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Classification of twinkling artifacts and blood flow for in vivo detection of breast microcalcifications

2026-03-16T04:50:02Z

Title: Classification of twinkling artifacts and blood flow for in vivo detection of breast microcalcifications Authors: Kang, Jinbum; Park, Seongjun; Lee, Eonho; Cho, Hyunwoo; Kim, Kangsik; Kim, Min Jung; Yoo, Yangmo Abstract: While mammography is the standard modality for detecting microcalcifications (MCs), their real-time detection with ultrasound imaging can be invaluable, particularly for guiding biopsies. Ultrasound twinkling artifact (TA) imaging allows the sensitive distinction of MCs from background breast tissue; however, it may also be confounded with blood flow in Doppler mode during in vivo scanning. In this paper, we propose a new MC imaging method that classifies TA and blood flow signals to enable in vivo detection of breast MCs. Based on the signal characteristics of TA and blood flow, two optimal features (i.e., mean frequency and spectrum bandwidth) are extracted and used to train a machine learning classifier. To train the classification model, tissue-mimicking and chicken breast phantom containing normal wire (285 mu m in diameter), MC wire (300 mu m in diameter) and micro-vessel tube (1 mm in diameter) were fabricated, and training and validation datasets were acquired under varying flow velocities and pulse repetition frequencies (PRFs). Among the four classifiers, i.e., k-nearest neighbors (KNN), support vector machine (SVM), na & iuml;ve Bayes and quadratic discriminant, trained with the two optimal features, the SVM achieved the highest accuracy (95.25 %), whereas the remaining models also exhibited strong performance with accuracies exceeding 92 %. The trained SVM model was then validated on a chicken breast MC phantom and in vivo human breast data, and they showed good agreement with color Doppler imaging. The feasibility study demonstrated that the proposed classification approach may enable effective in vivo detection and improve diagnostic accuracy, especially in cases with complex flow patterns in breast lesions.

HP-GAN: Harnessing pretrained networks for GAN improvement with FakeTwins and discriminator consistency

2026-03-16T07:18:00Z

Title: HP-GAN: Harnessing pretrained networks for GAN improvement with FakeTwins and discriminator consistency Authors: Son, Geonhui; Lee, Jeong Ryong; Hwang, Dosik Abstract: Generative Adversarial Networks (GANs) have made significant progress in enhancing the quality of image synthesis. Recent methods frequently leverage pretrained networks to calculate perceptual losses or utilize pretrained feature spaces. In this paper, we extend the capabilities of pretrained networks by incorporating innovative self-supervised learning techniques and enforcing consistency between discriminators during GAN training. Our proposed method, named HP-GAN, effectively exploits neural network priors through two primary strategies: FakeTwins and discriminator consistency. FakeTwins leverages pretrained networks as encoders to compute a self-supervised loss and applies this through the generated images to train the generator, thereby enabling the generation of more diverse and high quality images. Additionally, we introduce a consistency mechanism between discriminators that evaluate feature maps extracted from Convolutional Neural Network (CNN) and Vision Transformer (ViT) feature networks. Discriminator consistency promotes coherent learning among discriminators and enhances training robustness by aligning their assessments of image quality. Our extensive evaluation across seventeen datasets-including scenarios with large, small, and limited data, and covering a variety of image domains-demonstrates that HP-GAN consistently outperforms current state-of-the-art methods in terms of Fr & eacute;chet Inception Distance (FID), achieving significant improvements in image diversity and quality. Code is available at: https://github.com/higun2/HP-GAN.

