Tomography Magnetic Resonance Imaging Research Articles

Evaluating the impact of downsampling on 3D MRI images segmentation results based on similarity metrics

Medical imaging plays a crucial role in diagnosing patient conditions, with magnetic resonance imaging (MRI) standing as a significant modality for numerous years. However, leveraging convolutional neural network (CNN) architectures like U-Net and its variations for anatomical segmentation demands considerable memory, particularly when working with full 3D image sets. Therefore, downsampling 3D MRIs proves advantageous in reducing memory consumption. Nevertheless, downsampling leads to a reduction in voxel count, potentially impacting the performance of commonly used segmentation metrics. The jaccard similarity index (JSI), dice similarity coefficient (DSC), and structural similarity index (SSIM) are extensively employed in image segmentation contexts. Hence, this study employs all three metrics to assess downsampled images and evaluate the robustness of the metrics when used to evaluate the downsampled 3D MRI images. The results show that JSI and DSC are more robust than SSIM when handling the downsampled data.

Enhancing medical image classification via federated learning and pre-trained model

The precise classification of medical images is crucial in various healthcare applications, especially in fields like disease diagnosis and treatment planning. In recent times, machine-intelligent models are desired to work in remote settings. However, the potential privacy concerns that arise from sharing confidential patient information to train traditional centralized machine learning models cannot be ignored. Federated learning (FL) offers a promising method for collaborative training on distributed data held by various entities, ensuring the privacy of patient information. This study evaluated the efficiency of the pre-trained models in the FL environment for medical image classification. The Convolutional Neural Network (CNN) model with Gray-Level Co-occurrence Matrix (GLCM) and Local Binary Patterns (LBP), along with the EfficientNet model, are being used as the local models. The trainable parameters from the local models are fed as input for building the global model. Pre-trained models trained on extensive datasets, possess valuable characteristics that can be utilized by FL models trained on proprietary datasets. Implementing this method can improve the efficacy and precision of FL models while also ensuring data confidentiality. The proposed model is evaluated using two distinct medical imaging datasets: Magnetic Resonance Image(MRI) and Computed Tomography (CT) scan images. The research highlights the advantages of utilizing pre-trained models in federated learning for medical image classification (MIC). The model’s performance is assessed acrossseveral assessment criteria, demonstrating the model exhibited a satisfactory accuracy rate of 97.4% and 98.8% for MRI and CT scan images, respectively. The model is evaluated concerning to Diagnostic Odds Ratio (DOR), where the proposed global model has exhibited 1164.54 for the MRI images and 6825.17 for CT scan images, and the values have outperformed the pretrained model.

Automated brain tumor diagnostics: Empowering neuro-oncology with deep learning-based MRI image analysis.

Brain tumors, characterized by the uncontrolled growth of abnormal cells, pose a significant threat to human health. Early detection is crucial for successful treatment and improved patient outcomes. Magnetic Resonance Imaging (MRI) is the primary diagnostic tool for brain tumors, providing detailed visualizations of the brain's intricate structures. However, the complexity and variability of tumor shapes and locations often challenge physicians in achieving accurate tumor segmentation on MRI images. Precise tumor segmentation is essential for effective treatment planning and prognosis. To address this challenge, we propose a novel hybrid deep learning technique, Convolutional Neural Network and ResNeXt101 (ConvNet-ResNeXt101), for automated tumor segmentation and classification. Our approach commences with data acquisition from the BRATS 2020 dataset, a benchmark collection of MRI images with corresponding tumor segmentations. Next, we employ batch normalization to smooth and enhance the collected data, followed by feature extraction using the AlexNet model. This involves extracting features based on tumor shape, position, shape, and surface characteristics. To select the most informative features for effective segmentation, we utilize an advanced meta-heuristics algorithm called Advanced Whale Optimization (AWO). AWO mimics the hunting behavior of humpback whales to iteratively search for the optimal feature subset. With the selected features, we perform image segmentation using the ConvNet-ResNeXt101 model. This deep learning architecture combines the strengths of ConvNet and ResNeXt101, a type of ConvNet with aggregated residual connections. Finally, we apply the same ConvNet-ResNeXt101 model for tumor classification, categorizing the segmented tumor into distinct types. Our experiments demonstrate the superior performance of our proposed ConvNet-ResNeXt101 model compared to existing approaches, achieving an accuracy of 99.27% for the tumor core class with a minimum learning elapsed time of 0.53 s.

