Tomography Magnetic Resonance Imaging Research Articles

Automatic segmentation of tumour and organs at risk in 3D MRI for cervical cancer radiation therapy with anatomical variations.

Cervical cancer is a common cancer in women globally, with treatment usually involving radiation therapy (RT). Accurate segmentation for the tumour site and organ-at-risks (OARs) could assist in the reduction of treatment side effects and improve treatment planning efficiency. Cervical cancer Magnetic Resonance Imaging (MRI) segmentation is challenging due to a limited amount of training data available and large inter- and intra- patient shape variation for OARs. The proposed Masked-Net consists of a masked encoder within the 3D U-Net to account for the large shape variation within the dataset, with additional dilated layers added to improve segmentation performance. A new loss function was introduced to consider the bounding box loss during training with the proposed Masked-Net. Transfer learning from a male pelvis MRI data with a similar field of view was included. The approaches were compared to the 3D U-Net which was widely used in MRI image segmentation. The data used consisted of 52 volumes obtained from 23 patients with stage IB to IVB cervical cancer across a maximum of 7 weeks of RT with manually contoured labels including the bladder, cervix, gross tumour volume, uterus and rectum. The model was trained and tested with a 5-fold cross validation. Outcomes were evaluated based on the Dice Similarity Coefficients (DSC), the Hausdorff Distance (HD) and the Mean Surface Distance (MSD). The proposed method accounted for the small dataset, large variations in OAR shape and tumour sizes with an average DSC, HD and MSD for all anatomical structures of 0.790, 30.19mm and 3.15mm respectively.

Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images.

Magnetic Resonance Imaging (MRI) plays an important role in neurology, particularly in the precise segmentation of brain tissues. Accurate segmentation is crucial for diagnosing brain injuries and neurodegenerative conditions. We introduce an Enhanced Spatial Fuzzy C-means (esFCM) algorithm for 3D T1 MRI segmentation to three tissues, i.e. White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The esFCM employs a weighted least square algorithm utilizing the Structural Similarity Index (SSIM) for polynomial bias field correction. It also takes advantage of the information from the membership function of the last iteration to compute neighborhood impact. This strategic refinement enhances the algorithm's adaptability to complex image structures, effectively addressing challenges such as intensity irregularities and contributing to heightened segmentation accuracy. We compare the segmentation accuracy of esFCM against four variants of FCM, Gaussian Mixture Model (GMM) and FSL and ANTs algorithms using four various dataset, employing three measurement criteria. Comparative assessments underscore esFCM's superior performance, particularly in scenarios involving added noise and bias fields.The obtained results emphasize the significant potential of the proposed method in the segmentation of MRI images.

Exploring Deep Relationship Learning for Regression: A Case Study on Brain Age Estimation

To understand how the brain develops and matures, an accurate age estimate utilizing MRI pictures is essential. There has been encouraging progress in this area using deep learning-based methods, especially CNNs, which can extract important features from MRI scans. By combining the VGG16 and EDCNN networks, this study presents a new method for determining brain age. For feature extraction, we use VGG16, and for brain age prediction, we use EDCNN. The proposed method was evaluated using the Kaggle repository, a freely available benchmark dataset that includes magnetic resonance imaging (MRI) images of 562 individuals ranging in age from 20 to 86. Following the preprocessing of the dataset, the images were adjusted to 224 × 224 x 3 dimensions and normalized. Using a broad range of evaluation criteria, the proposed methodology's performance was contrasted with that of several state-of-the-art techniques. Additionally, ablation experiments were conducted as part of this study to determine the significance of various components of the suggested technique. The results showed that the method's performance was significantly improved when VGG16 was used as a feature extractor. In addition, this study found that the proposed method's performance was significantly improved after VGG16 network tuning. The research demonstrates that technologies based on deep learning might help us comprehend how the brain develops and ages. The approach uses EDCNN to forecast brain ageing and VGG16 to extract features. In order to decipher the structural subtleties seen in brain MRI images, the acclaimed feature extraction tool VGG16 is used as a base.

Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors.

Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy. Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data. We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

Hybrid image encryption using advanced least significant bit algorithm, chaotic maps and DNA encoding for digital healthcare

Security and privacy are major concerns in this modern world. Medical documentation of patient data needs to be transmitted between hospitals for medical experts opinions on critical cases which may cause threats to the data. Nowadays most of the hospitals use electronic methods to store and transmit data with basic security measures, but these methods are still vulnerable. There is no perfect solution that solves the security problems in any industry, especially healthcare. So, to cope with the arising need to increase the security of the data from being manipulated the proposed method uses a hybrid image encryption technique to hide the data in an image so it becomes difficult to sense the presence of data in the image while transmission. It combines Least Significant Bit (LSB) Algorithm using Arithmetic Division Operation along with Canny edge detection to embed the patient data in medical images. The image is subsequently encrypted using keys of six different chaotic maps sequentially to increase the integrity and robustness of the system. Finally, an encrypted image is converted into DNA sequence using DNA encoding rule to improve reliability. The experimentation is done on the Chest XRay image, Knee Magnetic Resonance Imaging (MRI) image, Neck MRI image, Lungs Computed Tomography (CT) Scan image datasets and patient medical data with 500 characters, 1000 characters and 1500 characters. And, it is evaluated based on time coefficient of encryption and decryption, histogram, entropy, similarity score (Mean Square Error), quality score (peak signal-to-noise ratio), motion activity index (number of changing pixel rate), unified average changing intensity, image similarity score (structure similarity index measurement) between original and encrypted images. Also, the proposed technique is compared with other recent state of arts methods for 500 characters embedding and performed better than those techniques. The proposed method is more stable and embeds comparatively more data than other recent works with lower Mean Square Error value of 4748.12 which is the main factor used to determine how well the data is hidden and cannot be interpreted easily. Also, it achieved a Peak Signal-Noise Ratio (PSNR) value of 71.34 dB, which is superior than other recent works, verifying that the image quality remains uncompromising even after being encrypted.

Pre-operative enhanced magnetic resonance imaging combined with clinical features predict early recurrence of hepatocellular carcinoma after radical resection.

Indentifying predictive factors for postoperative recurrence of hepatocellular carcinoma (HCC) has great significance for patient prognosis. To explore the value of gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) combined with clinical features in predicting early recurrence of HCC after resection. A total of 161 patients with pathologically confirmed HCC were enrolled. The patients were divided into early recurrence and non-early recurrence group based on the follow-up results. The clinical, laboratory, pathological results and Gd-EOB-DTPA enhanced MRI imaging features were analyzed. Of 161 patients, 73 had early recurrence and 88 were had non-early recurrence. Univariate analysis showed that patient age, gender, serum alpha-fetoprotein level, the Barcelona Clinic Liver Cancer stage, China liver cancer (CNLC) stage, microvascular invasion (MVI), pathological satellite focus, tumor size, tumor number, tumor boundary, tumor capsule, intratumoral necrosis, portal vein tumor thrombus, large vessel invasion, nonperipheral washout, peritumoral enhancement, hepatobiliary phase (HBP)/tumor signal intensity (SI)/peritumoral SI, HBP peritumoral low signal and peritumoral delay enhancement were significantly associated with early recurrence of HCC after operation. Multivariate logistic regression analysis showed that patient age, MVI, CNLC stage, tumor boundary and large vessel invasion were independent predictive factors. External data validation indicated that the area under the curve of the combined predictors was 0.861, suggesting that multivariate logistic regression was a reasonable predictive model for early recurrence of HCC. Gd-EOB-DTPA enhanced MRI combined with clinical features would help predicting the early recurrence of HCC after operation.

Development and validation of an MRI-Based nomogram to predict the effectiveness of immunotherapy for brain metastasis in patients with non-small cell lung cancer.

