Tomography最新文献

Background/objectives: This study aimed to investigate the relationship between carotid artery anatomy and geometry and white matter hyperintensities (WMH) and to determine whether it is a risk factor for the disease.

Methods: The geometry and anatomy of both carotid arteries were evaluated with the three-dimensional vessel model obtained from the computed tomography angiography (CTA) data, and the segmentation software calculated the geometrical features of the arteries. In this model, vascular diameter, vascular cross-sectional area, carotid bifurcation and internal carotid artery (ICA) angles, as well as ICA tortuosity index (TI) measurements of the common carotid artery (CCA) and ICA were determined.

Results: Compared with the non-WMH group, increased carotid bifurcation and ICA angle and higher ICA TI values were found in the WMH group (p < 0.001). In multivariate regression analysis, increased carotid bifurcation angle, higher ICA TI values, age, hypertension, and stroke history were identified as independent risk factors for the development of WMH (p < 0.05). In addition, age, carotid bifurcation angles and ICA angles were found to be associated with the severity of WMH (p < 0.05).

Conclusions: Considering the vascular pathologies involved in the pathogenesis of WMH, identifying these risk factors may help determine individuals who are at an increased risk.

Background/Objectives: Diffusion-weighted imaging (DWI) is a magnetic resonance technique used to map the apparent diffusion coefficient (ADC) of water in human tissue. ADC assessment plays a central role in clinical diagnostics, as malignant tissues typically exhibit reduced water mobility and, thus, lower ADC values. Accurately measuring the ADC requires effective fat suppression to prevent contamination from the residual fat signal, which is commonly believed to cause ADC underestimation. This study aimed to demonstrate that ADC overestimation may occur as well. Methods: Our theoretical analysis shows that out-of-phase conditions between fat and water signals lead to ADC overestimations. We performed demonstration experiments on fat-water phantoms and the breasts of 10 healthy female volunteers. In particular, we considered three out-of-phase conditions: First and second, short-time inversion recovery (STIR) fat suppression with incorrect inversion time and incorrect flip angle, respectively. Third, phase differences due to spectral fat saturation. The ADC values were assessed in regions of interest (ROIs) that included both water and residual fat signals. Results: In the phantoms and the volunteer data, ROIs containing both fat and water signals consistently exhibited lower ADC values under in-phase conditions and higher ADC values under out-of-phase conditions. Conclusions: We demonstrated that out-of-phase conditions can result in ADC overestimation in the presence of residual fat signals, potentially resulting in false-negative classifications where malignant lesions are misinterpreted as benign due to an elevated ADC. Out-of-phase fat and water signals might also reduce lesion conspicuity in high b-value images, potentially masking clinically relevant findings.

Background: Pelvic floor dysfunction, more prevalent in women but affecting both genders, impairs sphincter control and sexual health, and causes pelvic pain. Pelvic floor muscle (PFM) training is the first-line treatment for urinary incontinence, supported by robust evidence. Rehabilitative ultrasound imaging (RUSI) serves as a visual biofeedback tool, providing real-time imaging to enhance PFM training, motor learning, and treatment adherence. Aim: This narrative review evaluates the role and efficacy of RUSI in pelvic floor rehabilitation. Method: A comprehensive search of PubMed, Cochrane, and MEDLINE was conducted using keywords related to pelvic floor rehabilitation, ultrasound, and biofeedback, limited to English-language publications up to July 2025. Systematic reviews, meta-analyses, and clinical trials were prioritized. Results: Transperineal and transabdominal ultrasound improve PFM function across diverse populations. In post-prostatectomy men, transperineal ultrasound-guided training enhanced PFM contraction and reduced urinary leakage. In postpartum women with pelvic girdle pain, transabdominal ultrasound-guided biofeedback combined with exercises decreased pain and improved function. Ultrasound-guided pelvic floor muscle contraction demonstrated superior performance compared to verbal instruction. Notably, 57% of participants who were unable to contract the pelvic floor muscles with verbal cues achieved a correct contraction with ultrasound biofeedback, and this approach also resulted in more sustained improvements in PFM strength. Compared to other biofeedback modalities, RUSI demonstrated outcomes that are comparable to or superior to those of alternative methods. However, evidence is limited by a lack of standardized protocols and randomized controlled trials comparing RUSI with other modalities. Conclusions: RUSI is an effective visual biofeedback tool that enhances outcomes of PFM training in pelvic floor rehabilitation. It supports clinical decision-making and patient engagement, particularly in cases where traditional assessments are challenging. Further research, including the development of standardized protocols and comparative trials, is necessary to optimize the clinical integration of this method and confirm its superiority over other biofeedback methods.

