Tomography Magnetic Resonance Imaging Research Articles

A head-to-head comparison of 18F-FDG PET/CT and 18F-FDG PET/MR in patients with nasopharyngeal carcinoma under different disease settings.

Positron emission tomography/computed tomography (PET/CT) and magnetic resonance imaging (MRI) are complementary in staging of nasopharyngeal carcinoma (NPC). The combination of MRI and functional imaging from PET in PET/MR is promising in NPC management. Diagnostic performance of PET/CT and PET/MR was compared in 46 patients with histologically confirmed NPC under different disease scenarios, including primary non-metastatic cases, primary metastatic cases, recurrence and/or metastasis after treatment, and post-treatment follow-up cases. Forty-six patients underwent both PET/CT and PET/MR in the same day. Primary tumor extension into risk-stratified anatomic structures, retropharyngeal and cervical lymph node metastasis, distant metastasis and post-treatment follow-up results, were compared. For high-risk structures, PET/MR detected two more sides of tensor/levator veli palatine muscle involvement, one more case of clivus involvement, and ruled out 12 false-positive sides of prevertebral muscle involvement by PET/CT. For medium-risk structures, PET/MR detected four more sides of medial pterygoid muscle involvement. For low-risk structures, abnormal signal on massa lateralis atlantis was detected by PET/MR. PET/MR detected 14 more positive retropharyngeal lymph nodes and more liver micrometastases than PET/CT. Overall, PET/MR changed two patients' T staging. Positron emission tomography/MR outperforms PET/CT in delineating muscle, skull-base bone, and nodal involvement, and identifying liver micrometastases, may serve as a single-step staging modality for NPC.

Arginine vasopressin deficiency onset after COVID-19 vaccination with positive anti-rabphilin-3A antibodies: a case report and literature review

BackgroundArginine vasopressin deficiency (AVP-D) can occur due to various conditions, so clarifying its cause is important for deciding treatment strategy. Although several cases of AVP-D following coronavirus disease 2019(COVID-19) infection or COVID-19 vaccination have been reported, the diagnosis of the underlying disease has not been reported in most cases.Case presentationA 75-year-old woman who presented with polydipsia and polyuria 9 weeks after contracting COVID-19 and 5 weeks after receiving the SARS-CoV-2 vaccination, leading to the final diagnosis of AVP-D 8 months after the first appearance of symptoms. Interestingly, pituitary magnetic resonance imaging (MRI) still revealed stalk enlargement frequently observed in patients with SARS-CoV-2 vaccination-induced AVP-D. Although this finding could not rule out any malignancies, we additionally measured anti-rabphilin-3A antibodies, a known marker for lymphocytic infundibulo-neurohypophysitis (LINH), and found that the results were positive, strongly suggesting LINH as the cause of this disease. Thus, we avoided pituitary biopsy. At the follow-up MRI conducted 12 months after the initial consultation, enlargement of the pituitary stalk was still observed.ConclusionWe experienced a case with LINH probably induced by SARS-CoV-2 vaccination. In SARS-CoV-2 vaccination-related LINH, unlike typical LINH, there is a possibility of persistent pituitary stalk enlargement on MRI images for an extended period, posing challenges in differential diagnosis from other conditions. Pituitary stalk enlargement and positive anti-rabphilin-3A antibodies may help in the diagnosis of AVP-D induced by SARS-CoV-2 vaccination.