The variability and unpredictability of immune checkpoint inhibitors (ICIs) in treating brain metastases (BMs) in patients with advanced non-small cell lung cancer (NSCLC) is the main concern. We assessed the utility of novel imaging biomarkers (radiomics) for discerning patients with NSCLC and BMs who would derive advantages from ICIs treatment. Data clinical outcomes and pretreatment magnetic resonance images (MRI) were collected on patients with NSCLC with BMs treated with ICIs between June 2019 and June 2022 and divided into training and test sets. Metastatic brain lesions were contoured using ITK-SNAP software, and 3748 radiomic features capturing both intra- and peritumoral texture patterns were extracted. A clinical radiomic nomogram (CRN) was built to evaluate intracranial progression-free survival, progression-free survival, and overall survival. The prognostic value of the CRN was assessed by Kaplan-Meier survival analysis and log-rank tests. In the study, a total of 174 patients were included, and 122 and 52 were allocated to the training and validation sets correspondingly. The intratumoral radiomic signature, peritumoral radiomic signature, clinical signature, and CRN predicted intracranial objective response rate. Kaplan-Meier analyses showed a significantly longer intracranial progression-free survival in the low-CRN group than in the high-CRN group (p < 0.001). The CRN was also significantly associated with progression-free survival (p < 0.001) but not overall survival. Radiomics biomarkers from pretreatment MRI images were predictive of intracranial response. Pretreatment radiomics may allow the early prediction of benefits.

Positive Effect of Super-Resolved Structural Magnetic Resonance Imaging for Mild Cognitive Impairment Detection.

This paper presents a novel approach to improving the detection of mild cognitive impairment (MCI) through the use of super-resolved structural magnetic resonance imaging (MRI) and optimized deep learning models. The study introduces enhancements to the perceptual quality of super-resolved 2D structural MRI images using advanced loss functions, modifications to the upscaler part of the generator, and experiments with various discriminators within a generative adversarial training setting. It empirically demonstrates the effectiveness of super-resolution in the MCI detection task, showcasing performance improvements across different state-of-the-art classification models. The paper also addresses the challenge of accurately capturing perceptual image quality, particularly when images contain checkerboard artifacts, and proposes a methodology that incorporates hyperparameter optimization through a Pareto optimal Markov blanket (POMB). This approach systematically explores the hyperparameter space, focusing on reducing overfitting and enhancing model generalizability. The research findings contribute to the field by demonstrating that super-resolution can significantly improve the quality of MRI images for MCI detection, highlighting the importance of choosing an adequate discriminator and the potential of super-resolution as a preprocessing step to boost classification model performance.

Brain Damage Detection Using Ensemble Classifier for Children Health Progress

Magnetic Resonance Imaging (MRI) is a medical imaging technique used to detect brain damage, particularly in children. Previous classification techniques for brain damage had low accuracy, complex edge detection, and high noise levels. In this proposed work, use an ensemble classification technique to analyze MRI brain images of 6-10 year old children. The input data is preprocessed using a Median filter to remove noise and improve image quality before being sent to a segmentation technique. Each part of the segmented image, such as tissue, inner layer, overall shape, and texture, is classified based on pixel size. The classification is done using an ensemble of Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) techniques. The ensemble prediction is multiplied with several input values to classify the MRI image, aggregating different layer values. The output classified image is validated and developed using Mat Lab software simulation, with an accuracy of 97.86%. This method can accurately classify various types of brain damage and aid in early detection in children.

