Tomography Magnetic Resonance Imaging Research Articles

Metaheuristic-enhanced Deep Learning Model for Accurate Alzheimer's Disease Diagnosis from MRI Imaging

Alzheimer’s Disease (AD) is the neuro-degenerative dementia, where the precise and early recognition of AD is vital for timely treatment to reduce mortality rate. A new automated model is implemented in this work for early discovery of AD in the Magnetic Resonance Imaging (MRI) brain scans. Initially, the input brain scans are taken from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. Further, the acquired raw brain scans are visually improved by employing the binary normalization technique. The denoised brain scans are fed to the pre-trained Convolutional Neural Network (CNN) named GoogleNet for feature extraction. Next, the extracted richer feature values are fed to the Long Short Term Memory (LSTM) network for classifying the brain scan as Normal Control (NC), Mild Cognitive Impairment (MCI) and AD. In this manuscript, a Honey Badger Optimization Algorithm (HBOA) technique is incorporated with the LSTM networks for hyper-parameters optimization, where this procedure helps in diminishing the LSTM network’s complexity and computational time. The experimental results conducted on the ADNI database underscore the HBOA-based LSTM network's effectiveness, showcasing a remarkable mean classification accuracy of 97.83% in multi-class classification. Moreover, the sensitivity of HBOA based LSTM for AD/NC is 96.73% which is high when compared to the existing methodologies such as SVM with radial basis kernel function and NCSINs. This performance surpasses that of other comparative models for AD detection, emphasizing the superior capabilities and potential of the proposed method in the early detection.

Image Analysis as tool for Predicting Colorectal Cancer Molecular Alterations: A Scoping Review.

Among the most important diagnostic indicators of colorectal cancer; however, measuring molecular alterations are invasive and expensive. This study aimed to investigate the application of image processing to predict molecular alterations in colorectal cancer. In this scoping review, we searched for relevant literature by searching the Web of Science, Scopus, and PubMed databases. The method of selecting the articles and reporting the findings was according to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses; moreover, the Strengthening the Reporting of Observational Studies in Epidemiology checklist was used to assess the quality of the studies. Sixty seven out of 2,223 articles, 67 were relevant to the aim of the study, and finally 41 studies with sufficient quality were reviewed. The prediction of Kirsten Rat Sarcoma Viral Oncogene Homolog (KRAS), Neuroblastoma RAS Viral (NRAS), B-Raf proto-oncogene, serine/threonine kinase (BRAF), Tumor Protein 53 (TP53), Adenomatous Polyposis Coli, and microsatellite instability (MSI) with the help of image analysis has received more attention than other molecular characteristics. The studies used computed tomography (CT), magnetic resonance imaging (MRI), and 18F-FDG positron emission tomography (PET)/CT with radionics and quantitative analysis to predict molecular alterations in colorectal cancer, analyzing features like texture, maximum standard uptake value, and MTV using various statistical methods. In 39 studies, there was a significant relationship between the features extracted from these images and molecular alterations. Different modalities were used to measure the area under the receiver operating characteristic curve for predicting the alterations in KRAS, MSI, BRAF, and TP53, with an average of 78, 81, 80 and 71%, respectively. This scoping review underscores the potential of radiogenomics in predicting molecular alterations in colorectal cancer through non-invasive imaging modalities, like CT, MRI, and 18F-FDG PET/CT. The analysis of 41 studies showed the appropriate prediction of key alterations, such as KRAS, NRAS, BRAF, TP53, and MSI, highlighting the promise of radionics and texture features in enhancing predictive accuracy.

Leveraging Vision Transformers for Early Detection of Alzheimer's disease Through MRI Image Analysis

Alzheimer's disease (AD) poses challenges for early recognition due to subtle and overlapping symptoms, making timely care and intervention difficult. This work presents a new method for identifying Alzheimer's disease by combining magnetic resonance imaging (MRI) data with a Vision Transformer (ViT) model. In this research, we develop a method that leverages MRI scans to classify Alzheimer's disease (AD) into four distinct stages: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. To improve the identification of clinical and structural alterations in the brain linked to AD, sophisticated characteristics are retrieved, including voxel-intensity patterns, grey matter volume, and cortical thickness. These characteristics allow for a more accurate representation of MRI data when paired with sophisticated processing methods. Additionally, even in situations where data is scarce, transfer learning is used to maximize the Vision Transformer (ViT) model's classification accuracy, especially when it comes to differentiating between neighboring stages of dementia. The ViT model, a cutting-edge deep learning technique, shows strong performance and dependability in identifying AD phases. Our findings highlight its effectiveness, showcasing a significant improvement in diagnostic accuracy compared to existing methods. The accuracy with which the ViT model can distinguish between different phases of AD emphasizes both its suitability for handling complicated MRI data and its promise for early diagnosis and detection.This paper offers a promising method for enhancing MRI image processing for Alzheimer's disease diagnosis through sophisticated feature extraction, transfer learning, and the use of Vision Transformers (ViTs). Present authors believe this method successfully tackles the difficulties of early and precise detection, making it essential for appropriate medical image analysis.