Measurement of hepatic glucose (18F-fluorodeoxyglucose) uptake with positron emission tomography-magnetic resonance imaging in fumonisin B intoxicated rabbit bucks

Rabbit bucks (bodyweight 5 kg) underwent dietary intoxication with fumonisin B series mycotoxins (FB1 + FB2 + FB3, 15 mg/kg diet) for 14 days to test the applicability of positron emission tomography-magnetic resonance (PET MR) hybrid imaging in characterizing experimentally induced mild hepatotoxicosis. 18F-fluorodeoxyglucose (18F-FDG) radiotracer-aided imaging was performed before and after FBs administration on identical animals, and at both time points, blood was sampled for haematology and clinical chemistry. Kinetic PET image analysis revealed time-activity curves with uptake maxima below 1 min in the liver, renal cortex, portal vein, lung and coarctatio aortae. In the frame of static PET image analysis, based on the standardized uptake value (SUV), the so-called metabolic liver volume (MLV, liver volume defined by over 0.9 × average liver SUV) and the total liver glycolysis (TLG, MLV multiplied by the SUVmean) were calculated. Mycotoxicosis increased total liver glycolysis (p < 0.04) after 14 days and liver tissue TLG inhomogeneity was minimal. Pearson correlation between TLG and alkaline phosphatase (ALP) was positive (0.515), while negative with LDH and AST (− 0.721 and − 0.491, respectively). Results indicate a slight hepatic mycotoxin effect and significantly increased glucose uptake intensity, which has been sensitively detected with molecular imaging (18F-FDG PET MRI) in the rabbit model.

A Robust Deep Learning Model for Brain Tumor Detection and Classification Using Efficient Net: A Brief Meta-Analysis

Accurately detecting and classifying brain tumors’, is critical for timely diagnosis and effective treatment planning. The purpose of this paper is to provide a comprehensive examination utilizing the EfficientNet family of deep learning architectures to automatically identify and categorize three forms of brain tumors from magnetic resonance imaging (MRI) scans. The primary aim of the study is to assess the performance of different EfficientNet models (ranging from EfficientNet-B0 to EfficientNet-B7) and determine their capability to achieve high accuracy in brain tumor classification. In the implementation, a diverse dataset is compiled comprising approximately 18,500 MRI images, representing various types of brain tumors. EfficientNet models are trained, validated, and tested using a combination of Softmax and ReLU activation functions, along with the Adam optimizer by employing learning rates of 0.0005 and 0.00001 for model training and optimization. The experimental results underscore the significant potential of the EfficientNet family in brain tumor classification. The findings reveal a consistent improvement in tumor detection accuracy as model complexity increases. The attained accuracies for different EfficientNet models are; 96.07% (EfficientNet-B0), 97.86% (EfficientNet-B1), 98.21% (EfficientNet-B2), 97.86% (EfficientNet-B3), 98.93% (EfficientNet-B4), 99.64% (EfficientNet-B5), 98.57% (EfficientNet-B6), and 99.64% (EfficientNet-B7), respectively. The proposed research not only validates the efficiency of the EfficientNet architecture in classifying brain tumors but also offers valuable insights into how model complexity influences classification performance. The notably high accuracy rates emphasize the clinical promise of employing deep learning methods to aid radiologists and medical experts in precise and efficient brain tumor diagnosis. Additionally, the paper's scope adds to the growing body of knowledge regarding the application of deep learning techniques to enhance medical image analysis and diagnostic capabilities.

Difference in Laterality of the Dorsal Striatum in Schizoaffective Disorder

Background: Recent research has demonstrated that the dorsal striatum is directly associated with the integration of cognitive, sensory-motor, and motivational/emotional data. Disruptions in the corticostriatal circuit have been implicated in the pathophysiology of psychosis. The dorsal striatum was reported to show lateralized pathology in psychotic disorders. In this study, we aimed to analyze the laterality of the dorsal striatum with texture analysis of T2-weighted magnetic resonance imaging (MRI) images from schizoaffective disorder (SAD) patients. Methods: Twenty SAD patients, met the inclusion criteria and had available cranial MRI data were assigned as the patient group. Twenty healthy individuals were determined as the control group. Texture analysis values were obtained from striatum region of interests (ROI) generated from T2-weighted MRI images. Data are presented as mean and standard deviation. The suitability of the data for normal distribution was analyzed with the Kolmogorov-Smirnov test. Analysis of variance (ANOVA) test (Post Hoc TUKEY) was employed to compare the group data based on test findings. Results: There was no significant difference between the groups in terms of gender and age. There were differences in the values of texture analysis parameters of both caudate and putamen nuclei in comparison to controls. We identified differences in the left dorsal striatum nuclei in SAD. The differences in the putamen were more and more pronounced than in the caudate. Conclusions: Texture analyses suggest that the left dorsal striatum nuclei may be different in SAD patients. Further studies are needed to determine the pathophysiology of SAD and how it may affect disease treatment.