Optimised ResNet50 for Multi-Class Classification of Brain Tumors

Categorizing brain cancers, including glioma, meningioma, and pituitary tumors, based on magnetic resonance imaging (MRI) images presents a significant challenge. Deep learning and machine learning techniques have shown promise in enhancing image categorization. To address this challenge, we leverage the power of the optimized ResNet50 model. Our approach involves classifying medical images using Convolutional Neural Network (CNN) features, which are then compared with the ResNet50 model. The primary goal is to detect brain tumors at an early stage using an advanced deep-learning model. We utilize an accessible dataset from Figshare, containing MRI images of the three distinct categories of brain tumors. Existing brain tumor models face limitations in handling multi-class problems and early-stage diagnosis. Therefore, we propose a fully automated approach employing Convolutional Neural Networks (CNN) to extract diverse properties from brain MRI scans. This method aims to provide accurate tumor diagnosis, even with a high number of classes and limited information in MRI data. Our proposed model involves the creation of identification blocks within a four-layered primary architecture, followed by testing and assessment of the interconnected layers. The results demonstrate that our model outperforms existing methods, achieving an impressive overall classification accuracy of 99.03%,

A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm.

Brain tumors are a common disease that affects millions of people worldwide. Considering the severity of brain tumors (BT), it is important to diagnose the disease in its early stages. With advancements in the diagnostic process, Magnetic Resonance Imaging (MRI) has been extensively used in disease detection. However, the accurate identification of BT is a complex task, and conventional techniques are not sufficiently robust to localize and extract tumors in MRI images. Therefore, in this study, we used a deep learning model combined with a segmentation algorithm to localize and extract tumors from MR images. This paper presents a Deep Learning (DL)-based You Look Only Once (YOLOv7) model in combination with the Grab Cut algorithm to extract the foreground of the tumor image to enhance the detection process. YOLOv7 is used to localize the tumor region, and the Grab Cut algorithm is used to extract the tumor from the localized region. The performance of the YOLOv7 model with and without the Grab Cut algorithm is evaluated. The results show that the proposed approach outperforms other techniques, such as hybrid CNN-SVM, YOLOv5, and YOLOv6, in terms of accuracy, precision, recall, specificity, and F1 score. Our results show that the proposed technique achieves a high dice score between tumor-extracted images and ground truth images. The findings show that the performance of the YOLOv7 model is improved by the inclusion of the Grab Cut algorithm compared to the performance of the model without the algorithm.

Current State of Evidence for Neuroimaging Paradigms in Management of Acute Ischemic Stroke.

Stroke is the chief differential diagnosis in patient presenting to the emergency room with abrupt onset focal neurological deficits. Neuroimaging, including non-contrast computed tomography (CT), magnetic resonance imaging (MRI), vascular and perfusion imaging, is a cornerstone in the diagnosis and treatment decision-making. This review examines the current state of evidence behind the different imaging paradigms for acute ischemic stroke diagnosis and treatment, including current recommendations from the guidelines. Non-contrast CT brain, or in some centers MRI, can help differentiate ischemic stroke and intracerebral hemorrhage (ICH), a pivotal juncture in stroke diagnosis and treatment algorithm, especially for early window thrombolytics. Advanced imaging such as MRI or perfusion imaging can also assist making a diagnosis of ischemic stroke versus mimics such as migraine, Todd's paresis, or functional disorders. Identification of medium-large vessel occlusions with CT or MR angiography triggers consideration of endovascular thrombectomy (EVT), with additional perfusion imaging help identify salvageable brain tissue in patients who are likely to benefit from reperfusion therapies, particularly in the ≥6 h window. We also review recent advances in neuroimaging and ongoing trials in key therapeutic areas and their imaging selection criteria to inform the readers on potential future transitions into use of neuroimaging for stroke diagnosis and treatment decision making. ANN NEUROL 2024;95:1017-1034.

A Survey on AI-based Parkinson Disease Detection: Taxonomy, Case Study, and Research Challenges

Parkinson Disease (PD) is most common diseases from majority of disease encountered all over the world, with more than 7 million individuals being affected. PD is a type of progressive nervous system disease, causing deterioration in health or function. The timely identification of PD is a significant challenge because it rarely shows symptoms in the early stages. Moreover, it is typically encountered in older people where the symptoms sometimes coincide with age-related issues. Deep Learning (DL) can be integrated into many methodologies in diagnosing PD, such as Magnetic Resonance Imaging (MRI) and Single-Photon Emission Computed Tomography (SPECT). DL algorithms can detect PD based on observing some common symptoms. Moreover, it can also be detected using brain MRI images. So, in this study, we reviewed existing DL algorithms for timely identification of PD. We also have developed a CNN model for the timely identification of PD. We used 3D brain MRI images of PPMI datasets and achieved the 88% accuracy.