18FFlutemetamol-PET Aided Classification of Cerebral Amyloid Angiopathy: A Multicenter Study.

Cerebral amyloid angiopathy (CAA)-related features on neuroimaging often coexist with signs of arteriolosclerosis-small vessel disease on neuroimaging in people with intracranial hemorrhage (ICH). This study aimed at defining the value of amyloid pathology detected by 18Fflutemetamol PET in reclassification and stratification of risk of bleeding in people with mixed CAA-arteriolosclerosis features. We included consecutive patients admitted to 2 institutions (2018-2023) with spontaneous symptomatic ICH, subarachnoid hemorrhage (SAH), transient focal neurologic episodes (TFNE), or cognitive impairment and MRI showing CAA hallmarks. All patients underwent brain magnetic resonance imaging (MRI) with susceptibility weighted imaging and 18Fflutemetamol PET imaging and were followed up for at least 1 year. We compared cases with CAA and arteriolosclerosis + CAA features and defined long-term outcomes (composite outcome including death, ICH, ischemic stroke, SAH, TFNE) depending on PET status (CAA/amyloid pathology vs arteriolosclerosis-predominant groups). Among 47 patients, according to PET and MRI imaging, 38 patients were reclassified in the CAA/amyloid pathology group and 9 in the arteriolosclerosis-predominant group, with similar cardiovascular risk factors but a significantly higher lobar microbleed burden for the former group. The CAA/amyloid pathology group had higher rates of composite outcome (43.9 vs 11.1 events per 100 patient-year; p = 0.039) and ICH (36.5 vs 5.6 events per 100 patient-years; p = 0.04) compared with the arteriolosclerosis-predominant group. 18FFlutemetamol PET imaging can help in reclassification of mixed arteriolosclerosis + CAA into CAA/amyloid pathology and arteriolosclerosis-predominant, with implications on long-term risk of recurrent events. This study provides Class IV evidence that 18Fflutemetamol PET can distinguish between CAA + arteriolosclerosis and arteriolosclerosis-predominant pathology.

Update on the Role of Imaging in the Diagnosis, Staging, and Prognostication of Gallbladder Cancer

AbstractGallbladder cancer (GBC) is a highly aggressive malignancy with dismal prognosis. GBC is characterized by marked geographic predilection. GBC has distinct morphological types that pose unique challenges in diagnosis and differentiation from benign lesions. There are no specific clinical or serological markers of GBC. Imaging plays a key role not only in diagnosis and staging but also in prognostication. Ultrasound (US) is the initial test of choice that allows risk stratification in wall thickening and polypoidal type of gallbladder lesions. US findings guide further investigations and management. Computed tomography (CT) is the test of choice for staging GBC as it allows comprehensive evaluation of the gallbladder lesion, liver involvement, lymph nodes, peritoneum, and other distant sites for potential metastases. Magnetic resonance imaging (MRI) and magnetic resonance cholangiopancreatography allow better delineation of the biliary system involvement. Contrast-enhanced US and advanced MRI techniques including diffusion-weighted imaging and dynamic contrast-enhanced MRI are used as problem-solving tools in cases where distinction from benign lesion is challenging at US and CT. Positron emission tomography is also used in selected cases for accurate staging of the disease. In this review, we provide an up-to-date insight into the role of imaging in diagnosis, staging, and prognostication of GBC.

Imaging arterial and venous vessels using Iron Dextran enhanced multi-echo 3D gradient echo MRI at 7T.