MRI T1-Weighted Fat Segmentation Based on Active Contour for Osteosarcoma Patients

Osteosarcoma (OS) is the most common and frequent primary bone cancer in children and adolescents. This type of bone cancer often developed at the long bones’ extremities close to the metaphyseal growth plates. To address these issues, OS quantitative analysis is performed using Magnetic Resonance Imaging (MRI) to plan surgical procedures and track the effectiveness of treatment. Fat suppression is commonly employed to eliminate the fat signal from T1-Weighted MRI sequence. The suppressed fat signal in T1-Weighted is important and often used to identify abnormalities in other MRI sequences. In clinical, MRI images must be interpreted by a radiologist. Manually outlining the tumour location is laborious and subjective. Image processing involves segmentation to separate information from the desired target region of the image. Active Contour (AC) is an algorithm often used in medical images for segmentation. Nevertheless, the process of mask initialization can be challenging and requires careful attention. This study proposes a method to define the mask initialization so that the AC has a starting point to extract fat in T1-Weighted for the purpose of fat suppression. From the results obtained, AC with the proposed mask initialization method successfully segmented the fat in T1-Weighted MRI images with accuracy, precision, recall and F1-score of 0.92, 0.86, 0.88 and 0.89 respectively.

Liver vessel MRI image segmentation based on dual-path diffusion model

Accurate segmentation of liver blood vessels in magnetic resonance imaging (MRI) images is a challenging task due to the complex tree-like structure and anisotropic diffusion properties of blood vessels. To solve this problem, we propose a new Dual-Path Diffusion Model (DPDM) framework. The framework consists of two collaborative diffusion paths: a local feature learning path based on convolution operations and a global context modeling path based on transform blocks. Local path encodes rich shape priors and preserve spatial details, while global path captures long-distance dependencies and enhance representation. In the decoding phase, the boundary features from the boundary path are fused with the features of the ordinary path decoding, which further enhances the shape sensitivity. In addition, we leverage a multi-task learning scheme to jointly optimize vascular segmentation and boundary prediction tasks in an end-to-end manner. Experiments on retrospective clinical dataset demonstrate that the proposed DPDM framework achieves excellent performance on the liver vessel segmentation task. Compared with state-of-the-art methods, our approach achieved a 6.0% and 7.3% performance improvement in Dice coefficient and IoU index, respectively. Our approach offers a promising solution for automated blood vessel segmentation in precision medicine.

Effectiveness of VGG19 in deep learning for brain tumor detection

Image processing in the diagnosis of disease is one of the jobs that is currently developing in the world of health. Diagnosis is carried out by utilizing the role of image processing to provide a level of accuracy in diagnosis results and provide efficiency to medical personnel. This research aims to develop a brain tumor object detection process using a deep learning (DL) approach to magnetic resonance images (MRI) images. This development was carried out to optimize the brain tumor diagnosis process by playing the role of the image extraction process. This research dataset was sourced from the M. Djamil Padang Provincial General Hospital with a total of 3370 MRI images. The results of this work report show that DL performance is capable of carrying out the detection process automatically with an accuracy level of 97,83%. The results of the development of the extraction process can work effectively in ensuring brain tumor objects are precise and accurate. Overall, this research can make a major contribution to maximizing the diagnosis process and assisting medical personnel in the early treatment of brain tumor patients.