Improving Osteoporosis Prediction Using Vertebral Bone Quality Score and Paravertebral Muscle Measurements From Lumbar MRI Scans.

Retrospective cohort. This study aims to use a novel method of combining vertebral bone quality score with paravertebral cross-sectional area measurements to improve the accuracy of predicting individuals with total hip T-scores <-2.5. Osteoporosis is a prevalent skeletal condition associated with decreased bone density and increased fracture risk. Dual-energy x-ray absorptiometry (DXA) is the conventional method for diagnosing osteoporosis, but it has limitations. Opportunistic osteoporosis screening techniques using lumbar magnetic resonance imaging (MRI), particularly the vertebral bone quality (VBQ) score, have shown promise. This study aims to improve the accuracy of predicting individuals with low total hip T-scores using a novel method that combines VBQ scores with paravertebral cross-sectional area (CSA) measurements. A retrospective cohort of 98 patients with DXA and lumbar MRI scans was analyzed. VBQ scores were calculated based on lumbar MRI images, and CSA measurements of paravertebral and psoas muscles were obtained. Threshold-based logistic regression was used to identify optimal thresholds for predicting total hip T-scores <-2.5. The combined model incorporating the VBQ score and paravertebral muscle percent achieved an accuracy of 96.9% for predicting total hip T-scores <-2.5, compared to 81.6% when using the VBQ score alone. Incorporating paravertebral muscle measurements significantly improved the accuracy of identifying osteoporotic individuals. The combination of VBQ score and paravertebral muscle measurements enhances the accuracy of predicting individuals with low total hip T-scores. Lumbar MRI scans provide valuable information beyond opportunistic osteoporosis screening, and the inclusion of paravertebral muscle measurements could aid in identifying at-risk individuals more accurately.

Wearable Coaxially-Shielded Metamaterial for Magnetic Resonance Imaging.

Recent advancements in metamaterials have yielded the possibility of a wireless solution to improve signal-to-noise ratio (SNR) in magnetic resonance imaging (MRI). Unlike traditional closely packed local coil arrays with rigid designs and numerous components, these lightweight, cost-effective metamaterials eliminate the need for radio frequency cabling, baluns, adapters, and interfaces. However, their clinical adoption is limited by their low sensitivity, bulky physical footprint, and limited, specific use cases. Herein, a wearable metamaterial developed using commercially available coaxial cable, designed for a 3.0T MRI system is introduced. This metamaterial inherits the coaxially-shielded structure of its constituent cable, confining the electric field within and mitigating coupling to its surroundings. This ensures safer clinical adoption, lower signal loss, and resistance to frequency shifts. Weighing only 50g, the metamaterial maximizes its sensitivity by conforming to the anatomical region of interest. MRI images acquired using this metamaterial with various pulse sequences achieve an SNR comparable or even surpass that of a state-of-the-art 16-channel knee coil. This work introduces a novel paradigm for constructing metamaterials in the MRI environment, paving the way for the development of next-generation wireless MRI technology.

A noise robust image reconstruction using slice aware cycle interpolator network for parallel imaging in MRI.