Bifunctional Nanoassembly Enables Metabolism-Driven Microfluidic Blood Screening Guided by MRI Localization for Cancer Monitoring.

Early detection and precise tumor localization are critical for improving treatment outcomes and enabling more targeted and minimally invasive therapies as biotechnology evolves. However, endogenous biomarkers from early lesions face significant challenges, such as short circulation times and blood dilution, which hinder early diagnostic efforts. In this study, we present a multimodal nanosensor specifically engineered to target cancer by responding to CD44 and tumor-associated enzymes within the microenvironment. Following systemic administration, the nanosensor selectively accumulates at the disease site, delivering hexaminolevulinate (HAL) to produce protoporphyrin IX (PpIX) as a synthetic biomarker, thus amplifying disease signals for analysis via a microfluidics-based device. Concurrently, embedded Gd2O3 nanoclusters facilitate tumor visualization through magnetic resonance imaging (MRI). Beyond tumor diagnosis, this innovative methodology supports the multimodal monitoring of drug response through the assessment of blood reporter signals and MRI imaging. This multifunctional system addresses critical limitations in traditional cancer diagnostics, which typically rely on sequential blood biomarker tests, followed by imaging. Our approach enhances diagnostic efficiency, minimizes the need for invasive procedures, and promotes more accurate and personalized cancer care.

An algorithm for cardiac disease detection based on the magnetic resonance imaging

In experiments to detect heart disease on cardiac magnetic resonance imaging (MRI) medical images, existing object detection models face several challenges including low accuracy and unreliable detection results. To tackle these issues, this article proposes an innovative method for Object Detection in cardiac MRI medical images called SA-YOLO. This method is based on the YOLOv8 model but introduces several key modifications. Firstly, the standard Spatial Pyramid Pooling Fast module is replaced with a Multi-Channel Spatial Pyramid Pooling module. Secondly, an attention mechanism combining the ideas of Squeeze-Excitation and Coordinate Attention designed, and integrated into the Neck part of the baseline model. Subsequently, the bounding box regression loss function CIoU loss of the model was replaced with the iSD-IoU loss that combines shape loss and distance loss. Finally, comparative experiments were conducted on the Automated Cardiac Diagnosis Challenge cardiac MRI image dataset where it was found that SA-YOLOv8 achieved better results in detecting cardiac pathologies, and improvement of 7.4% in mAP0.5 value and 5.1% in mAP0.5-0.95 value compared to the baseline model.

Diagnosis of lymph node metastasis in oral squamous cell carcinoma by an MRI-based deep learning model.

Cervical lymph node metastasis (LNM) is a well-established poor prognosticator of oral squamous cell carcinoma (OSCC), in which occult metastasis is a subtype that makes prediction challenging. Here, we developed and validated a deep learning (DL) model using magnetic resonance imaging (MRI) for the identification of LNM in OSCC patients. This retrospective diagnostic study developed a three-stage DL model by 45,664 preoperative MRI images from 723 patients in 10 Chinese hospitals between January 2015 and October 2020. It was comprehensively processed from training (8:2), multicenter external validation to reader study. The performance of the DL model was accessed and compared with general and specialized radiologists. LNM was found in 36.51% of all patients, and the occult metastasis rate was 16.45%. The three-stage DL model together with a random forest classifier achieved the performance in identification of LNM with areas under curve (AUC) of 0.97 (0.93-0.99) in training cohort and AUC of 0.81 (0.74-0.86) in external validation cohorts. The models can reduce the occult metastasis rate up to 89.50% and add more benefit in guiding neck dissection in cN0 patients. DL models tied or exceeded average performance relative to both general and specialized radiologists. Our three-stage DL model based on MRI with three-dimensional sequences was beneficial in detecting LNM and reducing the occult metastasis rate of OSCC patients.