Continuum topological derivative - A novel application tool for segmentation of CT and MRI images

IntroductionComputed Tomography (CT) and Magnetic Resonance Imaging (MRI) are essential tools for unraveling anatomical and tissue properties, particularly in the head and brain. CT provides high-contrast images, particularly valuable in cases such as cerebral bleeds, and also aids in estimating cranial deformities and organ shape deviations. MRI, on the other hand, offers excellent imaging of cerebral artery regions, allowing analysis of various cerebral pathologies through different sequences. Beyond detecting common head and brain disorders, these modalities play a crucial role in identifying abnormalities in orbits, middle cerebral artery territories, brain ventricles, soft tissues, and bones. A unique aspect of brain MRI is its ability to produce multiplanar brain assessments. Both head/brain CT and MRI are invaluable for studying haemorrhage cases, with segmentation of affected areas providing detailed images for further analysis. This study explores the application of a novel mathematical technique, continuum topological derivative (CTD), for CT and MR image segmentation. MethodsThe initial stage of Continuum Topological Derivative (CTD) segmentation involves preprocessing CT and MR images due to their susceptibility to inherent noises, such as quantum mottle, and Gaussian and Rayleigh noises, respectively. In this study, we have implemented the CTD denoising algorithm to produce denoised CT/MR images, serving as ground truth for subsequent segmentation steps. Validation of the denoised CTD CT/MR images was conducted through minimal residual value computation across all case studies. Following this, segmentation of the region of interest was performed using the CTD technique, with comparisons made against Discrete Topological Derivatives (DTD), k-mean clustering and Adaptive Threshold methods. Evaluation of the proposed CTD algorithm's effectiveness in segmentation involved calculating performance metrics such as Jaccard and dice indices to assess spatial overlap of segmented images. ResultsThe CTD technique yields excellent segmentation results, not only for the delineated region of interest but also for volume-based cerebral blood areas and anomalies in the middle cerebral artery (MCA) and its territorial areas, which are substantiated through performance metrics and visual inspection by trained radiologist. This aids in determining the severity of stroke in affected patients. Additionally, a unique attempt is made to apply CTD to Electrical Impedance Tomography (EIT) images of the lungs for precise estimation of the breathing cycle. CTD successfully generates standardized images, demonstrating attenuation and density characteristics for cerebral cisterns, arteries, and ventricles. DiscussionThe denoised images obtained through CTD facilitate thorough analysis of both normal and pathological conditions, providing radiologists with enhanced capabilities to identify subtle details, particularly in areas such as abnormal cerebral artery territories, haemorrhage cases, cisterns, ventricles and arteries. Results clearly demonstrate that the combination of CTD denoising and segmentation outperforms the other three established methods in terms of both efficiency and accuracy in delineating diseased or affected areas, as evidenced by the various case studies conducted in this research. In summary, the proposed CTD method aims to delineate boundaries and contours of the region of interest, facilitating precise estimation of size and shape for accurate detection of the extent of diseased or affected areas.

Integrating MRI-based radiomics and clinicopathological features for preoperative prognostication of early-stage cervical adenocarcinoma patients: in comparison to deep learning approach

ObjectivesThe roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and clinical features for AC patients.MethodsClinical and pathological information from one hundred and ninety-seven patients with cervical AC was collected and analyzed. For each patient, 107 radiomics features were extracted from T2-weighted MRI images. Feature selection was performed using Spearman correlation and random forest (RF) algorithms, and predictive models were built using support vector machine (SVM) technique. Deep learning models were also trained with T2-weighted MRI images and clinicopathological features through Convolutional Neural Network (CNN). Kaplan-Meier curve was analyzed using significant features. In addition, information from another group of 56 AC patients was used for the independent validation.ResultsA total of 107 radiomics features and 6 clinicopathological features (age, FIGO stage, differentiation, invasion depth, lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) were included in the analysis. When predicting the 3-year, 4-year, and 5-year DFS, the model trained solely on radiomics features achieved AUC values of 0.659 (95%CI: 0.620–0.716), 0.791 (95%CI: 0.603–0.922), and 0.853 (95%CI: 0.745–0.912), respectively. However, the combined model, incorporating both radiomics and clinicopathological features, outperformed the radiomics model with AUC values of 0.934 (95%CI: 0.885–0.981), 0.937 (95%CI: 0.867–0.995), and 0.916 (95%CI: 0.857–0.970), respectively. For deep learning models, the MRI-based models achieved an AUC of 0.857, 0.777 and 0.828 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. And the combined deep learning models got a improved performance, the AUCs were 0.903. 0.862 and 0.969. In the independent test set, the combined model achieved an AUC of 0.873, 0.858 and 0.914 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively.ConclusionsWe demonstrated the prognostic value of integrating MRI-based radiomics and clinicopathological features in cervical adenocarcinoma. Both radiomics and deep learning models showed improved predictive performance when combined with clinical data, emphasizing the importance of a multimodal approach in patient management.

Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels

Brain tumors, a subset of these malignancies, demand accurate and efficient diagnosis. Traditional methods use non-invasive medical imaging like magnetic resonance imaging (MRI) and computed tomography (CT). Although necessary for diagnosis, manual brain MRI picture segmentation is tedious and time-consuming. Using deep learning is a promising solution. This study proposes an innovative approach for brain tumor detection, focusing on meningioma tumors. Utilizing threshold-based segmentation, the MobileNetV2 architecture, a modified feature pyramid network (FPN), and single shot MultiBox detector (SSD), our model achieves precise localization and object detection. Pre-processing techniques such as grayscale conversion, histogram equalization, and Gaussian filtering enhance the MRI image quality. Morphological operations and thresholding facilitate tumor segmentation. Data augmentation and a meticulous dataset division aid in model generalization. The architecture combines MobileNetV2 as a feature extractor, SSD for object detection, and FPN for detecting small objects. Modifications, including lowering the minimum FPN level, enhance small object detection accuracy. The proposed model achieved a recall value of around 98% and a precision value of around 89%. Additionally, the proposed model achieved approximately 93% on both the dice similarity coefficient (DSC) value and the index of similarity. Based on the promising results, our research holds significant advancements for the field of medical imaging and tumor detection.

Preoperative prediction model of lymph node metastasis in the inguinal and femoral region based on radiomics and artificial intelligence

ObjectiveTo predict preoperative inguinal lymph node metastasis in vulvar cancer patients using a machine learning model based on imaging features and clinical data from pelvic magnetic resonance imaging (MRI).Methods52 vulvar...

Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation

The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convolutional Neural Network (R-CNN) masks, Grad-CAM and transfer learning, this work offers an effective method for the detection of brain tumours. Helping doctors make extremely accurate diagnoses is the goal. A transfer learning-based model has been suggested that offers high sensitivity and accuracy scores for brain tumour detection when segmentation is done using R-CNN masks. To train the model, the Inception V3, VGG-16, and ResNet-50 architectures were utilised. The Brain MRI Images for Brain Tumour Detection dataset was utilised to develop this method. This work's performance is evaluated and reported in terms of recall, specificity, sensitivity, accuracy, precision, and F1 score. A thorough analysis has been done comparing the proposed model operating with three distinct architectures: VGG-16, Inception V3, and Resnet-50. Comparing the proposed model, which was influenced by the VGG-16, to related works also revealed its performance. Achieving high sensitivity and accuracy percentages was the main goal. Using this approach, an accuracy and sensitivity of around 99% were obtained, which was much greater than current efforts.

