Tomography最新文献

This study introduces a machine learning (ML) approach to diagnosing carotid artery diseases, including stenosis, aneurysm, and dissection, by leveraging craniocervical computed tomography angiography (CTA) data. A meticulously curated, balanced dataset of 122 patient cases was used, ensuring reproducibility and data quality, and this is publicly accessible at (insert dataset location). The proposed method integrates a super learner model which combines adaptive boosting, gradient boosting, and random forests algorithms, achieving an accuracy of 90%. To enhance model robustness and generalization, techniques such as k-fold cross-validation, bootstrapping, data augmentation, and the synthetic minority oversampling technique (SMOTE) were applied, expanding the dataset to 1000 instances and significantly improving performance for minority classes like aneurysm and dissection. The results highlight the pivotal role of blood vessel structural analysis in diagnosing carotid artery diseases and demonstrate the superior performance of the super learner model in comparison with state-of-the-art (SOTA) methods in terms of both accuracy and robustness. This manuscript outlines the methodology, compares the results with state-of-the-art approaches, and provides insights for future research directions in applying machine learning to medical diagnostics.

Background: Quantitative thresholds are helpful to define an abnormal DaT SPECT in patients with suspected nigrostriatal degenerative diseases (NSDD). The optimal DaTQUANT threshold for diagnostic accuracy of DaT SPECT across combined movement and cognitive disorder populations has been previously described. Methods: We established optimal DaTQUANT thresholds that enhance the discrimination between dementia with Lewy bodies (DLB) and non-DLB dementia types, as well as between Parkinsonian syndromes (PS) and conditions not characterized by nigrostriatal degeneration (non-PS). Results: Data from a total of 303 patients were used in this retrospective analysis. Posterior putamen of the more affected hemisphere (MAH) was shown to be an accurate single-variable predictor for both DLB and PS and was comparable to the most accurate multi-variable models. Conclusions: Automated quantification with DaTQUANT can accurately aid in the differentiation of DLB from non-DLB dementias and PS from non-PS. Optimal thresholds for assisting a diagnosis of DLB are striatal binding ratio (SBR) ≤ 0.65, z-score ≤ -2.36, and a percent deviation ≤ -0.54 for the posterior putamen of the MAH. Optimal posterior putamen thresholds for assisting a diagnosis of PS are SBR ≤ 0.92, z-score ≤ -1.53, and a percent deviation ≤ -0.33, which are similar to our previously reported posterior putamen threshold values using a blended patient pool from multiple study populations.

In an era of rapid technological progress, this Special Issue aims to provide a comprehensive overview of the state-of-the-art in tomographic imaging [...].

Background: Both lung lobe segmentation and lung fissure segmentation are useful in the clinical diagnosis and evaluation of lung disease. It is often of clinical interest to quantify each lobe separately because many diseases are associated with specific lobes. Fissure segmentation is important for a significant proportion of lung lobe segmentation methods, as well as for assessing fissure completeness, since there is an increasing requirement for the quantification of fissure integrity.

Methods: We propose a method for the fully automatic segmentation of pulmonary fissures on lung computed tomography (CT) based on U-Net and PAN models using a Derivative of Stick (DoS) filter for data preprocessing. Model ensembling is also used to improve prediction accuracy.

Results: Our method achieved an F1 score of 0.916 for right-lung fissures and 0.933 for left-lung fissures, which are significantly higher than the standalone DoS results (0.724 and 0.666, respectively). We also performed lung lobe segmentation using fissure segmentation. The lobe segmentation algorithm shows results close to those of state-of-the-art methods, with an average Dice score of 0.989.

Conclusions: The proposed method segments pulmonary fissures efficiently and have low memory requirements, which makes it suitable for further research in this field involving rapid experimentation.