Reducing Magnetic resonance imaging (MRI) scan time has been an important issue for clinical applications. In order to reduce MRI scan time, imaging acceleration was made possible by undersampling k-space data. This is achieved by leveraging additional spatial information from multiple, independent receiver coils, thereby reducing the number of sampled k-spacelines. The aim of this study is to develop a deep-learning method for parallel imaging with a reduced number of auto-calibration signals (ACS) lines in noisyenvironments. A cycle interpolator network is developed for robust reconstruction of parallel MRI with a small number of ACS lines in noisy environments. The network estimates missing (unsampled) lines of each coil data, and these estimated missing lines are then utilized to re-estimate the sampled k-space lines. In addition, a slice aware reconstruction technique is developed for noise-robust reconstruction while reducing the number of ACS lines. We conducted an evaluation study using retrospectively subsampled data obtained from three healthy volunteers at 3T MRI, involving three different slice thicknesses (1.5, 3.0, and 4.5 mm) and three different image contrasts (T1w, T2w, and FLAIR). Despite the challenges posed by substantial noise in cases with a limited number of ACS lines and thinner slices, the slice aware cycle interpolator network reconstructs the enhanced parallel images. It outperforms RAKI, effectively eliminating aliasing artifacts. Moreover, the proposed network outperforms GRAPPA and demonstrates the ability to successfully reconstruct brain images even under severe noisyconditions. The slice aware cycle interpolator network has the potential to improve reconstruction accuracy for a reduced number of ACS lines in noisyenvironments.

A coupled-GAN architecture to fuse MRI and PET image features for multi-stage classification of Alzheimer’s disease

Alzheimer’s disease (AD) is a degenerative neurological ailment that begins with memory loss and ultimately leads to a total loss of mental capacity. Researchers are interested in using magnetic resonance imaging (MRI) and positron emission tomography (PET) to find people with mild cognitive impairment (MCI), which is a stage before Alzheimer’s disease (AD). Significant hippocampal loss and temporal lobe atrophy characterize the transition from MCI to AD, which can be visualized using T1-W structural MRI. PET visualizes brain glucose metabolism, which indicates neuronal activity, making it a viable neuroimaging method for AD diagnosis. The extraction and fusion of structural and metabolite information about brain alterations contained in multimodal data is crucial for achieving an appropriate classification result. Therefore, in this work a new end-to-end coupled-GAN (CGAN) architecture is introduced. The proposed CGANC network consists of two sub-models: a CGAN for extraction of fused features from multimodal data, and a CNN classifier to classify these features. The proposed CGAN model is trained to encode MRI and PET images into a shared latent space. The fused features are extracted from this shared latent space and then are classified according to particular stage of AD. In order to test the effectiveness of the suggested approach, experiments are done on the publicly available ADNI dataset and compared with state-of-the-art methods. The proposed method’s source code will be made freely available at https://github.com/ChandrajitChoudhury/CGAN-AD.

Eighty-One Percent of Unrepaired Interportal Capsulotomies Showed Healed Capsules on Magnetic Resonance Imaging 5 Years After Primary Hip Arthroscopy

PurposeTo evaluate whether un-repaired interportal capsulotomy presents with capsular defect at MR imaging 5 years after primary hip arthroscopy, and to determine its effect on functional results and findings of osteoarthritis on a radiograph or MRI imaging. MethodsPatients with femoro-acetabular impingement (without arthritis, dysplasia) were retrospectively reviewed after arthroscopic labral repair or debridement and femoroplasty through interportal capsulotomy without closure. Patients were assessed preoperatively and a minimum of five years postoperatively using patient-reported outcomes (Hip Outcome Score Activities of Daily Living [HOS-ADL], modified Harris Hip Score [mHHS]), visual analogue scale scores, radiographic measures, and magnetic resonance imaging (MRI) ResultsForty patients (42hips) eligible for the study were evaluated and 81% of the hips had healed capsules, while there were 8 (19%) hips had capsule defects at the latest MR imaging. There were three hips with subchondral oedema in the defect group compared to one in the capsule healed group (p: 0.01) at the latest MRI, which were not present at preoperative MR imaging (still positive at the multivariate analysis when the preoperative alpha angle was also taken into consideration . Functional results did not differ between groups (p>0.05). ConclusionIn this study, 81% of interportal capsulotomies healed without repair at 5 years after primary hip arthroscopy.

