Frontiers in radiology最新文献

Introduction: Understanding the role of various brain regions of interest (ROIs) in various cognitive functions or tasks, across healthy or neurodegenerative conditions and multiple degrees of separation, remains a key challenge in neuroscience. Conventional network measures can only capture localized or quasi-localized features of brain ROIs. Topological data analysis (TDA), particularly persistent homology, provides a threshold-free, mathematically rigorous framework for identifying topologically salient features in complex networks. In this paper, we introduce a new metric, the Homological Vertex Importance Profile (H-VIP), designed to assess the relevance of vertices that participate in persistent topological structures (e.g., connected components, cycles or cavities) in brain networks. The H-VIP quantifies the topological features of the network at the ROI (node) level by compressing its higher-order connectivity profile using homological constructs.

Methods: Leveraging homological constructs of brain connectomes, we extend two of our previously defined network-level measures-average persistence and persistence entropy-to an ROI-level measure, i.e., the H-VIP. We then applied the H-VIP to two independent datasets: structural connectomes from the Human Connectome Project and functional connectomes from the Alzheimer's Disease Neuroimaging Initiative. Persistent homology was computed for each network, and H-VIP scores were derived to evaluate vertex-level contributions. Finally, H-VIP scores were used for the prediction of multiple cognitive measures.

Results: In both anatomical and functional brain networks, H-VIP values demonstrate predictive power for various cognitive measures. Notably, the connectivity of the frontal lobe exhibited stronger correlations with cognitive performance than the whole-brain network.

Discussion: H-VIP offers a robust and interpretable means to locate, quantify, and visualize region-specific contributions to network's topological, higher-order landscape. Its ability to detect potentially impaired connectivity at the individual level suggests possible applications in personalized medicine for neurological diseases and disorders. Beyond brain connectomics, the H-VIP can be used for other types of complex networks where topological features are of importance, such as financial, social, or ecological networks.

Introduction: Motion artifacts induced by atrial fibrillation (AF) present a substantial challenge in coronary computed tomography angiography (CCTA). Wide detectors, rapid scanning, and motion correction algorithms can effectively improve image quality in CCTA. This study aims to evaluate the impact of one-beat acquisition with a motion correction algorithm (Snapshot Freeze 1, SSF1) on the image quality of prospective CCTA in patients with AF, and its diagnostic performance using an artificial intelligence assisted diagnostic system (AI-ADS).

Materials and methods: A total of 91 consecutive patients with AF, who underwent one-beat CCTA were analyzed. Images were reconstructed with SSF1. The subjective and objective image quality of the coronary arteries were evaluated. Using the invasive coronary catheter angiography (ICA) as the reference standard, the diagnostic performance of AI-ADS and AI-ADS + radiologist for stenoses above moderate and severe degrees were calculated.

Results: Effective radiation dose was 2.43 ± 0.88 mSv. The average CT values of all major coronary arteries and branches were greater than 400 HU. All vessels were diagnosable (scores ≥ 3) with good or above ratings at 96.15% (350/364) and 96.70% (352/364). The diagnostic accuracy, sensitivity, specificity and AUC of AI-ADS vs. AI-ADS + radiologist for above moderate stenoses were: (84.62% vs. 91.21%), (89.61% vs. 98.70%), (57.14% vs. 50.00%) and (0.73 vs. 0.74) on patient level; (84.07% vs. 87.64%), (74.07% vs. 85.19%), (89.96% vs. 89.08%) and (0.82 vs. 0.87) on vessel level; (90.84% vs. 93.11%), (63.59% vs. 78.34%), (95.99% vs. 95.91%) and (0.80 vs. 0.87) on segment level. For severe stenoses, these values were: (62.64% vs. 82.42%), (58.62% vs. 91.38%), (69.70% vs. 66.67%) and (0.64 vs. 0.79) on patient level; (82.97% vs. 89.29%), (46.43% vs. 75.00%), (93.93% vs. 93.57%) and (0.70 vs. 0.84) on vessel level; (92.23% vs. 95.16%), (36.92% vs. 66.92%), (98.06% vs. 98.14%) and (0.68 vs. 0.83) on segment level.

