Alzheimer's disease (AD) is a neurodegenerative disorder characterized by a progressive cognitive decline, necessitating early diagnosis for effective treatment. This study presents the Relational Bi-level Aggregation Graph Convolutional Network with Dynamic Graph Learning and Puzzle Optimization for Alzheimer's Classification (RBAGCN-DGL-PO-AC), using denoised T1-weighted Magnetic Resonance Images (MRIs) collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) repository. Addressing the impact of noise in medical imaging, the method employs advanced denoising techniques includes: the Modified Spline-Kernelled Chirplet Transform (MSKCT), Jump Gain Integral Recurrent Neural Network (JGIRNN), and Newton Time Extracting Wavelet Transform (NTEWT), to enhance the image quality. Key brain regions, crucial for classification such as hippocampal, lateral ventricle and posterior cingulate cortex are segmented using Attention Guided Generalized Intuitionistic Fuzzy C-Means Clustering (AG-GIFCMC). Feature extraction and classification using segmented outputs are performed with RBAGCN-DGL and puzzle optimization, categorize input images into Healthy Controls (HC), Early Mild Cognitive Impairment (EMCI), Late Mild Cognitive Impairment (LMCI), and Alzheimer's Disease (AD). To assess the effectiveness of the proposed method, we systematically examined the structural modifications to the RBAGCN-DGL-PO-AC model through extensive ablation studies. Experimental findings highlight that RBAGCN-DGL-PO-AC state-of-the art performance, with 99.25 % accuracy, outperforming existing methods including MSFFGCN_ADC, CNN_CAD_DBMRI, and FCNN_ADC, while reducing training time by 28.5 % and increasing inference speed by 32.7 %. Hence, the RBAGCN-DGL-PO-AC method enhances AD classification by integrating denoising, segmentation, and dynamic graph-based feature extraction, achieving superior accuracy and making it a valuable tool for clinical applications, ultimately improving patient outcomes and disease management.

Different types of sports training can induce distinct changes in brain activity and function; however, it remains unclear if there are commonalities across various sports disciplines. Moreover, the relationship between these brain activity alterations and the duration of sports training requires further investigation. This study employed resting-state functional magnetic resonance imaging (rs-fMRI) techniques to analyze spontaneous brain activity using the amplitude of low-frequency fluctuations (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF) in 86 highly trained athletes compared to 74 age- and gender-matched non-athletes. Our findings revealed significantly higher ALFF values in the Insula_R (Right Insula), OFCpost_R (Right Posterior orbital gyrus), and OFClat_R (Right Lateral orbital gyrus) in athletes compared to controls, whereas fALFF in the Postcentral_R (Right Postcentral) was notably higher in controls. Additionally, we identified a significant negative correlation between fALFF values in the Postcentral_R of athletes and their years of professional training. Utilizing machine learning algorithms, we achieved accurate classification of brain activity patterns distinguishing athletes from non-athletes with over 96.97% accuracy. These results suggest that the functional reorganization observed in athletes' brains may signify an adaptation to prolonged training, potentially reflecting enhanced processing efficiency. This study emphasizes the importance of examining the impact of long-term sports training on brain function, which could influence cognitive and sensory systems crucial for optimal athletic performance. Furthermore, machine learning methods could be used in the future to select athletes based on differences in brain activity.

End-stage renal disease is characterized by an irreversible decline in kidney function. Despite a risk of chronic dysfunction of the transplanted kidney, renal transplantation is considered the most effective solution among available treatment options. Clinical attributes of graft survival prediction, such as allocation variables or results of pathological examinations, have been widely studied. Nevertheless, medical imaging is clinically used only to assess current transplant status. This study investigated the use of unsupervised deep learning-based algorithms to identify rich radiomic features that may be linked to graft survival from early dynamic contrast-enhanced magnetic resonance imaging data of renal transplants. A retrospective cohort of 108 transplanted patients (mean age 50 +/- 15, 67 men) undergoing systematic magnetic resonance imaging follow-up examinations (2013 to 2015) was used to train deep convolutional neural network models based on an unsupervised contrastive learning approach. 5-year graft survival analysis was performed from the obtained artificial intelligence radiomics features using penalized Cox models and Kaplan-Meier estimates. Using a validation set of 48 patients (mean age 54 +/- 13, 30 men) having 1-month post-transplantation magnetic resonance imaging examinations, the proposed approach demonstrated promising 5-year graft survival capability with a 72.7% concordance index from the artificial intelligence radiomics features. Unsupervised clustering of these radiomics features enabled statistically significant stratification of patients (p=0.029). This proof-of-concept study exposed the promising capability of artificial intelligence algorithms to extract relevant radiomics features that enable renal transplant survival prediction. Further studies are needed to demonstrate the robustness of this technique, and to identify appropriate procedures for integration of such an approach into multimodal and clinical settings.