Association between epilepsy duration and glymphatic dysfunction assessed by DTI-ALPS: A systematic review and meta-analysis

2026-06-09T07:49:01Z

Title: Association between epilepsy duration and glymphatic dysfunction assessed by DTI-ALPS: A systematic review and meta-analysis Authors: Lee, Su Ji; Cho, Soomi; Shin, Hui Jin; Lee, Hyunji; Cho, Minjae; 신희진 Abstract: Objective: To systematically evaluate whether epilepsy duration is associated with glymphatic dysfunction as measured by diffusion tensor image analysis along the perivascular space (DTI-ALPS). Methods: A systematic review and correlation-based meta-analysis were conducted in accordance with PRISMA guidelines. PubMed, Embase, Scopus, Web of Science, and Google Scholar were searched from inception through January 20, 2026, for observational studies reporting correlations between epilepsy duration and DTI-ALPS values. Correlation coefficients were pooled using random-effects models after Fisher's z transformation. Subgroup analyses and meta-regression were performed to explore heterogeneity. Results: Ten observational studies comprising 449 patients with epilepsy were included. Pooled analysis demonstrated a significant negative association between epilepsy duration and the DTI-ALPS index (r = -0.37, 95% confidence interval [CI]: -0.53 to -0.19), indicating lower glymphatic function with longer disease duration. A significant association persisted in temporal lobe epilepsy (r = -0.30, 95% CI: -0.54 to -0.02) and was stronger in late-onset epilepsy (r = -0.68, 95% CI: -0.79 to -0.54). Meta-regression identified age as a significant moderator of effect size, whereas mean disease duration did not significantly explain variability. Sensitivity analyses confirmed the robustness of findings, and no publication bias was detected. Conclusion: Longer epilepsy duration is associated with greater glymphatic dysfunction as measured by DTIALPS. Age significantly modulates this relationship, suggesting that seizure chronicity and aging-related vulnerability may synergistically influence perivascular clearance pathways. These findings support DTI-ALPS as a promising non-invasive marker of cumulative glymphatic burden in epilepsy and provide a quantitative framework for future longitudinal studies.

Uterine Artery Embolization for Pure Adenomyosis: Predictive Factors Affecting Outcomes

2026-04-14T07:23:13Z

Title: Uterine Artery Embolization for Pure Adenomyosis: Predictive Factors Affecting Outcomes Authors: Han, Kichang; Kim, Man-Deuk; Kwon, Joon Ho; Seo, Seok Kyo; Alqarni, Abdullah Ali; Park, Juil; Kim, Gyoung Min; Won, Jong Yun; Cho, Jaesung; Jeong, Seok Min Abstract: Purpose: To identify factors associated with postprocedural necrosis after uterine artery embolization (UAE) for pure adenomyosis. Materials and Methods: This study included patients who underwent UAE for pure symptomatic adenomyosis between January 2011 and May 2025. Adenomyosis characteristics, including T2-weighted signal intensity, adenomyosis morphology (Types I and II), and focal versus diffuse location, were evaluated using preprocedural magnetic resonance (MR) imaging. Contrast-enhanced MR imaging was used to assess adenomyosis necrosis 3 months after UAE. Symptom severity scores (SSSs) and health-related quality of life (HRQOL) were evaluated before and 3 months after the procedure. Univariate and multivariate analyses were performed to identify factors associated with incomplete necrosis of the adenomyotic tissue. Results: Of the 147 patients (mean age, 42.7 years [SD +/- 4.2]) who underwent UAE for adenomyosis, 116 (78.9%) exhibited complete necrosis. In multivariate analysis, Type II adenomyosis (odds ratio [OR], 10.492; 95% CI, 3.492-31.523; P < .001) and heterogeneous T2 signal intensity (OR, 4.003; 95% CI, 1.565-10.242; P = .003) were significant predictive factors for incomplete necrosis. The rates of incomplete necrosis were 13.6% (17/125) for Type I adenomyosis and 63.6% (14/22) for Type II adenomyosis. The postprocedural SSS and HRQOL scores were significantly improved in patients with complete necrosis compared with those with incomplete necrosis. Conclusions: Type II morphology arising from the subserosa and a heterogeneous T2 signal are associated with an increased risk of incomplete necrosis after UAE. Incorporating these features into preprocedural counseling may help improve clinical outcomes.