Conclusion: One-beat CCTA with SSF1 provides high-quality coronary images for patients with AF. AI-ADS automatically distinguishes coronary images with different stenoses, but the sensitivity of AI-ADS is low, especially for severe stenoses. AI-ADS + radiologist further improves the diagnostic performance.

Background: Aortic stenosis (AS) is diagnosed by echocardiography, the current gold standard, but examinations are often performed only after symptoms emerge, highlighting the need for earlier detection. Recently, artificial intelligence (AI)-based screening using non-invasive and widely available modalities such as electrocardiography (ECG) and chest x-ray(CXR) has gained increasing attention for valvular heart disease. However, single-modality approaches have inherent limitations, and in clinical practice, multimodality assessment is common. In this study, we developed a multimodal AI model integrating ECG and CXR within a cooperative learning framework to evaluate its utility for earlier detection of AS.

Methods: We retrospectively analyzed 23,886 patient records from 7,483 patients who underwent ECG, CXR, and echocardiography. A multimodal model was developed by combining a 1D ResNet50-Transformer architecture for ECG data with an EfficientNet-based architecture for CXR. Cooperative learning was implemented using a loss function that allowed the ECG and CXR models to refine each other's predictions. We split the dataset into training, validation, and test sets, and performed 1,000 bootstrap iterations to assess model stability. AS was defined echocardiographically as peak velocity ≥2.5 m/s, mean pressure gradient ≥20 mmHg, or aortic valve area ≤1.5 cm².

Results: Among 7,483 patients, 608 (8.1%) were diagnosed with AS. The multimodal model achieved a test AUROC of 0.812 (95% CI: 0.792-0.832), outperforming the ECG model (0.775, 95% CI: 0.753-0.796) and the CXR model (0.755, 95% CI: 0.732-0.777). Visualization techniques (Grad-CAM, Transformer attention) highlighted distinct yet complementary features in AS patients.

Conclusions: The multimodal AI model via cooperative learning outperformed single-modality methods in AS detection and may aid earlier diagnosis and reduce clinical burden.

[This corrects the article DOI: 10.3389/fradi.2025.1509377.].

Mucormycosis is a serious fungal infection affecting immunocompromised individuals, caused by fungi from the Mucorales order, particularly Rhizopus species. It primarily spreads through inhalation of spores, with diabetes, cancers, organ transplants, immunosuppressive drugs, and COVID-19 being major risk factors. The infection manifests in various forms such as encephalic, cutaneous, gastrointestinal, pulmonary, and rhino cerebral, often leading to tissue necrosis and blood vessel invasion. Imaging diagnosis is aided by CT and MRI scans, while 99m Tc MDP bone scintigraphy has found to be a more accurate imaging tool to look for bone remodelling and erosive changes associated with invasive fungal sinusitis including mucormycosis. Treatment involves prompt surgical debridement and addressing the underlying immune deficiency. Here we present a series of cases where 99m Tc MDP bone scintigraphy played a key role in management of mucormycosis of the head. In conclusion, 99mTc MDP scintigraphy is a promising tool for evaluation, guiding diagnosis and management of mucormycosis.

Background: Tunneled cuffed catheter (TCC) remains a crucial vascular access option for patients undergoing hemodialysis, particularly in those who are not candidates for arteriovenous fistulas or grafts. However, placement carries immediate and delayed complications.

Objective: This narrative review aims to provide a comprehensive overview of the complications encountered during and after the placement of a TCC for hemodialysis, highlighting current evidence, risk factors, prevention strategies, and management approaches.

Methods: A critical selection of relevant literature was performed through PubMed and Scopus databases, focusing on articles published in the last two decades. Particular attention was given to studies reporting on mechanical, infectious, thrombotic, and late-onset complications, as well as technical factors influencing outcomes.