Iron Dextran is a widely used iron oxide compound to treat iron-deficiency anemia patients in the clinic. Similar to other iron oxide compounds such as Ferumoxytol, it can also be used off-label as an intravascular magnetic resonance imaging (MRI) contrast agent due to its strong iron-induced T2 and T2* shortening effects. In this study, we seek to evaluate the feasibility of using Iron Dextran enhanced multi-echo susceptibility weighted imaging (SWI) MRI at 7T to image arterial and venous blood vessels in the human brain. Phantom experiments were performed to measure the r2* relaxivity for Iron Dextran in blood, based on which the SWI sequence was optimized. Pre- and post-infusion MR images were acquired in human subjects from which maps of arteries and veins were extracted. The post-contrast SWI images showed enhanced susceptibility difference between blood and the surrounding tissue in both arteries and veins. Our results showed that the proposed Iron Dextran enhanced multi-echo SWI approach allowed the visualization of blood vessels with diameters down to ~100 μm, including small blood vessels supplying and draining small brain structures such as the hippocampus. We conclude that Iron Dextran can be an alternative iron-based MRI contrast agent for blood vessel imaging in the human brain.

Baseline-to-loaded changes in regional tibial cartilage thickness, T1ρ and T2: Utilization of an MRI compatible loading device.

The objective of the study was to evaluate tibial cartilage thickness (TCT), T1ρ and T2 values within both loaded and baseline configurations in a cadaveric knee model using a 3D bone based tibial coordinate system. Ten intact cadaveric knees were mounted into an magnetic resonance imaging (MRI) compatible loading device. Morphologic and quantitative MRI (qMRI) images were acquired with the knee in a baseline configuration and after application of 50% body weight. The morphologic images were evaluated for cartilage degeneration using a modified Noyes scoring system. A 3D bone-based tibial coordinate system was utilized to evaluate regional changes of tibial T1ρ, T2, and cartilage thickness values among regions covered and uncovered by the meniscus. Inter-regional differences in medial and lateral MRI outcomes were found between loaded and baseline configurations. Cartilage regions covered by the meniscus demonstrated disparate qMRI and TCT results as compared to cartilage regions not covered by the meniscus. The regions covered by meniscus experienced a ~3.5%, ~0.5%, and ~5.5% reduction of T1ρ (p < 0.05, medial and lateral compartments), T2 and TCT, respectively, in both compartments while regions not covered by the meniscus experienced larger reductions of ~10%, ~2%, and ~10.5% reduction of T1ρ (p < 0.05, medial and lateral compartments), T2 and TCT (p < 0.05, lateral compartment only), respectively, in both compartments. T1ρ and T2 decreases following application of 50% body weight load were substantially larger in the tibial regions with modified Noyes grade 3 (n = 2) compared to either healthy regions (n = 85, p < 0.0.003) or regions with modified Noyes grade 2 (n = 13, p < 0.004). Interregional differences in MRI outcomes reflect variations in structure and function, and largely followed a pattern in cartilage regions that were covered or not covered by the meniscus. Results of the current study suggest that ΔT1ρ and ΔT2 values may be sensitive to superficial fissuring, more than baseline or loaded T1ρ or T2 values, or TCT alone, however future studies with additional specimens, with greater variability in OA grade distribution, may further emphasize the current findings.

Prompt Multi-level Segmentation with Denoising Model with Fragile Correlated Feature Subset for Brain Tumor Classification.

Background Classifying brain tumors with extraordinary precision using images is critical for prognosis and treatment planning. The aberrant proliferation of brain cells characterizes brain tumors. Variations in neuronal development may occur among individuals. The classification of tumors as benign or malignant is contingent upon their rate of growth. A benign tumor remains localized at its site of origin; one that has spread to distant sites is malignant. Brain tumor identification may be difficult due to the unique characteristics of brain tumor cells. Objective This study presents a method that methodically improves the identification of brain tumor cells and the analysis of functional structures through the utilization of sample training that incorporates features extracted from Magnetic Resonance Imaging (MRI) images. In the image enhancement phase, the color information of the MRI image is converted to greyscale, and its margins are sharpened to facilitate the detection of finer details. For specialists or general practitioners to accurately diagnose life-threatening conditions, such as brain tumors, medical images are required. Picture denoising has been identified in recent research as a potentially fruitful area of study. It is critical to perform image cleanup while preserving the sharpness of the boundaries. Methods In this research, a Prompt Multi Level Segmentation Denoising model with a Fragile Correlated Feature Subset (PMLSD-FCFS) model is proposed for accurate denoising of MRI images and to extract the most relevant features set by applying a feature dimensionality reduction model for better brain tumor predictions. Results The proposed model achieves 98.2% accuracy in Multi-Level Image Segmentation and 98.4% accuracy in Fragile Correlated Feature Subset Generation. Conclusion The experimental findings indicated that the model proposed exhibits superior performance compared to the traditional algorithms. Furthermore, it successfully eliminates the noise from the MRI images, and most relevant features are only considered for brain tumor detection, thereby enhancing the accuracy of classification.

