Tomography, Volume 10, Issue 10 (October 2024) – 4 articles

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. Full article

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. Full article

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. Full article

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. Full article