MEPs and MRI Motor Band Sign as Potential Complementary Markers of Upper Motor Neuron Involvement in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is characterized by the degeneration of both upper and lower motor neurons (UMNs and LMNs). Recognizing the involvement of UMNs is challenging because of the absence of reliable biomarkers beyond clinical evaluation. To identify a reliable marker of UMN damage in a cohort of patients with ALS referring to the Motor Neuron Disease Clinic of the University Hospital of Padova. We retrospectively evaluated the clinical records of 79 patients with ALS and compared the results of various investigations, including the motor-evoked potentials (MEPs), positron emission tomography-magnetic resonance imaging (MRI) and light neurofilaments (NfLs), with the degree of UMN clinical involvement, as assessed by the Penn Upper Motor Neuron Score (PUMNS). MEPs, considering the central motor conduction time (CMCT) values in both the upper and lower limbs, showed a significant correlation with the relative PUMNS subscores (p = 0.01, ρ = 0.4; and p = 0.005, ρ = 0.45, respectively). Additionally, there was a positive correlation between NfLs and PUMNS values (p = 0.04, ρ = 0.33). The presence of the motor band sign on MRI was associated with higher PUMNS values. Receiver operating characteristic analysis revealed that PUMNS accurately predicted abnormalities in CMCT values (specificity 86%, sensitivity 62%) and the presence of the motor band sign (specificity 58%, sensitivity 80%). In our cohort of patients with ALS, CMCT values proved to be the most reliable test for assessing UMN involvement, albeit the presence of the motor band sign on MRI showed higher sensitivity.

Transforming Alzheimer’s Disease Diagnosis: Implementing Vision Transformer (ViT) for MRI Images Classification

Alzheimer’s disease (AD) is a progressive neurological disorder and the leading cause of dementia, accounting for 60-80% of cases. Early detection of AD is crucial for timely intervention, as the disease significantly impacts cognitive functions and daily activities. Diagnosing AD in clinical settings remains challenging due to subtle early symptoms, diverse presentations, lengthy diagnostic processes, and inconsistent criteria that heavily rely on medical expertise. Accurate and timely diagnosis during the early stages is essential for effective treatment and intervention. Modern imaging techniques, such as Magnetic Resonance Imaging (MRI), have become essential in diagnosing Alzheimer’s by providing detailed insights into structural brain changes. This study explores the application of the Vision Transformer (ViT) model for classifying MRI images of Alzheimer’s patients, focusing on enhancing accuracy and reliability through data augmentation during pre-processing. A dataset of 8,000 MRI images, categorised into four groups—non-demented, very mild demented, mild demented, and moderate demented—was used to evaluate the ViT model. The experiment achieved promising results, with an accuracy of 98.19%, sensitivity of 96.34%, specificity of 98.80%, and an F1-score of 96.37%. These findings underscore the model’s effectiveness in distinguishing between affected and unaffected individuals, minimising misdiagnosis and enabling timely clinical interventions. However, some challenges remain, particularly in the classification between “Non-Demented” and “Very Mild Demented” cases. Future research should focus on enhancing data augmentation techniques and increasing data diversity in these categories to improve the model’s performance further. The ViT model holds great potential for advancing Alzheimer’s diagnosis, offering a valuable tool for early detection and intervention in clinical settings.

A Study on the Classification of Brain MRI Images for Brain Tumor Detection

Accurate classification of brain tumors is essential for effective diagnosis, as it directly affects treatment choices, patient outcomes, and survival rates. Early and precise detection enables timely interventions, reducing the risk of tumor progression. Without reliable classifiers, traditional feature extraction techniques have drawbacks. Our study suggests a hybrid model that incorporates the advantages of Random Forest Classifier (RFC), Support Vector Machine (SVM), and Visual Geometry Group (VGG16) to improve classification performance. To improve feature extraction, the Magnetic resonance imaging (MRI) images are pre-processed. This includes pixel intensity. To improve data diversity, augmentation techniques such as random flip, random height, random width, horizontal flip, and vertical flip are used. Next, a unique Deep Convolutional Neural Networks (DCNN) that is VGG16 is created to extract significant deep features. Evaluation of the model's performance using various optimizers revealed that the RMSprop optimizer outperformed models employing Adam (80.39%) and SGD (64.71%), achieving the highest validation accuracy (82.35%). SVM obtained a validation accuracy of 47.06%, while RFC obtained 64.71%. These results show the importance of classifier and optimizer selection. This study highlights the efficacy of the VGG16 model with the RMSprop optimizer and shows the potential of integrating deep learning and conventional machine learning approaches for brain tumor classification. It demonstrates the potential of combining deep learning and traditional machine learning techniques for brain tumor classification, highlighting effectiveness of the VGG16 model with the RMSprop optimizer while emphasizing the need for further exploration of optimizers and classifiers to enhance overall model performance and robustness.