Self-Supervised Contrastive Learning for Automated Segmentation of Brain Tumor MRI Images in Schizophrenia

Schizophrenic patients’ brain tumor magnetic resonance imaging (MRI) images are important references for doctors to diagnose and treat schizophrenia. However, automatic segmentation of these images is a professional and tedious task. Existing methods suffer from problems such as large model parameters, long computation time, and inadequate image processing. To achieve more accurate segmentation of brain tumors, we propose brain tumor MRI images for automatic segmentation using self-supervised contrastive learning in schizophrenia patients (BTCSSSP). First, a denoising algorithm based on progressive principal component analysis approximation and adaptive clustering is designed to process the noisy MRI images. Second, a brightness-aware image enhancement algorithm is developed to address the problems of non-uniformity, unclear boundaries, and poor spatial resolution of the MRI images. Finally, a cross-scale U-Net network with selective feature fusion attention module is designed based on self-supervised contrastive learning to achieve automatic segmentation of brain tumor MRI images. The results show that the BTCSSSP method yields higher Recall and Precision than existing methods. The maximum recall is 95%, and the image segmentation precision is 95%, thus indicating good practical applicability.

The Use of Acoustic Mapping to Improve Surveillance of Pediatric Patient with Residual COVID-19 Symptomology

Objective: This study was designed to assess the severity of lung tissue changes due to COVID-19 in a pediatric cohort that still complained of post-COVID-19 symptoms. Materials and Methods: Parents and adolescents, 13 to 22 years old and have had a confirmatory positive polymerase chain reaction (+PCR) and/or rapid antigen test for COVID-19, consented to have a series of diagnostic tests to determine the level of lung tissue changes due to COVID-19. Sixteen adolescents completed the intake form and lung sonography. The lung sonogram was completed using the bedside lung ultrasound in emergency (BLUE) point method and a GE Logiq I laptop ultrasound system, as well as a linear 7.5-MHz or 3.0-MHz convex-array transducers. Thirteen adolescents completed a chest magnetic resonance imaging (MRI) examination on a 0.55T Siemens MAGNETOM Free.Max scanner. The examination included the heart and lungs of each participant and took about 90 minutes. A radiologist, sonographer, and computed tomography (CT) technologist reviewed and scored the images. Those positive lung sonogram scores were matched anatomically to the same slice within the chest MRI examination. Results: Based on the 13 participants, the acoustic mapping process demonstrated no diagnostic matches for positively scored sonograms at the pleural line or pulmonary parenchyma with the corresponding MRI image slices. There were diagnostic matches for negatively scored sonograms at the pleural line or pulmonary parenchyma with the corresponding MRI image slices. Conclusion: With lingering post-COVID-19 symptomology, this adolescent cohort demonstrated that acoustic mapping of their lung sonograms and chest MRI images had important combined diagnostic value. Lung sonography demonstrated diagnostic effectiveness for evaluating the periphery of the lung tissue, in this cohort but limited in viewing deeper structures. Conversely, MRI provided definitive views of the entire chest cavity and lung tissue, which is important for properly evaluating deeper pulmonary parenchyma.

Pre-operative prediction of histopathological growth patterns of colorectal cancer liver metastasis using MRI-based radiomic models.

Histopathological growth patterns (HGPs) of colorectal liver metastases (CRLMs) have prognostic value. However, the differentiation of HGPs relies on postoperative pathology. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomic model to predict HGP pre-operatively, following the latest guidelines. This retrospective study included 93 chemotherapy-naïve patients with CRLMs who underwent contrast-enhanced liver MRI and a partial hepatectomy between 2014 and 2022. Radiomic features were extracted from the tumor zone (RTumor), a 2-mm outer ring (RT+2), a 2-mm inner ring (RT-2), and a combined ring (R2+2) on late arterial phase MRI images. Analysis of variance method (ANOVA) and least absolute shrinkage and selection operator (LASSO) algorithms were used for feature selection. Logistic regression with five-fold cross-validation was used for model construction. Receiver operating characteristic curves, calibrated curves, and decision curve analyses were used to assess model performance. DeLong tests were used to compare different models. Twenty-nine desmoplastic and sixty-four non-desmoplastic CRLMs were included. The radiomic models achieved area under the curve (AUC) values of 0.736, 0.906, 0.804, and 0.794 for RTumor, RT-2, RT+2, and R2+2, respectively, in the training cohorts. The AUC values were 0.713, 0.876, 0.785, and 0.777 for RTumor, RT-2, RT+2, and R2+2, respectively, in the validation cohort. RT-2 exhibited the best performance. The MRI-based radiomic models could predict HGPs in CRLMs pre-operatively.