Background/Objectives: The usefulness of dual-energy computed tomography (DECT) for low absorption in the parenchymal phase and contrast effects in the delayed phase for pancreatic cancer is not clear. Therefore, the diagnostic capability of low-KeV images obtained using DECT for pancreatic cancer in the pancreatic parenchymal and delayed phases was evaluated quantitatively and qualitatively. Methods: Twenty-five patients with pancreatic cancer who underwent contrast-enhanced DECT were included. A total of 50 and 70 KeV CT images, classified as low-keV and conventional CT-equivalent images, were produced, respectively. The tumor-to-pancreas contrast (Hounsfield units [HU]) in the pancreatic parenchymal and delayed phases was calculated by subtracting the CT value of the pancreatic tumor from that of normal parenchyma. Results: The median tumor-to-pancreas contrast on 50 KeV CT in the pancreatic parenchymal phase (133 HU) was higher than that on conventional CT (68 HU) (p < 0.001). The median tumor-to-pancreas contrast in the delayed phase was -28 HU for 50 KeV CT and -9 HU for conventional CT (p = 0.545). For tumors < 20 mm, the tumor-to-pancreas contrast of 50 KeV CT (-39 HU) had a significantly clearer contrast effect than that of conventional CT (-16.5 HU), even in the delayed phase (p = 0.034). Conclusions: These 50 KeV CT images may clarify the low-absorption areas of pancreatic cancer in the pancreatic parenchymal phase. A good contrast effect was observed in small pancreatic cancers on 50 KeV delayed-phase images, suggesting that DECT is useful for the visualization of early pancreatic cancer with a small tumor diameter.

Background: Existing methods for MRI brain tumor segmentation often suffer from excessive model parameters and suboptimal performance in delineating tumor boundaries.

Methods: For this issue, a lightweight MRI brain tumor segmentation method, enhanced by hierarchical feature fusion (EHFF), is proposed. This method reduces model parameters while improving segmentation performance by integrating hierarchical features. Initially, a fine-grained feature adjustment network is crafted and guided by global contextual information, leading to the establishment of an adaptive feature learning (AFL) module. This module captures the global features of MRI brain tumor images through macro perception and micro focus, adjusting spatial granularity to enhance feature details and reduce computational complexity. Subsequently, a hierarchical feature weighting (HFW) module is constructed. This module extracts multi-scale refined features through multi-level weighting, enhancing the detailed features of spatial positions and alleviating the lack of attention to local position details in macro perception. Finally, a hierarchical feature retention (HFR) module is designed as a supplementary decoder. This module retains, up-samples, and fuses feature maps from each layer, thereby achieving better detail preservation and reconstruction.

Results: Experimental results on the BraTS 2021 dataset demonstrate that the proposed method surpasses existing methods. Dice similarity coefficients (DSC) for the three semantic categories ET, TC, and WT are 88.57%, 91.53%, and 93.09%, respectively.

Background: Studying causality relationships between different brain regions using the fMRI method has attracted great attention. To investigate causality relationships between different brain regions, we need to identify both the brain network structure and the influence magnitude. Most current methods concentrate on magnitude estimation, but not on identifying the connection or structure of the network. To address this problem, we proposed a nonlinear system identification method, in which a polynomial kernel was adopted to approximate the relation between the system inputs and outputs. However, this method has an overfitting problem for modelling the input-output relation if we apply the method to model the brain network directly. Methods: To overcome this limitation, this study applied the least absolute shrinkage and selection operator (LASSO) model selection method to identify both brain region networks and the connection strength (system coefficients). From these coefficients, the causality influence is derived from the identified structure. The method was verified based on the human visual cortex with phase-encoded designs. The functional data were pre-processed with motion correction. The visual cortex brain regions were defined based on a retinotopic mapping method. An eight-connection visual system network was adopted to validate the method. The proposed method was able to identify both the connected visual networks and associated coefficients from the LASSO model selection. Results: The result showed that this method can be applied to identify both network structures and associated causalities between different brain regions. Conclusions: System identification with LASSO model selection algorithm is a powerful approach for fMRI effective connectivity study.

Introduction: Nailfold video-capillaroscopy (NVC) is a non-invasive cost-effective technique involving the microscopic examination of small blood vessels of the distal nailfold with a magnification device. It provides valuable information regarding the microcirculation including anomalies such as tortuous or dilated capillaries, hemorrhages, and avascular areas, which can characterize connective tissue diseases. The utility of NVC in the diagnosis and monitoring of systemic sclerosis (SSc) has been investigated in numerous studies allowing the distinction of the specific microvascular pattern of scleroderma from different conditions other than scleroderma (non-scleroderma pattern). Sarcoidosis (SA) is a systemic inflammatory disease that can affect various organs, including the lungs, skin, and lymph nodes. The purpose of our review was to evaluate the current state of the art in the use of NVC in the diagnosis of SA, to understand the indications for its use and any consequent advantages in the management of the disease in different settings in terms of benefits for patients.