Background: The pterygomaxillary region is a complex anatomical area formed by the junction of the maxillary, palatine, and sphenoid bones and contains critical neurovascular structures. Accurate assessment of this region during Le Fort I osteotomy is essential, particularly to prevent hemorrhage and nerve injury that may occur during the pterygomaxillary separation phase. This study aims to investigate the morphometric characteristics of the pterygomaxillary region using cone-beam computed tomography (CBCT) and to evaluate the effects of age, sex, and laterality on these anatomical parameters.

Materials and methods: In this retrospective study, CBCT scans of 200 individuals (100 males and 100 females) aged 20-80 years were analyzed. Axial measurements included distances between the piriform rim, the descending palatine artery, the pterygomaxillary osteotomy line, and the pterygomaxillary fissure. Additionally, the thickness and width of the pterygomaxillary region and pterygoid process, lengths of the medial and lateral pterygoid laminae, and the distance between the greater palatine canal and the medial pterygoid lamina apex were recorded. Measurements were statistically evaluated by sex, age group, and laterality.

Results: The following parameters demonstrated statistically significant differences based on the conducted measurements: The distance between the piriform rim and the descending palatine artery was significantly greater on the left side (p < 0.001). The length of the lateral pterygoid lamina increased with advancing age (p = 0.048). The thickness of the pterygomaxillary region was significantly greater in females (p = 0.014). Additionally, the distance between the greater palatine canal and the terminal point of the medial pterygoid lamina was significantly higher in males (p < 0.001).

Conclusions: The pterygomaxillary region exhibits anatomical variations that may lead to serious complications during Le Fort I osteotomy. Detailed preoperative evaluation of this area using CBCT can guide surgical planning and help prevent potential vascular and neural complications.

Background/Objectives: Pancreatic cancer is among the most aggressive malignancies, with poor survival rates. Emerging evidence suggests that body composition, including skeletal muscle mass and adiposity distribution, plays a crucial role in predicting patient outcomes. However, its impact on survival in pancreatic cancer remains incompletely understood. The aim of this systematic review was to assess the correlation between body composition parameters and survival outcomes in pancreatic cancer patients, focusing on overall survival. Methods: A comprehensive literature search was conducted, including three main components: pancreatic cancer, body composition, and survival outcomes. Results: 23 studies were included in this review. The findings indicate that body composition can serve as a predictor of survival in pancreatic cancer patients, with 21 studies reporting a significant correlation. The most frequently observed predictor, with 11 studies reporting, was not a baseline parameter but rather changes in parameters over time during treatment. However, discrepancies remain regarding the extent of predictive power and the relative importance of individual components. Conclusions: Specific body composition parameters hold potential as prognostic indicators of survival in pancreatic cancer patients. However, further research is necessary to establish consistent patterns and to clarify which parameters are most predictive and under what conditions.

Background/Objectives: Super-resolution deep-learning reconstruction (SR-DLR) is an advanced image reconstruction technique, but its effect on dynamic myocardial computed tomography perfusion (CTP) imaging has not been evaluated. This study aimed to examine the impact of SR-DLR on image quality and perfusion parameters in dynamic myocardial CTP. Methods: Thirty-five patients who underwent dynamic myocardial CTP for coronary artery disease assessment were retrospectively analyzed. Two CTP datasets were reconstructed using hybrid iterative reconstruction (HIR) and SR-DLR. Image quality was compared qualitatively and quantitatively, including image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and edge rise slope (ERS). Equivalence of CT-derived myocardial blood flow (CT-MBF) between two reconstructions was tested using a previously reported 15% equivalence margin. Intra-patient variability of CT-MBF was evaluated using the robust coefficient of variation (rCV). Results: In the qualitative assessment, SR-DLR had significantly higher scores in contrast (4.0 vs. 2.0) and sharpness (4.5 vs. 2.5) compared with HIR (p < 0.001), while contrast scores were similar. In the quantitative assessment, SR-DLR demonstrated significantly lower image noise (19.4 vs. 29.4 HU), and improved SNR (6.1 vs. 4.1), CNR (13.7 vs. 10.9), and ERS (171.0 vs. 135.1 HU/mm) (all p < 0.001). Mean global CT-MBF was comparable (3.15 ± 0.91 mL/g/min for HIR vs. 3.18 ± 0.97 mL/g/min for SR-DLR) and equivalence was confirmed (p = 0.022). SR-DLR significantly reduced rCV compared with HIR (36.0% vs. 41.0%, p < 0.001). Conclusions: SR-DLR enhances image quality in dynamic myocardial CTP while maintaining mean global CT-MBF and reducing intra-patient variability.