Super-resolution reconstruction for early cervical cancer magnetic resonance imaging based on deep learning

This study aims to develop a super-resolution (SR) algorithm tailored specifically for enhancing the image quality and resolution of early cervical cancer (CC) magnetic resonance imaging (MRI) images. The proposed method is subjected to both qualitative and quantitative analyses, thoroughly investigating its performance across various upscaling factors and assessing its impact on medical image segmentation tasks. The innovative SR algorithm employed for reconstructing early CC MRI images integrates complex architectures and deep convolutional kernels. Training is conducted on matched pairs of input images through a multi-input model. The research findings highlight the significant advantages of the proposed SR method on two distinct datasets at different upscaling factors. Specifically, at a 2× upscaling factor, the sagittal test set outperforms the state-of-the-art methods in the PSNR index evaluation, second only to the hybrid attention transformer, while the axial test set outperforms the state-of-the-art methods in both PSNR and SSIM index evaluation. At a 4× upscaling factor, both the sagittal test set and the axial test set achieve the best results in the evaluation of PNSR and SSIM indicators. This method not only effectively enhances image quality, but also exhibits superior performance in medical segmentation tasks, thereby providing a more reliable foundation for clinical diagnosis and image analysis.

Morphometric Analysis of Corpus Callosum in Individuals with Alzheimer's Disease: Magnetic Resonance Imaging (MRI) Study.

The Corpus Callosum (CC) is the largest commissural tract in the nervous system. Few studies have examined the extent of CC in Alzheimer's disease (AD) patients, and these studies have reported conflicting findings. The study was performed using 176 brain MRI images of 88 Alzheimer's patients (55 women-32 men) and 88 healthy individuals (44 women-44 men). In our study, 7 different parameters of the CC were measured, and their average values were determined. We measured each parameter separately in AD patients and healthy individuals and compared them with each other. CC has an important place not only in Patients with AD but also in other neurodegenerative diseases. We consider that our study will be useful in the evaluation of Patients with AD.

Case Report: Tolosa-Hunt syndrome, a neuroinflammatory origin of painful ophthalmoplegia

Background Tolosa-Hunt syndrome is a rare neuroinflammatory disease that affects the cavernous sinus and superior orbital fissure, resulting in symptoms of neuropathy. Case presentation We report a case of a 35-year-old male patient with no past medical history who presented to the emergency department (ED) complaining of a four-day history of progressively increasing headaches associated with right eye pain and double vision. A physical examination showed findings suggestive of abducens nerve palsy. A computed tomography (CT) scan showed subtle asymmetric thickening of the right cavernous sinus, which prompted further investigation by magnetic resonance imaging (MRI) scan. The MRI scan showed intensely enhanced soft tissue thickening in the right cavernous sinus region, slightly extending to the right orbital apex, which caused enlargement of the cavernous sinus and appeared isointense to the adjacent muscles on T1 and T2. The diagnosis of Tolosa-Hunt syndrome was made after the exclusion of all other possible pathologies. The patient received prednisolone therapy; his symptoms improved, and he was discharged after three days. Two weeks later, the patient presented again to the ED with the same symptoms and was treated symptomatically. Conclusions This case highlights a very rare etiology for painful ophthalmoplegia, which is Tolosa-Hunt syndrome. Thickening of the cavernous sinus on MRI images with a background history of painful ophthalmoplegia and headache should raise clinician’s suspicion towards the disease. In addition, exclusion of other possible causes is very important, as Tolosa-Hunt syndrome is a diagnosis of exclusion.

