Min-Hee Lee

Background: Noise reduction in low-dose pediatric computed tomography (CT) requires effective suppression of quantum noise without compromising subtle anatomical structures. This study compared three bilateral-based noise reduction algorithms, namely the conventional bilateral filter (BF), adaptive BF (ABF), and trainable BF (TBF), using eight low-dose pediatric abdominal CT cases. Methods: Denoising performance was evaluated in the axial and coronal planes through visual assessment and quantitative measurements of the coefficient of variation (CV) within the liver parenchyma and the contrast-to-noise ratio (CNR) between the liver and portal vein. Statistical comparisons were performed using the Friedman test, followed by the Wilcoxon signed-rank tests with Holm-adjustment. All three methods reduced the visible noise present in the original images. Results: ABF improved uniformity in homogeneous liver regions while preserving anatomical detail, whereas TBF produced the strongest apparent noise suppression but introduced greater blurring and occasional ambiguous structures. Quantitatively, the CV decreased progressively from BF to ABF and TBF. The TBF method achieved the lowest average CV and the highest average CNR. Significant differences were observed among the four conditions for both metrics in each imaging plane. However, CNR differences between the three denoising methods were less consistent, particularly for comparisons involving TBF. Conclusions: Overall, ABF provided a favorable balance between noise reduction and structural preservation, whereas TBF offered stronger image specific denoising with additional concerns regarding signal fidelity and reproducibility. Further refinement is required to ensure clinical reliability without compromising quantitative accuracy or anatomical details.

Background and ObjectivesCarotid plaque reflects systemic atherosclerosis and may serve as an early marker of cognitive decline, but its longitudinal association with cognitive trajectories remains...

Background: Diverse factors including seizure onset age, seizure frequency, epilepsy duration, total number of antiseizure medications trialed are considered as seizures-related neurocognitive loads in children with drug-resistant focal epilepsy (DRE). However, their associations with the structural integrity of neurocognitive networks remain largely unknown.Purpose: This study investigates a novel diffusion-weighted imaging (DWI) connectome methodology that can extract seizure-associated structural abnormality biomarkers from clinical DWI tractography, use them to classify neurocognitive impairments prior to surgery, and unveil the relationship between epilepsy-related factors and neurocognitive impairments.Methods: Thirty-three DRE children (age: 11.8±3.3 years, 17 boys) and 29 age-matched healthy controls were enrolled to create seizure-affected networks whose edges connect epileptogenic regions to key brain regions of 6 neurocognitive networks. The deviations of local efficiency values were averaged across the seizure-affected brain regions and used as new imaging-based biomarkers quantifying the degrees of seizure-associated structural abnormalities accumulated on individual neurocognitive networks and classifying the neurocognitive impairments along with the epilepsy-related factors.Results: Effect sizes of the proposed biomarkers for differentiating DRE from healthy controls were consistently very large across various subgroups defined by lesion types, lobar locations of epileptogenic foci, seizure frequency categories, and seizure types (i.e., Cohen d value >1.8). Compared with the epilepsy-related factors, the proposed biomarkers demonstrated superior classification accuracy for identifying neurocognitive impairments in general, verbal, and nonverbal domains. When combined with the epilepsy-related factors, the classification performance further improved, achieving an accuracy range of 90%–98% in the independent test patients. The subsequent association analysis using the proposed biomarkers as seizure-associated structural abnormality indicators demonstrated that the inclusion of such imaging indicators significantly enhances the strength of associations between epilepsy factors and neurocognitive impairments.Conclusion: These findings offer strong potential for objectively identifying neurocognitive impairments in DRE children, supporting early, data-driven decisions for personalized interventions to mitigate long-term effects.