Abdominal and Pelvic MRI Protocol Prediction Using Natural Language Processing.

Exam protocoling is a significant non-interpretive task burden for radiologists. The purpose of this work was to develop a natural language processing (NLP) artificial intelligence (AI) solution for automated protocoling of standard abdomen and pelvic magnetic resonance imaging (MRI) exams from basic associated order information and patient metadata. This Institutional Review Board exempt retrospective study used de-identified metadata from consecutive adult abdominal and pelvic MRI scans performed at our institution spanning 2.5years from 2019 to 2021 to fine-tune an AI model to predict the exam protocol. The NLP algorithm Bidirectional Encoder Representations from Transformers (BERT) was employed in sequence classification mode. Twelve months of data from the COVID pandemic were excluded to avoid bias from known practice and referral pattern disruptions, with approximately 46,000 MRI exams in the resulting cohort. The final trained model had an accuracy of 88.5% with a Matthews correlation coefficient of 0.874, a true positive rate of 0.872, and a true negative rate of 0.995. Subsequent expert review of the errors performed to satisfy departmental leadership showed 81.9% were in fact correct or reasonable alternative protocols, yielding real-world performance accuracy of 97.9%. We conclude that NLP algorithms, including "smaller" large language models like the BERT family often overlooked today, can predict MRI imaging protocols for the abdomen and pelvis with high real-world performance, offering to decrease radiologists' non-interpretive task load and increasing departmental efficiency.

P-904. Spinal Arachnoiditis and Syringomyelia in Coccidioidomycosis: A Case Series of 53 Adult Patients

Abstract Background Central nervous system (CNS) involvement with coccidioidomycosis (cocci) is a serious infection that is universally fatal if not treated. Prior to the advent of magnetic resonance imaging (MRI), very few reports describe spinal cord involvement and autopsies often omitted spinal cord examination. Accurate and anatomic localization of areas affected by CNS cocci can be challenging due to mental status changes and the presence of brain abnormalities. Here, we describe radiologic and clinical characteristics of 53 cases of CNS cocci who had spinal MRI imaging performed. Methods This retrospective case series reviewed 53 adult patients at the Valley Fever Institute in Bakersfield, California. Patients had confirmed CNS cocci and MRI of at least one segment of the spinal image with and without contrast. A waiver of consent was submitted, and approval was obtained by Kern Medical’s Institutional Review Board. ICD 9 and ICD 10 codes were used to query electronic health record and cross referenced with imaging. Each record reviewed required inclusion criteria of age above 18 years, cerebrospinal fluid (CSF) abnormalities compatible with chronic meningitis and one of the following: positive CSF IgG antibody, CSF complement fixation (CF) or growth of Coccidioides on CSF culture. MRI Imaging with and without contrast of at least one of the following: cervical, thoracic or lumbar spine. Results 77% of the patients were Latinx, 13% African American, 4% Asian and 6% Caucasian. Males make up the majority at 72%. 37% had intrathecal (IT) amphotericin (ABD) injections. Spinal cord abnormalities can be asymptomatic with the most common symptom of back pain followed by radiculopathy. Abnormal findings included leptomeningeal enhancement, arachnoid cysts, masses or adhesions, syringomyelia, myelitis, cord edema, myelomalcia, flattened cord, nerve root "clumping" or solid cauda equina. Conclusion This study suggests the benefit of obtaining neuro axis imaging with and without contrast on all new cases of Coccidioidal meningitis. Spinal cord involvement with CNS cocci is common and contributes to significant morbidity. Contrary to common belief, our study shows that spinal arachnoiditis occurs in patients who were never treated with IT ABD. This may suggest that IT ABD may not cause arachnoiditis. Disclosures All Authors: No reported disclosures

Delta-radiomics analysis based on magnetic resonance imaging to identify radiation proctitis in patients with cervical cancer after radiotherapy

ObjectivesTo develop a magnetic resonance imaging (MRI)-based radiomics model for predicting the severity of radiation proctitis (RP) in cervical cancer patients’ post-radiotherapy.MethodsWe retrospectively analyzed clinical data and MRI images from 126 cervical squamous cell carcinoma patients treated with concurrent chemoradiotherapy. Logistic regression (LR), Pearson correlation coefficient, and least absolute shrinkage and selection operator (LASSO) methods were utilized to select optimal imaging features, leading to a combined prediction model developed using a random forest (RF) algorithm. Model performance was assessed using the area under the curve (AUC), DeLong test, calibration curve, and decision curve analysis (DCA), with Shapley Additive exPlanations (SHAP) values for interpretation.ResultsThe samples were split into training (70%) and validation (30%) sets. The delta-radiomics model, comprising 10 delta features, showed strong predictive performance (AUC: 0.92 for training and 0.90 for validation sets). A comprehensive model combining delta-radiomics with clinical features outperformed this, achieving AUCs of 0.99 and 0.98. DeLong’s test confirmed the comprehensive model’s statistical superiority, and both calibration curves and DCA indicated good calibration and high net benefit. Key features associated with RP included D1cc, T1_wavelet-LLL_glcm_MCC, D2cc, and T2_original_firstorder_90 Percentile.ConclusionsThe MRI-based delta radiomics model shows significant promise in predicting RP severity in cervical cancer patients following radiotherapy, with enhanced predictive performance when combined with clinical features.