DUNET D UNET Dilated UNET for Brain Tumor Sub Region Segmentation using MRI Images

The precise diagnosis and treatment planning of brain tumors significantly rely on the accurate segmentation of sub-regions from Magnetic Resonance Imaging (MRI) data. In this research, we propose a framework, D-UNET (Dilated-UNET), which enhances the traditional UNET architecture by incorporating dilated convolutions. UNET is the deep CNN architecture widely adopted for biomedical image segmentation tasks. D-UNET is specifically designed for brain tumor sub-region segmentation from multi-modal (T1, T2, T1ce, Flair) MRI images in nifti file format, each comprising 155 slices. The framework comprises of four distinct steps viz. data collection, data preprocessing, model training, and outcome evaluation. D-UNET employs two key modules during training, the dilated encoding module and the dilated decoding module. These modules enable the model to efficiently capture multi-scale contextual information, facilitating better representation learning for complex and varied tumor sub[1]regions. We evaluated the performance of D-UNET using Intersection over Union and Dice Coefficient metrics. The experimental results demonstrate that D-UNET outperforms the traditional UNET and other benchmark models in terms of segmentation accuracy. Notably, D-UNET excels in capturing finer details and intricate shapes of tumor sub-regions, contributing to its superiority in brain tumor segmentation. The ability to precisely delineate tumor sub-regions from different modalities provides crucial insights for medical professionals in treatment planning and decision-making.

Evaluation of Femoral Head Avascular Necrosis With Virtual Noncalcium Dual-Energy Computed Tomography.

Our aim was to investigate the effectiveness of the dual-energy computed tomography (DECT) virtual noncalcium (VNCa) technique in avascular necrosis (AVN) for detecting bone marrow edema (BME) and staging. This prospective study included adult patients diagnosed with unilateral or bilateral femoral head AVN between January 2023 and December 2023, who had magnetic resonance imaging (MRI) and DECT. Two participants were excluded from the study due to undergoing surgical procedures during the period between the scans. Two reviewers, blinded to MRI images and clinical data, visually examined color-coded VNCa pictures to assess BME using a binary classification (0 = normal bone marrow, 1 = BME). Same 2 reviewers also used color-coded and nonmapped images to stage AVN in accordance to the "Association for Research on Osseous Circulation" (ARCO) staging system. Interobserver agreements for the visual evaluation and staging were calculated with κ coefficient. Following a visual assessment of BME and the staging of AVN, same 2 reviewers conducted CT density measurements on regions of BME regions utilizing DECT noncalcium images. An independent third investigator (reference standard) utilized MRI, x-ray, and clinical data to confirm the definitive diagnosis and staging of AVN. A P value less than 0.05 was considered statistically significant. Fifty patients (28 men, 22 women, mean age: 44.2 ± 13.1 years, range: 25-75 years) were included in the final analysis. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the VNCa technique in detecting BME were 96.0%, 94.4%, 97.9%, 89.4%, and 95.6%, respectively, for reviewer 1; and 96.0%, 88.9%, 96.0%, 88.9%, and 94.1%, respectively, for reviewer 2. Interobserver agreement was almost perfect ( κ = 0.84). Both reviewer 1 and reviewer 2 accurately classified 92.7% of the AVNs. The density measurements showed a statistically significant difference ( P = 0.001) between the edema regions and the normal marrow regions. No statistically significant difference was observed in the density measurements of edema regions at different stages ( P = 0.13). DECT VNCa technique exhibits excellent performance in detecting BME in hip AVN cases, as well as accurately determining the stage of AVN.

Association between Clinical Manifestations in Temporomandibular Joint Disorders and Corresponding Radiographic Findings.

