Current Medical Imaging Reviews最新文献

Purpose: This study aimed to assess the hemodynamic changes in the vena cava and predict the likelihood of Cardiac Remodeling (CR) and Myocardial Fibrosis (MF) in athletes utilizing four-dimensional (4D) parameters.

Materials and methods: A total of 108 athletes and 29 healthy sedentary controls were prospectively recruited and underwent Cardiac Magnetic Resonance (CMR) scanning. The 4D flow parameters, including both general and advanced parameters of four planes for the Superior Vena Cava (SVC) and Inferior Vena Cava (IVC) (sheets 1-4), were measured and compared between the different groups. Four machine learning models were employed to predict the occurrence of CR and/or MF.

Results: Most general 4D flow parameters related to VC were increased in athletes and positive athletes compared to controls (p < 0.05). Gradient Boosting Machine (GBM) was the most effective model in sheet 2 of SVC, with the area under the curve values of 0.891, accuracy of 85.2%, sensitivity of 84.6%, and specificity of 85.4%. The top five predictors in descending order were as follows: net positive volume, forward volume, waist circumference, body weight, and body surface area.

Conclusion: Physical activity can induce a high flow state in the vena cava. CR and/or MF may elevate the peak velocity and maximum pressure gradient of the IVC. This study successfully constructed a GBM model with high efficacy for predicting CR and/or MF. This model may provide guidance on the frequency of follow-up and the development of appropriate exercise plans for athletes.

Background: Multiple sclerosis (MS) is one of the most common disabling central nervous system diseases affecting young adults. Magnetic resonance imaging (MRI) is an essential tool for diagnosing and following up multiple sclerosis. Over the years, many MRI techniques have been developed to improve the sensitivity of MS disease detection. In recent years synthetic MRI (sMRI) and quantitative MRI (qMRI) have gained traction in neuroimaging applications, providing more detailed information than traditional acquisition methods. These techniques enable the detection of microstructural changes in the brain with high sensitivity and robustness to inter-scanner and inter-observer variability. This study aims to evaluate the feasibility of using these techniques to avoid administering intravenous gadolinium-based contrast agents (GBCAs) for assessing MS disease activity and monitoring.

Materials and methods: Forty-two known MS patients, aged 20 to 45, were scanned as part of their routine follow-up. MAGnetic resonance image Compilation (MAGiC) sequence, an implementation of synthetic MRI, was added to our institute's routine MS protocol to automatically generate quantitative maps of T1, T2, and PD. T1, T2, PD, and apparent diffusion coefficient (ADC) data were collected from regions of interest (ROIs) representing normalappearing white matter (NAWM), enhancing, and non-enhancing MS lesions. The extracted information was compared, and statistically analyzed, and the sensitivity and specificity were calculated.

Results: The mean R1 (the reciprocal of T1) value of the non-enhancing MS lesions was 0.694 s-1 (T1=1440 ms), for enhancing lesions 1.015 s-1 (T1=985ms), and for NAWM 1.514 s-1 (T1=660ms). For R2 (the reciprocal of T2) values, the mean value was 6.816 s-1 (T2=146ms) for nonenhancing lesions, 8.944 s-1 (T2=112 ms) for enhancing lesions, and 1.916 s-1 (T2=522 ms) for NAWM. PD values averaged 93.069% for nonenhancing lesions, 82.260% for enhancing lesions, and 67.191% for NAWM. For ADC, the mean value for non-enhancing lesions was 1216.60×10-6 mm2/s, for enhancing lesions 1016.66×10-6 mm2/s, and for NAWM 770.51×10-6 mm2/s.

Discussion: Our results indicate that enhancing and non-enhancing MS lesions significantly decrease R1 and R2 values. Non-enhancing lesions have significantly lower R1 and R2 values compared to enhancing lesions.

Conclusion: Conversely, PD values are significantly higher in non-enhancing lesions than in enhancing lesions. For ADC, while NAWM has lower values, there was minimal difference between the mean ADC values of enhancing and non-enhancing lesions.

Background: Cytomegalovirus (CMV) infection is common in the digestive and central nervous systems and can infect the entire digestive tract from the mouth to the rectum. In immunocompromised patients, CMV infection is prone to develop into CMV disease, especially in Acquired Immune Deficiency Syndrome (AIDS) patients. Severe cases may accelerate the progression of AIDS patients and form systemic CMV infection. Timely diagnosis and treatment are very important for the prognosis of patients.

Case presentation: In this paper, we report a 36-year-old man with a Human Immunodeficiency Virus (HIV) infection complicated with CMV colitis. Three weeks ago, he developed abdominal pain with fresh blood in the stool, accompanied by anal pain. He was found to be HIV positive 8 years ago. An enhanced CT scan showed edema and irregular thickening of the rectal wall, obvious enhancement of the mucosa, and multiple enlarged lymph nodes around. 18F-FDG PET/CT imaging displayed diffuse rectum wall thickening and increased glucose metabolism, and the SUV max was 12.7. There were multiple enlarged lymph nodes around the rectum, glucose metabolism was increased, and the SUVmax was 4.6.