Innovative Approaches for Early Alzheimer’s Disease Detection through Novel Analysis of Brain MRI Images

Alzheimer's is one of the most well-known reasons for Dementia. Alzheimer's sickness (AD) is an irreversible, moderate cerebrum issue that gradually obliterates memory and thinking abilities. Alzheimer's and different types of dementia positioned seventh driving reason for death by WHO. Picture handling is broadly utilized in the clinical field to recognize illness and help specialists in dynamics dependent on perception. The paper means to recognize the Alzheimer's sickness at the most punctual with the goal that patients can be forestalled before irreversible changes happen in the mind. We propose the picture handling method to deal with the Magnetic Resonance Imaging (MRI) of the cerebrum from the pivotal plane, coronal plane, and sagittal plane. The picture division is utilized to feature the impacted locale in cerebrum MRI. The analyzed area in mind MRI incorporates the hippocampus and volume of the cerebrum. A similar ID of people impacted with the Alzheimer's illness, Healthy partner, and Mild Cognitive weakness is finished.

AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.

The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. Multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model's performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model's innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis.

Forensic bone age assessment of hand and wrist joint MRI images in Chinese han male adolescents based on deep convolutional neural networks.

In Chinese criminal law, the ages of 12, 14, 16, and 18 years old play a significant role in the determination of criminal responsibility. In this study, we developed an epiphyseal grading system based on magnetic resonance image (MRI) of the hand and wrist for the Chinese Han population and explored the feasibility of employing deep learning techniques for bone age assessment based on MRI of the hand and wrist. This study selected 282 Chinese Han Chinese males aged 6.0-21.0 years old. In the course of our study, we proposed a novel deep learning model for extracting and enhancing MRI hand and wrist bone features to enhance the prediction of target MRI hand and wrist bone age and achieve precise classification of the target MRI and regression of bone age. The evaluation metric for the classification model including precision, specificity, sensitivity, and accuracy, while the evaluation metrics chosen for the regression model are MAE. The epiphyseal grading was used as a supervised method, which effectively solved the problem of unbalanced sample distribution, and the two experts showed strong consistency in the epiphyseal plate grading process. In the classification results, the accuracy in distinguishing between adults and minors was 91.1%, and the lowest accuracy in the three minor classifications (12, 14, and 16 years of age) was 94.6%, 91.1% and 96.4%, respectively. The MAE of the regression results was 1.24 years. In conclusion, the deep learning model proposed enabled the age assessment of hand and wrist bones based on MRI.

Association between Autism Spectrum Disorder, Trace Elements, and Intracranial Fluid Spaces

(1) Autism spectrum disorder (ASD) belongs to the group of complex developmental disorders. Novel studies have suggested that genetic and environmental factors equally affect the risk of ASD. Identification of environmental factors involved in the development of ASD is therefore crucial for a better understanding of its etiology. Whether there is a causal link between trace elements, brain magnetic resonance imaging (MRI), and ASD remains a matter of debate and requires further studies. (2) In the prospective part of the study, we included 194 children, including an age-matched control group; in the retrospective study, 28 children with available MRI imaging were included. All children had urine analysis of trace elements performed. In those with available brain MRI, linear indexes for the ventricular volumes were measured and calculated. (3) We found the highest vanadium, rubidium, thallium, and silver levels in children with ASD. These elements also correlated with the estimated ventricular volume based on MRI indexes in children with ASD in the subanalysis. However, the severity of the deficits did not correlate with brain MRI indexes of our elements, except negatively with magnesium. (4) Trace elements have an impact on children with ASD, but further multi-centric studies are needed to explain the pathophysiological mechanisms.