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Background: In computed tomography (CT), automatic exposure control (AEC) determines the tube current and thus the radiation dose based on scout images. We investigated CT dose modulation using two versions of CARE Dose 4D, Siemens AEC software.

Methods: A cylindrical phantom and an anthropomorphic phantom with the upper extremities raised or down were imaged. The CT tube current was determined using two versions of CARE Dose 4D and different scout directions: the posteroanterior scout image alone (PA scout), the lateral scout image alone (Lat scout), and the combination of the PA and Lat scout images (PA + Lat scout). The new version is designed to utilize the Lat image solely for off-center correction when both PA and Lat images are available. Experiments were performed at various vertical positions and with various scout imaging parameters.

Results: The influence of the scout direction on CT dose was demonstrated, with variations depending on the imaging object and software version. The CT dose determined with the PA scout varied according to vertical positioning, presumably due to changes in image magnification. Such effects were small with the Lat scout or PA + Lat scout. Decreasing the tube voltage or tube current in scout imaging affected CT dose modulation with the Lat scout but not with the PA scout. With the PA + Lat scout, the effects of scout parameters were evident using the previous version but minimal using the new version.

Conclusions: Off-center correction in the new version functioned appropriately. Because the behavior of an AEC system is complicated, it is recommended to examine the characteristics of each AEC system under various imaging conditions.

Background/objectives: Alzheimer's disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioeconomic burden. Early and accurate diagnosis is crucial to enhancing patient outcomes, yet traditional clinical and imaging assessments are often limited in sensitivity, particularly at early stages. This study presents a dual-modal framework that integrates symptom-based clinical data with magnetic resonance imaging (MRI) using machine learning (ML) and deep learning (DL) models, enhanced by explainable AI (XAI).

Methods: Four ML classifiers-K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)-were trained on demographic and clinical features. For stage-wise classification, five DL models-CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2-were applied to MRI scans. Interpretability was incorporated through SHAP and Grad-CAM visualizations.

Results: Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging. SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement.

Conclusions: Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging. The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment.

Objective: Axillary lymph node changes are frequently observed in patients with HIV, yet their radiological characteristics and clinical significance remain underexplored. This study aimed to evaluate the association between axillary lymph node computed tomography (CT) features and clinical markers of immune function, including CD4 lymphocyte count and plasma viral load, in HIV-positive patients. Materials and Methods: In this retrospective study, 113 HIV-positive patients who underwent contrast-enhanced chest CT were included. Patients were stratified by CD4 count (<200, 200-500, >500 cells/μL) and plasma viral load (<100,000 or >100,000 copies/mL). Axillary lymph node parameters-including maximum and minimum diameters, cortical thickness, hilar width, and density (Hounsfield units, HU)-were measured on multiplanar reconstructed CT images. Group differences were assessed using the Kruskal-Wallis and Mann-Whitney U tests, and Spearman's correlation was used to evaluate associations between imaging and laboratory findings. Receiver operating characteristic (ROC) curve analysis identified optimal density thresholds. Results: Lymph node diameters, cortical thickness, and hilar width did not significantly differ between CD4 groups. However, mean lymph node density was higher in patients with CD4 < 200 cells/μL (p = 0.024). A density threshold of 84.5 HU distinguished impaired from preserved immune function (sensitivity 61.1%, specificity 71.2%). Patients with viral load >100,000 copies/mL showed increased lymph node density, minimal diameter, and cortical thickness. Conclusions: Elevated axillary lymph node density correlates with immune suppression and high viral load, suggesting its potential as a non-invasive prognostic imaging biomarker in HIV infection.