Enhanced ADHD detection: Frequency information embedded in a visual-language framework

This paper presents the Frequency-Integrated Visual-Language Network (FIVLNet), a deep learning (DL) framework tailored to improve the diagnostic accuracy for Attention Deficit Hyperactivity Disorder (ADHD) using magnetic resonance imaging (MRI) scans. Traditional DL approaches in ADHD diagnosis often overlook the sequential dependencies of MRI images or fail to adequately capture their complex structural details, resulting in suboptimal classification accuracy. To address this, the proposed FIVLNet synergistically integrates both high and low-frequency data from MRI images, based on the Convolutional Neural Network (CNN) and the cross-attention mechanism, subsequently achieving more comprehensive representations of the MRI images. Furthermore, in order to enrich the model’s learning capacity, textual embeddings from Contrastive Language-Image Pre-training (CLIP) are introduced to provide additional modalities of information. FIVLNet also preserves a lightweight architecture, which necessitates a smaller number of learnable parameters compared to existing models.

Abstract LB419: Neuroinflammation and aromatase inhibitor associated musculoskeletal syndrome (AIMSS) in breast cancer: Initial insights from [11C]PBR28 PET/MR imaging

Abstract Introduction: While 5-10 years of aromatase inhibitor (AI) treatment clearly improves survival rates of postmenopausal women with hormone-sensitive breast cancer, up to 85% of AI users report aromatase inhibitor associated musculoskeletal syndrome (AIMSS, including arthralgias, myalgias, and muscle stiffness). The biological mechanisms underlying AIMSS are not well understood, but accumulating preclinical and clinical evidence implicate an important role for neuroinflammation in the development and maintenance of other types of chronic musculoskeletal pain (e.g., low back pain, fibromyalgia etc.). The purpose of this pilot study was to investigate the role of neuroinflammation in breast cancer patients with AIMSS. Methods: This cross-sectional case control study used Siemens 3 T integrated positron emission tomography-magnetic resonance imaging (PET/MRI) with the radioligand [11C]PBR28, which binds to the neuroinflammation marker 18kDa translocator protein (TSPO), to assess neuroinflammation in two cohorts (chronic AIMSS, no-AIMSS) of breast cancer patients undergoing AI therapy, and age-, sex- and TSPO genotype-matched healthy controls. Static [11C]PBR28 PET images were reconstructed from 60-90 minutes post-injection. Standardized uptake value (SUV) maps were obtained voxel-wise by normalizing radioactivity by injected dose/body weight. SUV maps were normalized to whole brain to obtain SUV ratio (SUVR) maps. SUVR images spatially normalized to MNI template and then spatially smoothed (FWHM=8mm). Results: Two breast cancer patients (1 AIMSS, 1 no-AIMSS) and 2 matched healthy controls were included in this analysis. Compared to their individual matched healthy controls, both breast cancer patients showed increased [11C]PBR28 signal uptake in the thalamus/midbrain and pons. This elevated signal was more prominent in the breast cancer patient with AIMSS. Conclusions: These preliminary findings suggest an elevated signal of neuroinflammation in the thalamus and brainstem in AI-treatment and AIMSS, aligning with existing literature emphasizing their pivotal role in chronic pain pathology. While it is imperative to acknowledge the limitation of a small sample size, these promising findings suggest a potential role of neuroinflammation for the first time in AIMSS. Future focused Region of Interest (ROI) analysis in thalamus and brainstem with a larger sample size may confirm. Citation Format: Yehui Zhu, Natalie Swanson, Jennifer Murphy, Minhae Kim, Ann Partridge, Robert Edwards, Kristin Schreiber, Marco Loggia. Neuroinflammation and aromatase inhibitor associated musculoskeletal syndrome (AIMSS) in breast cancer: Initial insights from [11C]PBR28 PET/MR imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB419.