Conclusion: 18F-FDG-PET imaging technology has potential value in the diagnosis of CMV colitis, especially in immunocompromised patients. Detection of FDG concentrations in the colon wall can help diagnose CMV infection and understand the extent of the lesion, which is essential for the timely initiation of antiviral therapy.

Background: Primary thoracic lymphangioma is a rare disease. Most of the previous studies are comprised of individual case reports, with a very limited number of patients included.

Objective: This study aims to investigate the chest computed tomography (CT) imaging features and clinical manifestations of thoracic lymphangioma, thereby enhancing our understanding of the condition.

Methods: A retrospective analysis was conducted on 62 patients diagnosed with thoracic lymphangioma, comprising 32 males and 30 females. The study focused on analyzing the chest CT imaging features and the clinical manifestations observed in these patients.

Results: The incidence rates of thoracic lymphangioma did not differ significantly between males and females; however, it was more frequently observed in children and adolescents. The most common clinical symptoms included cough, fever, chylothorax, chylous pericardium, and lymphedema. The mediastinum (82.3%) emerged as the most frequent location for thoracic lymphangioma, followed by the chest wall (62.9%), bone (40.3%), and pleura (32.3%). Pulmonary lymphangioma, the least prevalent subtype (19.4%), exhibited a propensity to induce respiratory symptoms, frequently manifesting as a generalized lymphatic anomaly (GLA). Furthermore, elevated levels of D-dimer were detected in 34 patients (54.8%) with thoracic lymphangioma.

Conclusions: Imaging examinations play a crucial role in assisting clinicians in making more accurate early diagnoses of thoracic lymphangioma. They are also helpful for assessing the extent of systemic infiltration and enhancing diagnostic precision. With radiological assessment, clinicians could more readily select appropriate therapeutic treatments and monitor the progression of follow-up care.

Background: Leptomeningeal enhancement, visible on MRI, can indicate a variety of diseases, both neoplastic and non-neoplastic.

Objective: This comprehensive pictorial review aims to equip radiologists and trainees with a thorough understanding of the diverse imaging presentations of leptomeningeal disease.

Methods: Drawing from a retrospective analysis of MRI scans conducted between 1 January 2008 and 30 September 2022, at two tertiary teaching hospitals in Singapore, this review covers a wide range of conditions. Case Collection: The main neoplastic conditions discussed include leptomeningeal carcinomatosis, myelomatosis, schwannoma, CNS lymphoma, and pineal region tumors. Additionally, the review addresses non-neoplastic enhancements such as meningoencephalitis, intracranial hypotension, cerebral ischemia/infarction, epidural lipomatosis, syringomyelia, Sturge-Weber syndrome, and subarachnoid hemorrhage.

Conclusion: By highlighting the imaging features and patterns associated with these conditions, the review underscores the critical role of accurate diagnosis and timely management in improving patient outcomes. Enhanced understanding of these conditions can significantly improve patient outcomes through timely and effective therapeutic interventions.

Aims: The aim of this study was to evaluate renal artery embolization in patients with spontaneous renal artery bleeding based on detailed angiographic findings and a comprehensive analysis of its efficacy and clinical outcomes.

Materials and methods: This retrospective study evaluated the outcomes of renal artery embolization in 18 cases among 15 patients (11 men and 4 women; mean age: 57.9 years) treated for spontaneous renal bleeding at our institution between March 2017 and October 2023. Data derived from abdominal computed tomography (CT) and arteriography were analyzed to assess the effectiveness of embolization.

Results: Most patients had end-stage renal disease or renal atrophy, with common findings on CT scans, including signs of active bleeding in 66.7% (10/15) and hematoma extending to the retroperitoneal space in 53.3% (8/15). Microcoils were commonly used for embolization (n = 10), with a technical success rate of 100% and primary and final clinical success rates of 80% and 100%, respectively. No major complications were reported during the follow-up, and clinical improvement was observed in all patients who underwent total embolization, with few instances of reduced hematoma size and renal atrophy.

Conclusion: Transarterial embolization is safe and effective for controlling spontaneous renal hemorrhage.

Background and objective: Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating early and accurate detection methods. Our study aims to enhance lung cancer detection by integrating VGGNet-16 form of Convolutional Neural Networks (CNNs) and Support Vector Machines (SVM) into a hybrid model (SVMVGGNet-16), leveraging the strengths of both models for high accuracy and reliability in classifying lung cancer types in different 4 classes such as adenocarcinoma (ADC), large cell carcinoma (LCC), Normal, and squamous cell carcinoma (SCC).

Methods: Using the LIDC-IDRI dataset, we pre-processed images with a median filter and histogram equalization, segmented lung tumors through thresholding and edge detection, and extracted geometric features such as area, perimeter, eccentricity, compactness, and circularity. VGGNet-16 and SVM employed for feature extraction and classification, respectively. Performance matrices were evaluated using accuracy, AUC, recall, precision, and F1-score. Both VGGNet-16 and SVM underwent comparative analysis during the training, validation, and testing phases.