Background: Temporomandibular disorders are common conditions characterized by discomfort within the temporomandibular joints, acoustic changes, and restricted mandibular movement. Accurate diagnosis and subsequent treatment rely heavily on clinical examination, but it is often necessary to add radiological examinations to the diagnostics. Magnetic Resonance Imaging (MRI) is the gold standard for visualizing the disc, while cone-beam computed tomography (CBCT) is primarily used for evaluating condylar morphology. Ultrasound (US) serves as a real-time imaging modality for soft tissues. The objective of the present study was to explore the association between clinical manifestations observed in patients with temporomandibular joint disorders and corresponding radiographic findings. Methods: A total of 63 adult patients (51 female and 12 male) with temporomandibular joint disorders were included in this cross-sectional study. Each patient underwent a clinical examination, followed by appropriate radiological examinations (MRI, CBCT, or US). The level of statistical significance was set at an alpha of 0.05. The Shapiro-Wilk test assessed the normality of numerical variables. The Wilcoxon rank sum test compared two independent groups with non-normally distributed data. Relationships between categorical variables were evaluated using the Pearson chi-square test or Fisher's exact test. The Kendall tau (τ) method analyzed the correlation between two binary variables. Results: The analysis included 63 patients with TMD, predominantly females (80.95%). Ages ranged from 18 to 74 years with a median of 39 years. In the CBCT study, we observed rarefied changes in the left bone structures in patients with bruxism (p = 0.010). MRI and ultrasound imaging revealed changes in patients with limited jaw opening: erosions in the right mandibular head on ultrasound (p = 0.008) and abnormal right bone structures on MRI (p = 0.009). In CBCT, asymmetry in the left joint space was correlated with a high incidence of right side muscle tension (p = 0.004). Additionally, both CBCT and ultrasound showed a correlation between muscle tension and erosion (p = 0.040 in ultrasound, p = 0.020 in CBCT). Acoustic changes, when compared with radiological imaging, were evident in all three studies, like temporomandibular joint pain or palpation. Conclusions: Our study compared three radiographic imaging methods with clinical examinations to assess their correlation with clinical symptoms. Each imaging technique provided unique insights depending on the specific symptoms presented. The observed correlations varied, highlighting the unique contributions of each modality to the diagnostic process. This underscores the importance of employing multiple diagnostic approaches for a thorough assessment of the temporomandibular joint. However, a limitation of our study is the small sample size and the uneven distribution of participants among the groups. Additionally, not all patients underwent every imaging modality.

Interactive Multi-scale Fusion: Advancing Brain Tumor Detection Through Trans-IMSM Model.

Multi-modal medical image (MI) fusion assists in generating collaboration images collecting complement features through the distinct images of several conditions. The images help physicians to diagnose disease accurately. Hence, this research proposes a novel multi-modal MI fusion modal named guided filter-based interactive multi-scale and multi-modal transformer (Trans-IMSM) fusion approach to develop high-quality computed tomography-magnetic resonance imaging (CT-MRI) fused images for brain tumor detection. This research utilizes the CT and MRI brain scan dataset to gather the input CT and MRI images. At first, the data preprocessing is carried out to preprocess these input images to improve the image quality and generalization ability for further analysis. Then, these preprocessed CT and MRI are decomposed into detail and base components utilizing the guided filter-based MI decomposition approach. This approach involves two phases: such as acquiring the image guidance and decomposing the images utilizing the guided filter. A canny operator is employed to acquire the image guidance comprising robust edge for CT and MRI images, and the guided filter is applied to decompose the guidance and preprocessed images. Then, by applying the Trans-IMSM model, fuse the detail components, while a weighting approach is used for the base components. The fused detail and base components are subsequently processed through a gated fusion and reconstruction network, and the final fused images for brain tumor detection are generated. Extensive tests are carried out to compute the Trans-IMSM method's efficacy. The evaluation results demonstrated the robustness and effectiveness, achieving an accuracy of 98.64% and an SSIM of 0.94.