P-1129. Exploring Cost Savings through Reduced Sedation for Magnetic Resonance Imaging in Pediatric Osteomyelitis Diagnosis

Abstract Background Magnetic resonance imaging (MRI) is essential for the diagnostic workup of pediatric osteoarticular infection. In the pediatric population, sedation is often required to mitigate motion artifact and improve imaging quality, which contributes to cost, resource utilization, and risk to the patient. In 2021, our hospital implemented an evidence-based update to the pediatric osteomyelitis guideline adjusting MRI imaging sequences, which reduced sedation age requirements from 8 years to 3 years of age. This change was initiated in September 2021. This study evaluates economic implications of sedation reduction strategies in pediatric osteomyelitis imaging. Figure 1. Pre vs Post Guideline Sedation Rates. Methods This is a retrospective chart review study of pediatric patients evaluated for bone and joint infection by MRI at a free standing 299 bed hospital in Central Texas. Cases were pulled by MRI indication according to the hospital’s osteomyelitis guideline inclusion criteria between January 1, 2020 and December 31, 2023. Previously healthy children age 6 months to 18 years with physical exam suggestive of acute hematogenous osteomyelitis or septic joint and less than 14 days of symptoms were included. Cases were compared by year pre-guideline (2020-2021) and post-guideline (2022-2023) and reviewed for age, sex, need for sedation, and diagnosis after MRI. Cost savings estimates were based on an average per case cost of sedation. The cost of sedation was determined by hospital charges. Figure 2. Pre & Post Guideline Sedated vs Non-Sedated Patients. Results 279 patients were included, (98 pre guideline change; 181 post guideline change). 61.3% male, 38.7% female, median age 6, IQR 6.5. A statistically significant reduction in sedation usage was observed after guideline implementation (50%-33%, p=0.0087) (fig 1). Among patients 3-8 years of age, 21 of 34 patients (62%) pre-guideline and 15 of 78 patients (19%) post- guideline received sedation (p< 0.001) (fig 2). An average estimated cost savings of $270,215 per year was found. There was no increase in repeat MRI rates seen with the implementation of the age requirement change. Conclusion Lowering the required age for sedation allows for individualized assessment of sedation needs in the 3-8 age group. This leads to improved patient care through decreased patient risk, reduction in overall rate of sedation and results in cost savings. Disclosures Brian Kaufman, MD, Johnson & Johnson: Advisor/Consultant

Evaluation of the Femoral Condyle Radius of Curvature at the Chondral Surface Shows Significant Correlation With the Anterior-Posterior Length.

Accurate donor-recipient matching of the femoral condyle radius of curvature (ROC) in osteochondral allograft (OCA) transplantation may aid in minimizing articular surface incongruities. Matching linear femorotibial dimensions, such as the femoral condyle anterior-posterior length (APL), femoral condyle width (lateral-medial length, LML), femoral hemicondyle width (HCW), and tibial plateau width (TPW), can provide similar results if they correlate well with ROC. This study investigates the relationship between femorotibial dimensions and ROC at the cartilage surface using magnetic resonance imaging (MRI). Deidentified MRI images of 63 patients (35 men, 33 ± 10 years old, and 28 women, 27 ± 8 years old) were analyzed. Axial images were used for APL, LML, and TPW (TPW-Ax) measurements, while coronal images were used for HCW and TPW (TPW-Cor) measurements. Cartilage was segmented in true sagittal images at the medial femoral condyle (MFC) and lateral femoral condyle (LFC) to calculate their specific cartilage surface ROCs. Linear regression models were used to determine the relationship between the femorotibial dimensions and ROC. Cartilage ROC was significantly correlated with all the linear femorotibial dimensions at the MFC (P < 0.01, R2 = 0.78, 0.69, 0.67, 0.59, and 0.37 for ROC correlations with APL, LML, TPW-Ax, TPW-Cor, and HCW, respectively), and the LFC (P < 0.01, R2 = 0.81, 0.61, 0.56, 0.54, and 0.41 for ROC correlations with APL, LML, TPW-Ax, TPW-Cor, and HCW, respectively). The APL was the most predictive femorotibial dimension of the cartilage surface ROC. Donor-recipient APL matching in OCA transplantation may provide a similar level of matching to that achievable by direct ROC measurements. The APL matching may help reduce cartilage incongruities, particularly for patients with large osteochondral lesions, in which the ROC measurement cannot be accurately determined.