Materials and methods: We searched for the key terms "sarcoidosis" and "video-capillaroscopy" in a computerized search of Pub-Med, extending the search back in time without setting limits. We provided a critical overview of the literature, based on a precise evaluation. After our analysis, we examined the six yielded works looking for answers to our questions.

Results: Few studies have evaluated that microcirculation is often compromised in SA, with alterations in blood flow and consequent tissue damage.

Discussion: Basing on highlighted findings, NVC appears to be a useful tool in the initial evaluation of sarcoidosis patients. Furthermore, capillaroscopy is useful in the evaluation of the coexistence of sarcoidosis and scleroderma spectrum disorder or overlap syndromes.

Conclusions: In conclusions, no specific pattern has been described for sarcoidosis, and further re-search is needed to fully understand the implications of nailfold capillaroscopy find-ings in this disease and to establish standardized guidelines for its use in clinical practice.

Objectives: Facial fractures and associated traumatic brain injuries represent a worldwide public health concern. Therefore, we aimed to determine the pattern of brain injury accompanying facial fractures by comparing adult patients with and without facial fractures in terms of demographic, clinical, and imaging features.

Methods: This single-center, retrospective study included 492 polytrauma patients presenting at our emergency department from January 2019 to July 2023, which were divided in two groups: with facial fractures (FF) and without facial fractures (non-FF). The following data were collected: age, sex, mechanism of trauma (road traffic accident, fall, and other causes), Glasgow Coma Scale (GCS), the evolution of the patient (admitted to a medical ward or intensive care unit, neurosurgery performed, death), and imaging features of the injury. Data were analyzed using descriptive tests, Chi-square tests, and regression analyses. A p-value less than 0.05 was considered statistically significant.

Results: In the FF group, there were 79% (n = 102) men and 21% (n = 27) women, with a mean age of 45 ± 17 years, while in the non-FF group, there were 70% (n = 253) men and 30% (n = 110) women, with a mean age 46 ± 17 years. There was a significant association between brain injuries and facial fractures (p < 0.001, AOR 1.7). The most frequent facial fracture affected the zygoma bone in 28.1% (n = 67) cases. The most frequent brain injury associated with FF was subdural hematoma 23.4% (n = 44), and in the non-FF group, the most common head injury was intraparenchymal hematoma 29% (n = 73); Conclusions: Both groups shared similarities regarding gender, age, cause of traumatic event, and outcome but had significant differences in association with brain injuries, ICU admission, and clinical status.

Background: The workload of musculoskeletal radiologists has come under pressure. Our objective was to estimate the reading times of common musculoskeletal MRI examinations.

Methods: A total of 144 radiologists were asked to estimate reading times (including interpretation and reporting) for MRI of the shoulder, elbow, wrist, hip, knee, and ankle. Multivariate linear regression analyses were performed.

Results: Reported median reading times with interquartile range (IQR) for the shoulder, elbow, wrist, hip, knee, and ankle were 10 (IQR 6-14), 10 (IQR 6-14), 11 (IQR 7.5-14.5), 10 (IQR 6.6-13.4), 8 (IQR 4.6-11.4), and 10 (IQR 6.5-13.5) min, respectively. Radiologists aged 35-44 years reported shorter reading times for the shoulder (β coefficient [β] = B-3.412, p = 0.041), hip (β = -3.596, p = 0.023), and knee (β = -3.541, p = 0.013) than radiologists aged 45-54 years. Radiologists not working in an academic/teaching hospital reported shorter reading times for the hip (β = -3.611, p = 0.025) and knee (β = -3.038, p = 0.035). Female radiologists indicated longer reading times for all joints (β of 2.592 to 5.186, p ≤ 0.034). Radiologists without musculoskeletal fellowship training indicated longer reading times for the shoulder (β = 4.604, p = 0.005), elbow (β = 3.989, p = 0.038), wrist (β = 4.543, p = 0.014), and hip (β = 2.380, p = 0.119). Radiologists with <5 years of post-residency experience indicated longer reading times for all joints (β of 5.355 to 6.984, p ≤ 0.045), and radiologists with 5-10 years of post-residency experience reported longer reading time for the knee (β = 3.660, p = 0.045) than those with >10 years of post-residency experience.

Conclusions: There is substantial variation among radiologists in reported reading times for common musculoskeletal MRI examinations. Several radiologist-related determinants appear to be associated with reading speed, including age, gender, hospital type, training, and experience.