Rationale: Obstructive sleep apnea (OSA) may impair perivascular diffusivity, a proposed glymphatic biomarker, contributing to cognitive decline. Objectives: To investigate the longitudinal impact of OSA on cognitive performance and perivascular diffusivity measured by diffusion tensor imaging along the perivascular space (DTI-ALPS) and whether changes in diffusivity mediate the relationship between OSA and cognition. Methods: In a community-based cohort, 1,110 participants underwent polysomnography, brain DTI, and cognitive assessments at baseline and 4-year follow-up. Participants were categorized by apnea–hypopnea index (AHI) at baseline and further grouped longitudinally as OSA-free, improved OSA, stable OSA, or worsened OSA. DTI-ALPS index was calculated at both time points to assess perivascular diffusivity. Multivariable models were adjusted for age, sex, education, body mass index, smoking, alcohol use, hypertension, diabetes, total sleep time, and sleep efficiency. Longitudinal models (i.e., Δ-values) also included baseline outcome values. Measurements and Main Results: Higher AHI was consistently associated with lower DTI-ALPS index. Each 1–event-per-hour increase in AHI corresponded to a 0.008 decrease in DTI-ALPS index at baseline and a 0.007 decrease over 4 years (both P < 0.001), with the worsened OSA group showing the greatest reduction. Increases in DTI-ALPS index were associated with improvements in visual memory, including immediate recall (β = 0.401), delayed recall (β = 0.351), and recognition (β = 0.145; all P < 0.001). ΔDTI-ALPS mediated the relationship between ΔAHI and changes in immediate visual recall (β = −0.015), delayed recall (β = −0.021), and recognition (β = −0.016; all P < 0.001). Conclusions: OSA severity was associated with altered perivascular diffusivity and related changes in memory, suggesting a potential pathway linking OSA to cognitive impairment via perivascular fluid dynamics.

Objective: To develop a novel deep‐learning model of clinical DWI tractography that can accurately predict the general assessment of epilepsy severity (GASE) in pediatric drug‐resistant epilepsy (DRE) and test if it can screen diverse neurocognitive impairments identified through neuropsychological assessments. Methods: DRE children and age‐sex‐matched healthy controls were enrolled to construct an epilepsy severity network ( ESN ), whose edges were significantly correlated with GASE scores of DRE children. An ESN ‐based biomarker called the predicted GASE score was obtained using dilated deep convolutional neural network with a relational network (dilated DCNN + RN ) and used to quantify the risk of neurocognitive impairments using global/verbal/non‐verbal neuropsychological assessments of 36/37/32 children performed on average 3.2 ± 2.7 months prior to the MRI scan. To warrant the generalizability, the proposed biomarker was trained and evaluated using separate development and independent test sets, with the random score learning experiment included to assess potential overfitting. Results: The dilated DCNN + RN outperformed other state‐of‐the art methods to create the predicted GASE scores with significant correlation ( r = 0.92 and 0.83 for development and test sets with clinical GASE scores) and minimal overfitting ( r = −0.25 and 0.00 for development and test sets with random GASE scores). Both univariate and multivariate models demonstrated that compared with the clinical GASE scores, the predicted GASE scores provide better model fit and discriminatory ability, suggesting more adjusted and accurate estimate of epilepsy severity contributing to the overall risk. Interpretation: The proposed biomarker shows strong potential for early identification of DRE children at risk of neurocognitive impairments, enabling timely, personalized interventions to prevent long‐term effects.

Objective: To develop an innovative deep convolutional neural network (DCNN)-based tract classification to enhance the prediction of short-term postoperative language improvement using axonal connectivity markers derived from specific language modular networks (LMNs) within the preoperative whole-brain diffusion-weighted imaging connectome (wDWIC). Methods: We employed a three-step approach. First, our previous DCNN-based tract classification to detect true-positive eloquent tracts was extended using an open-source database of high-quality wDWIC to facilitate the accurate classification of true-positive tracts within the preoperative backbone wDWIC of individual patients. Next, we applied psychometry-driven DWIC analysis to the resulting DCNN-based backbone wDWIC in order to create core, expressive, and receptive LMNs. Finally, graph and circuit theory-based connectivity markers were assessed within the three LMNs and compared using a series of machine learning algorithms to predict the presence of postoperative language improvement from a given LMN. Results: The results showed that the extended DCNN tract classification significantly improved the reproducibility of connectivity markers by up to 35.5$\%$ of F-statistics across different LMNs. The prediction accuracy increased by up to 40$\%$ across different machine learning algorithms. Notably, the best algorithm achieved the accuracy of 96$\%$/94$\%$/96$\%$ to predict the presence of language improvement about two months after surgery in core/expressive/receptive domain of an independent validation cohort. Conclusion: These domains hold great potential to assist physicians in identifying candidates whose language skills stand to benefit from early surgery. Significance: DCNN tract classification may be an effective tool to improve predicting short-term postoperative language improvement in pediatric epilepsy surgery.