P-444. Cerebral Small Vessel disease risk in people with HIV and substance use disorder

Abstract Background Cerebral small vessel disease (CSVD) is associated with an increased risk of stroke, dementia, and death. The risk of CSVD in people with HIV (PWH) is twice the general population but the CSVD risk in PWH with substance abuse is poorly defined. With national surveys showing approximately two-thirds of PWH admitting to using an illicit drug in their lifetime, the evaluation of CSVD risk in this population is needful.Table 1:Demographic and Clinical information of CSVD in PWH with substance use disorder (N=23) Methods We retrospectively analyzed brain magnetic resonance imaging (MRI) findings of PWH attending our ambulatory HIV needle exchange clinic. Brain MRIs done in the previous 5 years before study analysis were included. CSVD was determined by Cerebral small vessel disease score (0 to 4), assigning 1 point each for the following: (i) presence of lacunar infarct, (ii) microbleeds, (iii) prominent perivascular spaces, and (iv) white matter hyperintensities. The findings suggestive of CSVD such as age-related white matter changes and chronic microvascular ischemia were also included in the CSVD assessment. Substance use disorder (uncontrolled use of a substance despite harmful consequences) was determined to be present if this was included on the patient diagnosis list.Table 2:Cerebral Small Vessel disease Score in PWH with substance use disorder. 5 participants had a CSVD score of 0 but "age related white matter disease" on Brain MRI Results 51 patients (PWH with substance use disorder) were included in the analysis. 23 participants out of this group had brain MRI imaging out of which 12 (52.2%) had CSVD. The mean age was 50 years. The demographic and clinical data of the cohort are shown in Table 1. The CSVD score ranged from 0 to 3 with an average score of 1. Table 2 illustrates the CSVD score of the cohort. 5 participants had a CSVD score of 0 but a brain MRI report of “age-related white matter changes”. The Pearson Correlation between age and CSVD score was 0.057 with a p-value of 0.86. Conclusion 52% of PWH who use substance have CSVD on MRI brain imaging. This could translate to an increased risk of stroke, cognitive impairment, and death if not recognized early and aggressively addressed. CSVD score provides objective quantification of CSVD but age-related white matter changes, which are not captured on this score may be equally important especially as PWH continues to age. A systematic approach for screening for CSVD in PWH with substance use disorder is warranted to minimize the unfavorable outcomes associated with this cohort. Disclosures All Authors: No reported disclosures

A realistic human head phantom for electromagnetic detection of brain diseases.

The research on electromagnetic detection technology for brain diseases requires precise simulation of the human head. This article combines high-precision computed tomography (CT) images and magnetic resonance imaging (MRI) images to establish an electromagnetic numerical model of the human head with a real anatomical structure. (1) It had Asian characteristics and encompassed 14 different structures, including skin, muscles, cranial bones, cerebrospinal fluid, cerebral veins, cerebral arteries, gray matter, white matter of the brain, basal ganglia, thalamus, cerebellum, brainstem, eyeballs, and vertebrae. (2) The model used a combination of 0.625 mm-resolution CT and 1 mm-resolution MRI image data for reconstruction, with a smooth surface and high accuracy. (3) Within the simulation environment, this model enabled the generation of various brain disease scenarios, such as different types and degrees of cerebral hemorrhage and cerebral ischemia. It proved valuable for studying the distribution of electromagnetic fields in the human head and for investigating novel electromagnetic detection techniques exploiting brain tissue dielectric properties. (4) The created physical model and the numerical model were derived from the same person, which provided a good continuity between simulation experiments and physical experiments, and provided a realistic verification platform for the research of electromagnetic detection technology for brain diseases, such as differentiating the kind of stroke, monitoring brain edema, brain tumor microwave imaging, and diagnosis of Alzheimer's disease.

Deep Learning for Lumbar Disc Herniation Diagnosis and Treatment Decision Making Using MRI Images: A Retrospective Study.