Results: The SVMVGGNet-16 model outperformed both, with a training accuracy (97.22%), AUC (99.42%), recall (94.22%), precision (95.28%), and F1- score (94.68%). In testing, our SVMVGGNet-16 model maintained high accuracy (96.72%), with an AUC (96.87%), recall (84.67%), precision (87.40%), and F1-score (85.73%).

Conclusion: Our experimental results demonstrate the potential of SVMVGGNet-16 in improving diagnostic performance, leading to earlier detection and better treatment outcomes. Future work includes refining the model, expanding datasets, conducting clinical trials, and integrating the system into clinical practice to ensure practical usability.

Objectives: The objective of this study was to summarize the findings of children's intracranial congenital or developmental malformations found during imaging procedures in the Tibetan plateau.

Methods: We retrospectively reviewed the imaging data of the suspected patients who presented with the central nervous system (CNS) malformations and were enrolled either through the clinic or after ultrasound examinations between June 2019 and June 2023 in our institution. All imaging data were interpreted by two experienced radiologists through consensus reading.

Results: In this study, we recruited 36 patients, including two neonates, 17 infants and 17 children. Seven cases underwent an MRI examination, while the others had a CT scan. Polygyria and pachygyria malformation were the most common type of congenital neurological malformations (7 cases, 31.8%), followed by cystic changes of the cerebral parenchyma (3 cases, 13.6%). Cerebral atrophy was the most common type of secondary CNS abnormality(8 cases, 57.1%), followed by communicative hydrocephalus (3 cases, 21.4%). Five patients in the congenital group and 4 patients in the secondary group had complex malformations. In the current study group, there were 8 deaths, 12 cases with neurological sequelae, 1 case with normal development, and 15 cases lost to follow-up. There were no significant differences between the primary and secondary CNS groups in terms of the outcome for both the infants and children groups.

Conclusions: CNS malformations in the Tibetan Plateau are associated with high mortality and morbidity rates. Better utilization of imaging modalities could help design tailored treatments as early as possible.

Objective: The study aims to evaluate the clinical efficacy of CT-guided radiofrequency ablation in conjunction with targeted therapy in lung cancer patients.

Method: We retrospectively analyzed 80 lung cancer patients. They were stratified into the Observation Group (OG, n=40, treated with CT-guided radiofrequency ablation in conjunction with targeted therapy) and the Control Group (CG, n=40, treated solely with targeted therapy).

Results: The OG group reported 4 cases of Complete Response (CR), 24 cases of Partial Response (PR), 10 cases of Stable Disease (SD), and 2 cases of Progressive Disease (PD). The Overall Response Rate (ORR) was 70.00% (28/40), and the Disease Control Rate (DCR) was 95.00% (38/40). In contrast, the CG group exhibited 3 cases of CR, 20 cases of PR, 12 cases of SD, and 5 cases of PD. The ORR was 57.50% (23/40), and the DCR was 87.50% (35/40). The ORR and DCR in the OG group were significantly higher than those in the CG group. After 6 weeks of treatment, the levels of SCC, CEA, and CA125 in the OG group were significantly lower than those in the CG group; The CD4+ levels in the OG group were significantly higher and the CD8+ levels significantly lower than those in the CG group. A 24-month follow-up showed that the survival rate of the OG group was 47.50% (19/40), which was significantly higher than that of the CG group at 27.50% (11/40).

Conclusion: CT-guided radiofrequency ablation and targeted therapy have been proven effective in treating lung cancer.

Background: Early and timely detection of pulmonary nodules and initiation treatment can substantially improve the survival rate of lung carcinoma. However, current detection methods based on convolutional neural networks (CNNs) cannot easily detect pulmonary nodules owing to low detection accuracy and the difficulty in detecting small-sized pulmonary nodules; meanwhile, more accurate CNN-based models are slow and require high hardware specifications.

Objective: The aim of this study is to develop a detection model that achieves both high accuracy and real-time performance, ensuring effective and timely results.

Methods: In this study, based on YOLOv5s, a concentrated-comprehensive convolution (C3_ODC) module with multidimensional attention is designed in the convolutional layer of the original backbone network for enhancing the feature-extraction capabilities of the model. Moreover, lightweight convolution is combined with weighted bidirectional feature pyramid networks (BiFPNs) to form a GS-BiFPN structure that enhances the fusion of multiscale features while reducing the number of model parameters. Finally, Focal Loss is combined with the normalized Wasserstein distance (NWD) to optimize the loss function. Focal loss focuses on carcinoma-positive samples to mitigate class imbalance, whereas the NWD enhances the detection performance of small lung nodules.

Results: In comparison experiments against the YOLOv5s, the proposed model improved the average precision by 8.7% and reduced the number of parameters and floating-point operations by 5.4% and 8.2%, respectively, while achieving 116.7 frames per second.

Conclusion: The proposed model balances high detection accuracy against real-time requirements.