Results: Complications of TCCs can be classified as immediate (e.g., arterial puncture, pneumothorax, bleeding), early (e.g., catheter malposition, exit-site infections), and late (e.g., central venous stenosis, catheter-related bloodstream infections, thrombosis). Patient- and procedure-related factors increase risk. Ultrasound and fluoroscopy, strict sterility, and timely management reduce complications rates.

Conclusion: TCCs are indispensable in selected patients, but understanding their complications is key to patient safety and outcomes. Optimal outcomes depend on accurate patient selection, operator expertise, and standardized post-placement care.

Purpose: This study aimed to assess the feasibility and accuracy of fiducial marker placement using US-CT/MRI fusion imaging guidance in poorly conspicuous liver tumors prior to percutaneous thermal ablation (PTA).

Method: From January 2016 to February 2018, 30 consecutive patients with 38 liver lesions that were poorly or not visible on conventional ultrasound underwent fiducial marker placement under real-time US-CT/MRI fusion imaging before the PTA procedure. Marker position was confirmed via CT or MRI immediately after placement. The shortest distance between the marker and the edge of the target lesion, the lesion size, and the depth were measured. The fiducial marker placement was considered successful if the marker was within, in contact or ≤5 mm distance from the lesion; a distance >5 mm was considered a failure.

Results: Of the 38 lesions, 28 (74%) were undetectable using ultrasound alone, while 10 (26%) were not confidently identified. After fusion, 26 lesions (68%) showed enhanced visibility, while 12 (32%) remained undetectable. Overall, the mean distance between the fiducial marker and the lesion's edge was 4 mm (range: 0-45 mm). Successful placement was achieved in 30 lesions (79%): 27, inside or in contact, and 3, at a <5 mm distance from the target lesion. Placement was unsuccessful in eight lesions (21%). No procedure-related complications occurred.

Conclusions: The present work suggests that pre-PTA placement of a fiducial marker in poorly visible tumors using real-time US-CT/MRI fusion imaging is accurate, potentially enhancing the effectiveness of subsequent PTA.

Introduction: Vietnam still faces a high burden of infectious diseases compared with developed countries, and improving its health and sanitation environment is essential for addressing both infectious and non-communicable diseases. Chest radiography is key for early detection of cardiopulmonary diseases. Artificial Intelligence (AI) research on detecting cardiopulmonary diseases from chest radiographs has advanced; however, no AI development studies have used Vietnamese data, despite its high burden of both disease types, for early detection. Therefore, we aimed to develop an AI model to classify normal and abnormal images using a Vietnamese chest radiograph dataset.

Methods: We retrospectively analyzed 12,827 normal and 4,644 abnormal cases from two Vietnamese institutions. Features were derived from principal component analysis and extracted using Vision Transformer and EfficientnetV2. We performed binary classification of normal and abnormal images using Light Gradient Boosting Machine with 5-fold cross-validation.

Results: The model achieved an F1-score of 0.668, sensitivity of 0.596, specificity of 0.931, accuracy of 0.842, and AUC of 0.897. Subgroup evaluation revealed high accuracy in both infectious and non-communicable cases, as well as in urgent cases.

Conclusion: We developed an AI system that classifies normal and abnormal chest radiographs with high clinical accuracy using Vietnamese data.

Erectile dysfunction (ED) is a worldwide health concern and clinical condition for men, leading to high medical costs and imposing significant emotional and psychological burdens on sufferers annually. ED is associated with multiple causes, including psychological factors and organic issues such as arterial insufficiency and venous leakage. Endovascular treatments have emerged as promising options for managing ED, offering minimally invasive procedures that can improve blood flow to the penis and restore erectile function. Different endovascular interventional approaches have been implemented with varying success rates and therapeutic impacts, and efforts continue to optimize these methods (both arterial and venous) for maximum effectiveness and minimal invasiveness. This narrative review aims to provide an overview of endovascular treatments for arterial and venous types of ED, discussing their mechanisms of action, efficacy, safety, and future directions.