Lumbar disc herniation (LDH) is a common cause of back and leg pain. Diagnosis relies on clinical history, physical exam, and imaging, with magnetic resonance imaging (MRI) being an important reference standard. While artificial intelligence (AI) has been explored for MRI image recognition in LDH, existing methods often focus solely on disc herniation presence. We retrospectively analyzed MRI images from patients assessed for surgery by specialists. We then trained deep learning convolutional neural networks (CNNs) to detect LDH on MRI images. This study compared pure AI, pure human, and AI-assisted approaches for diagnosis accuracy and decision time. Statistical analysis evaluated each method's effectiveness. Our approach demonstrated the potential of deep learning to aid LDH diagnosis and treatment. The AI-assisted group achieved the highest accuracy (94.7%), outperforming both pure AI and pure human approaches. AI integration reduced decision time without compromising accuracy. CNNs effectively assist specialists in initial LDH diagnosis and treatment decisions based on MRI images. This synergy between AI and human expertise improves diagnostic accuracy and efficiency, highlighting the value of AI-assisted diagnosis in clinical practice.

Morphology and Distribution of Fat Globules in Osteomyelitis on Magnetic Resonance Imaging.

The purpose of this study was to investigate the morphology and distribution characteristics of fat globules in osteomyelitis on magnetic resonance imaging (MRI). Patients with pathologically-confirmed osteomyelitis and MRI scans were retrospectively enrolled, and fat globules on the MRI images were analyzed. Among 103 patients with non-traumatic osteomyelitis, 75 were fat globule negative and 28 were positive. There was no statistically significant difference in age and gender between patients with and without fat globules (p>0.05). The inflammatory indicators (CRP, ESR, WBC, and NEUT) in the fat globule positive group were significantly higher (p<0.05) than those in the negative group. The lesions were mainly located in the long bones of the limbs in patients with positive fat globules. Twenty-eight patients (27.2% or 28/103) were detected to have fat globules on MRI images, including 20 males (71%) and 8 females (29%) aged 5-64 years (mean 16 years). The time from onset to MRI examination was 8 days to 4 months. The location of fat globules was in the tibia in 10 patients (35.7%), femur in 8 (28.6%), humerus in 4 (14.3%), radius in 2 (7.1%), ulna in 1 (3.6%), calcaneus in 1 (3.6%), sacrum in 1 (3.6%), and fibula in 1 patient (3.6%). On MRI imaging, 28 cases (100%) showed widely distributed patches or tortuous and sinuous abnormal signals in the bone marrow. In 25 cases (89.2%), a grid-like abnormal signal was found in the subcutaneous soft tissue. In 21 patients (75%), pus was found in the adjacent extraosseous soft tissues. Among 28 patients with fat globules, 17 patients (60.7%) had fat globules only in the adjacent extraosseous soft tissue, 6 patients (21.4%) had only intraosseous fat globules [including 5 cases with halo signs around the fat globules and 1 case (3.6%) with fat globules located at the edge of the pus cavity inside the bone without a halo sign], and 5 patients (17.8%) had both intraosseous and extraosseous fat globules. Of 6 patients (21.4% or 6/28) with liquid levels, the liquid level appeared outside the bone. The appearance of fat globules on MRI in patients with osteomyelitis indicates severe infection. Fat globules of osteomyelitis may present with diverse shapes inside and outside the bone marrow as one of the MRI signs of osteomyelitis, with a probability of approximately 27.2%. They have high specificity in diagnosing osteomyelitis and can be used for diagnosis and differential diagnosis.