Diagnosing the MRI brain tumour images through RNN-LSTM

According to the World Health Organization, cancer is the second most common cause of death worldwide (WHO). Death from cancer may sometimes be avoided through early diagnosis, although this is not always practicable. A tumour, unlike cancer, may be benign, pre-cancerous, or malignant. Contrary to malignant tumours, benign tumours may frequently be surgically removed and seldom metastasize to other organs or tissues. The method most often used to differentiate between various types of tumours is magnetic resonance imaging (MRI) (MRI). But because it depends on human subjectivity, it could be challenging for one person to see a lot of data. The radiologist's ability to identify brain tumours early mostly depends on their level of training. Before determining whether the tumour is benign or malignant, the diagnostic process for it could not be finished. A biopsy is often carried out to determine if the tissue is benign or cancerous. The biopsy of a brain tumour is often delayed until the last brain operation, in contrast to tumours discovered elsewhere in the body. An efficient diagnostics tool for tumour segmentation and classification from MRI images is crucial to obtaining accurate diagnoses, avoiding surgery, and eliminating subjectivity. The classification and segmentation of the tumour part of the MRI brain picture is the goal of this study. Prior to feature extraction in the first study, pre-processing is done using the Gray Level Co-occurrence Matrix (GLCM). Based on these features, the data is then separated into normal, glioma, meningioma, and pituitary groups. In prior studies, classification was handled by traditional Deep Neural Networks (DNN), with the best weights selected using optimization methods. Accordingly, a recent study recommends including Long Short-Term Memory based on Recurrent Neural Networks (RNN) (LSTM). The Local Direction Ternary Pattern (LDTP), the Gray Level Co-occurrence Matrix (GLCM), and the Le-Net features are all combined in the proposed Hybrid GLCM-LDTP-Le-Net Feature extraction approach. The hybrid parameters produced by the feature extraction process and the brain MRI data are used by the RNN-LSTM model to categorise the data as benign or malignant. By removing mixed features from the input data, the suggested hybrid feature extraction improves the learnt models' accuracy. The accuracy of 98.99 % of the suggested hybrid feature extraction approach was higher than the current CNN-based model methodologies.

Automatic detection and visualization of temporomandibular joint effusion with deep neural network

This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance imaging (MRI) in patients with temporomandibular disorder and whether the diagnostic accuracy of the model improved when patients’ clinical information was provided in addition to MRI images. The sagittal MR images of 2948 TMJs were collected from 1017 women and 457 men (mean age 37.19 ± 18.64 years). The TMJ effusion diagnostic performances of three convolutional neural networks (scratch, fine-tuning, and freeze schemes) were compared with those of human experts based on areas under the curve (AUCs) and diagnosis accuracies. The fine-tuning model with proton density (PD) images showed acceptable prediction performance (AUC = 0.7895), and the from-scratch (0.6193) and freeze (0.6149) models showed lower performances (p < 0.05). The fine-tuning model had excellent specificity compared to the human experts (87.25% vs. 58.17%). However, the human experts were superior in sensitivity (80.00% vs. 57.43%) (all p < 0.001). In gradient-weighted class activation mapping (Grad-CAM) visualizations, the fine-tuning scheme focused more on effusion than on other structures of the TMJ, and the sparsity was higher than that of the from-scratch scheme (82.40% vs. 49.83%, p < 0.05). The Grad-CAM visualizations agreed with the model learned through important features in the TMJ area, particularly around the articular disc. Two fine-tuning models on PD and T2-weighted images showed that the diagnostic performance did not improve compared with using PD alone (p < 0.05). Diverse AUCs were observed across each group when the patients were divided according to age (0.7083–0.8375) and sex (male:0.7576, female:0.7083). The prediction accuracy of the ensemble model was higher than that of the human experts when all the data were used (74.21% vs. 67.71%, p < 0.05). A deep neural network (DNN) was developed to process multimodal data, including MRI and patient clinical data. Analysis of four age groups with the DNN model showed that the 41–60 age group had the best performance (AUC = 0.8258). The fine-tuning model and DNN were optimal for judging TMJ effusion and may be used to prevent true negative cases and aid in human diagnostic performance. Assistive automated diagnostic methods have the potential to increase clinicians’ diagnostic accuracy.

