materials and Methods In this prospective study from May 2019 to February 2020, 25 healthy participants (mean age, 25

Computed tomography texture features such as higher entropy and lower homogeneity were significant differentiating factors of IPAs presenting as PGGNs larger than 5 mm and have potentials to enhance the differentiating performance.

We demonstrate, for the first time, Vertical-Transport Nanosheet (VTFET) CMOS logic transistors at sub-45nm gate pitch on bulk silicon wafers. We show that VTFETs present an opportunity to break the Contacted Gate Pitch (CGP) barrier faced by Lateral-Transport FETs. VTFETs offer scaling relief for electrostatics and parasitics by decoupling key device features from CGP-scaling roadblocks. First, nMOS/pMOS VTFET electrostatics are reported at sub-45nm gate pitch with <tex xmlns:mml="http://www.w3

Either breath-hold triple-echo MR imaging or MR spectroscopy can be used to detect substantial macrovesicular steatosis in living liver donor candidates. In the future, this may allow selective biopsy in candidates who are expected to have substantial macrovesicular steatosis on the basis of MR-based hepatic fat fraction.

Glioblastoma remains the most devastating brain tumor despite optimal treatment, because of the high rate of recurrence. Distant recurrence has distinct genomic alterations compared to local recurrence, which requires different treatment planning both in clinical practice and trials. To date, perfusion-weighted MRI has revealed that perfusional characteristics of tumor are associated with prognosis. However, not much research has focused on recurrence patterns in glioblastoma: namely, local and

We suggest that the ADCratio calculated from the ADC1000 and ADC2000 is a promising value for the differentiation of recurrent tumor and posttreatment changes in head and neck squamous cell carcinoma.

We developed and validated the "radiomics risk score" from the features of DCE MRI based on non-enhancing T2 hyperintense areas for risk stratification of patients with glioblastoma. It was associated with progression-free survival independently of IDH mutation status.

3T SWI can visualize putaminal atrophy and marked signal hypointensity in patients with parkinsonism-predominant multiple system atrophy with high specificity. Furthermore, it clearly demonstrates the dominant side of putaminal changes, which correlate with the contralateral symptomatic side of patients.

Background A superselective (SS) arterial spin labeling (ASL) MRI technique can be used to monitor the revascularization area as a supplementary or alternative modality to digital subtraction angiography (DSA), with the advantage of being noninvasive. Purpose To evaluate whether SS-ASL perfusion MRI could be used to visualize the revascularization area after combined direct and indirect bypass surgery in adults with moyamoya disease compared with DSA. Materials and Methods Patients diagnosed wit

PURPOSE: To predict hematoma growth in intracerebral hemorrhage patients by combining clinical findings with non-contrast CT imaging features analyzed through deep learning. METHODS: Three models were developed to predict hematoma expansion (HE) in 572 patients. We utilized multi-task learning for both hematoma segmentation and prediction of expansion: the Image-to-HE model processed hematoma slices, extracting features and computing a normalized DL score for HE prediction. The Clinical-to-HE mo