O-04 A CASE OF FUNCTIONAL GONADOTROPINOMA PRESENTING WITH OVARIAN MASSES

Abstract Introduction Most gonadotropinomas are nonfunctional macroadenomas, usually diagnosed by immunohistochemical staining. Here, a rare case of functional gonadotropinoma will be presented. Clinical Case A 37-year-old female patient applied to the neurology clinic with a severe headache, and a macroadenoma was detected on cranial magnetic resonance imaging(MRI). She was referred to our endocrinology clinic. Her menstrual cycle was irregular, and she had frequent intermenstrual bleeding for about a year. She has never had galactorrhea, and there were no features of acromegaly or Cushing’s disease on physical examination that would suggest a functional adenoma. The biochemical evaluation showed an elevated estradiol (E2) level of 2900 ng/L (normal 12,5 –166 ng/L) and an elevated prolactin(PRL) level of 203 µg/L (normal 2,8-29,2 µg/L) with normal follicular-stimulating hormone (FSH), luteinizing hormone (LH) and beta-human chorionic gonadotropin (b-hCG) levels of 2,8 IU/L, 6,6 IU/L and 0.1 mIU/mL respectively. PRL level was 196 µg/L after dilution. The low-dose dexamethasone suppression test was 2.1 µg/dL.The patient’s laboratory results are given in Table 1. Pituitary MRI revealed a 31*20*28 mm lesion invading both cavernous sinuses and compressing the optic nerve on the left side(Figure 1A). Visual field testing showed no visual field defects. The patient was consulted with the gynecology and obstetrics clinic due to high estradiol levels. Pelvic MRI revealed 5-6 cystic lesions, the largest of which was 45*30 mm in the right ovary and 41*37 mm in the left ovary, which did not enhance contrast and did not contain solid components (Figure 1B). The patient, who was considered for surgery due to suspicion of malignancy, was planned to be closely followed up by gynecology because tumor markers were negative and the lesions did not enhance contrast. While the patient’s gynecological examinations were continuing, the patient was started on cabergoline one tablet/week. After the gynecology department decided to follow up on the lesions, the patient underwent TSS for macroadenoma. Immunohistochemical staining was positive for FSH and LH, negative for PRL and adrenocorticotropic hormone (ACTH), and the ki-67 index was %2. The patient’s E2, cortisol, and PRL levels became in the normal range after the operation, and her hormone levels are given in Table 1. A pelvic ultrasound performed six months after the operation revealed that the previously detected lesions in the ovaries had disappeared. At this stage, the patient was considered to have functional gonadotropinoma and cortisol elevation has been linked to the effect of estradiol. Conclusion In patients with pituitary macroadenoma, it would be beneficial to check pituitary-gonadal axis hormones even if they are not in the follicular phase to avoid missing functional gonadotropinomas and prevent unnecessary surgeries for ovarian pathologies that may be observed in patients with functional gonadotropinomas.Figure 1:Pituitary and pelvic MRI images of the patientA: Pituitary MRI revealed a 31*20*28 mm lesion invading both cavernous sinuses and compressing the optic nerve on the left side. B: Pelvic MRI revealed 5-6 cystic lesions, the largest of which was 45*30 mm in the right ovary and 41*37 mm in the left ovary, which did not enhance contrast and did not contain solid components. Table 1:Laboratory results of the patientTSS: transsphenoidal surgery, ACTH: Adrenocorticotropic hormone, FSH: follicle stimulating hormone, LH: luteinizing hormone, IGF-1: Insülin-like growth factor, TSH: Thyroid stimulating hormone, fT4: free thyroxie fT3: free triiodothyronine, beta HCG: beta-human chorionic gonadotropin

Altered medial temporal lobe subregion volumes in systemic lupus erythematosus patients with neuropsychiatric symptoms

BackgroundSystemic lupus erythematosus (SLE) often presents with neuropsychiatric (NP) involvement, including cognitive impairment and depression. Past magnetic resonance imaging (MRI) research in SLE patients showed smaller hippocampal volumes but did not investigate other medial temporal lobe (MTL) regions. Our study aims to compare MTL subregional volumes in SLE patients to healthy individuals (HI) and explore MTL subregional volumes in relation to neuropsychiatric SLE (NPSLE) manifestations.MethodsA total of 70 SLE patients and 25 HI underwent clinical evaluation, cognitive testing, and 3 tesla MRI imaging. T1-weighted MRI images were analyzed using the Automatic Segmentation of Hippocampal Subfields-T1 software. Analyses of Covariance were used to compare MTL subregion volumes between SLE and HI, and between NPSLE and non-NPSLE patients according to three models: the American College of Rheumatology (ACR) model defined by the ACR case definitions for NPSLE (n = 42), the more stringent Systemic Lupus International Collaborating Clinics (SLICC) B model (n = 21), and the most stringent SLICC A model (n = 15). Additionally, we explored the relation between MTL subregion volumes, cognitive functions, and depression scores in SLE patients using partial correlation analyses.ResultsSignificantly smaller volumes of bilateral whole hippocampus, anterior hippocampus, posterior hippocampus, and Brodmann Area 35 were demonstrated in NPSLE compared to non-NPSLE patients according to the ACR model (p = 0.01, p = 0.03, p = 0.04, and p = 0.01 respectively). The differences did not reach significance according to the SLICC B and SLICC A models. No significant differences in MTL subregional volumes between SLE patients and HI were found. Partial correlation analyses showed a significant positive correlation between left Brodmann Area 35 volume and complex attention scores in SLE patients. No significant associations between MTL subregion volumes and depression scores were demonstrated.ConclusionsNPSLE patients display significantly smaller volumes in various subregions of the MTL compared to non-NPSLE patients. These findings are suggestive of neuronal damage in MTL subregions in NPSLE patients on a group level.