A Machine Learning Prediction Model for Myelitis and Multiple Sclerosis Based on Fourier Transform Features from MRI Images

Myelitis is a neurodegenerative disease positioned in the spinal cord, with multiple sclerosis (MS) being a common subtype. Radiological indicators enable the diagnosis of these diseases. This study proposes a classification framework to detect myelitis, MS, and healthy control (HC) groups using magnetic resonance imaging (MRI) images. The feature extraction step involves applying the fast Fourier transform (FFT) to MRI images. FFT is important because it converts spatial data into the frequency domain, making it easier to identify patterns and abnormalities that indicate these diseases. Then, statistical features (mean, minimum, maximum, standard deviation, skewness, kurtosis, and total energy) are extracted from this frequency information. These features are then used to train support vector machine (SVM), k-nearest neighbor (KNN), and decision tree algorithms. In multi-class classification (myelitis vs. MS vs. HC), the proposed method achieves a classification accuracy of 99.31% with SVM, with average precision, recall, and F1-score values of 99.27%, 99.21%, and 99.24%, respectively, indicating effective classification across all classes. In the binary class classification (HC vs. MS, MS vs. myelitis, HC vs. myelitis), the SVM achieves an outstanding classification accuracy of 99.36%, 99.71%, and 100% respectively. This study highlights the efficiency of FFT-based feature extraction in forming detection patterns for classifying HC, MS, and myelitis classes.

Buckling of the Ligamentum Flavum in Patients with Lumbar Spinal Canal Stenosis.

Experimental study. We sought to elucidate the association between ligamentum flavum thickening and tissue buckling, and the clinical and imaging factors related to buckling by comparing the ligamentum flavum thickness on MRI images and within the actual tissue. Ligamentum flavum thickening is a main contributor to lumbar spinal canal stenosis. Buckling of the tissue may contribute to ligamentum flavum thickening along with tissue hypertrophy; however, this association has not been established conclusively. Ligamentum flavum samples (135 ligament samples) from 70 patients with lumbar spinal canal stenosis were evaluated. The ligamentum flavum thicknesses on magnetic resonance imaging (MRI) and in the tissue samples were compared to assess for the presence of buckling. The ligamentum flavum samples were divided into groups with or without buckling based on the difference between their thicknesses on MRI and in the tissues. The Pearson correlation coefficient test was used to assess the relationships between the LF thicknesses on MRI and in the tissues, MRI-tissue difference and LF thickness in the tissues, and MRI-tissue difference and LF thickness on MRI. Further, differences between the buckling+ and buckling- groups were compared using the unpaired t-test (LF thickness on MRI, LF thickness in the tissues, age, disc angle, and disc height) and χ2 (disc level, disc degeneration, and receival/nonreceival of dialysis) test. The ligamentum flavum thickness on MRI and in the tissues had a positive linear relationship, although the thickness was estimated to be significantly larger on MRI than in the tissues themselves. The ligamentum flavum with buckling had a larger thickness on MRI, less tissue hypertrophy, more severe disc degeneration, and was present in patients with a higher rate of dialysis. There were no differences in age and disc height, angle, or level between the two groups. Buckling of the ligamentum flavum coexists with tissue hypertrophy and contributes to perceived ligamentum thickening on imaging. Buckling of the ligamentum flavum tends to occur in less hypertrophied tissues and is associated with the grade of disc degeneration and the presence of other characteristics associated with spinal degeneration.

A Diffusion Tensor Imaging White Matter Atlas of the Domestic Canine Brain

Abstract There is increasing reliance on magnetic resonance imaging (MRI) techniques in both research and clinical settings. However, few standardized methods exist to permit comparative studies of brain pathology and function. To help facilitate these studies, we have created a detailed, MRI-based white matter atlas of the canine brain using diffusion tensor imaging. This technique, which relies on the movement properties of water, permits the creation of a three-dimensional diffusivity map of white matter brain regions that can be used to predict major axonal tracts. To generate an atlas of white matter tracts, thirty neurologically and clinically normal dogs underwent MRI imaging under anesthesia. High-resolution, three-dimensional T1-weighted sequences were collected and averaged to create a population average template. Diffusion weighted imaging sequences were collected and used to generate diffusivity maps, which were then registered to the T1-weighted template. Using these diffusivity maps, individual white matter tracts—including association, projection, commissural, brainstem, olfactory and cerebellar tracts—were identified with reference to previous canine brain atlas sources. To enable the use of this atlas, we created downloadable overlay files for each white matter tract identified using manual segmentation software. In addition, using diffusion tensor imaging tractography, we created tract files to delineate major projection pathways. This comprehensive white matter atlas serves as a standard reference to aid in the interpretation of quantitative changes in brain structure and function in clinical and research settings.