The explosive growth of available high-quality imaging data coupled with new progress in hardware capabilities has enabled a new era of unprecedented performance in brain segmentation tasks. Despite the explosion of new data released by consortiums and groups around the world, most published, closed, or openly available segmentation models have either a limited or an unknown role in pediatric brains. This study explores the utility of state-of-the-art automated ventricular segmentation tools applied to pediatric hydrocephalus. Two popular, fast, whole-brain segmentation tools were used (FastSurfer and QuickNAT) to automatically segment the lateral ventricles and evaluate their accuracy in children with hydrocephalus. Forty scans from 32 patients were included in this study. The patients underwent imaging at the University of Minnesota Medical Center or satellite clinics, were between 0 and 18 years old, had an ICD-10 diagnosis that included the word hydrocephalus, and had at least one T1-weighted pre- or postcontrast MPRAGE sequence. Patients with poor quality scans were excluded. Dice similarity coefficient (DSC) scores were used to compare segmentation outputs against manually segmented lateral ventricles. Overall, both models performed poorly with DSCs of 0.61 for each segmentation tool. No statistically significant difference was noted between model performance (p = 0.86). Using a multivariate linear regression to examine factors associated with higher DSC performance, male gender (p = 0.66), presence of ventricular catheter (p = 0.72), and MRI magnet strength (p = 0.23) were not statistically significant factors. However, younger age (p = 0.03) and larger ventricular volumes (p = 0.01) were significantly associated with lower DSC values. A large-scale visualization of 196 scans in both models showed characteristic patterns of segmentation failure in larger ventricles. Significant gaps exist in current cutting-edge segmentation models when applied to pediatric hydrocephalus. Researchers will need to address these types of gaps in performance through thoughtful consideration of their training data before reaching the ultimate goal of clinical deployment.

The task of generating medical reports automatically is of paramount importance in modern healthcare, offering a substantial reduction in the workload of radiologists and accelerating the processes of clinical diagnosis and treatment. Current challenges include handling limited sample sizes and interpreting intricate multi-modal and multi-view medical data. In order to improve the accuracy and efficiency for radiologists, we conducted this investigation. This study aims to present a novel methodology for medical report generation that leverages Multi-View Contrastive Learning (MVCL) applied to MRI data, combined with a Symptom Consultant (SC) for extracting medical insights, to improve the quality and efficiency of automated medical report generation. We introduce an advanced MVCL framework that maximizes the potential of multi-view MRI data to enhance visual feature extraction. Alongside, the SC component is employed to distill critical medical insights from symptom descriptions. These components are integrated within a transformer decoder architecture, which is then applied to the Deep Wrist dataset for model training and evaluation. Our experimental analysis on the Deep Wrist dataset reveals that our proposed integration of MVCL and SC significantly outperforms the baseline model in terms of accuracy and relevance of the generated medical reports. The results indicate that our approach is particularly effective in capturing and utilizing the complex information inherent in multi-modal and multi-view medical datasets. The combination of MVCL and SC constitutes a powerful approach to medical report generation, addressing the existing challenges in the field. The demonstrated superiority of our model over traditional methods holds promise for substantial improvements in clinical diagnosis and automated report generation, indicating a significant stride forward in medical technology.

Background and purposeMRI is critical for the detection of subtle cortical pathology in epilepsy surgery assessment. This can be aided by improved MRI quality and resolution using ultra-high field (7T). But poor access and long scan durations limit widespread use, particularly in a paediatric setting. AI-based learning approaches may provide similar information by enhancing data obtained with conventional MRI (3T). We used a convolutional neural network trained on matched 3T and 7T images to enhance quantitative R1-maps (longitudinal relaxation rate) obtained at 3T in paediatric epilepsy patients and to determine their potential clinical value for lesion identification. Materials and MethodsA 3D U-Net was trained using paired patches from 3T and 7T R1-maps from n=10 healthy volunteers. The trained network was applied to enhance paediatric focal epilepsy 3T R1 images from a different scanner/site (n=17 MRI lesion positive / n=14 MR-negative). Radiological review assessed image quality, as well as lesion identification and visualization of enhanced maps in comparison to the 3T R1-maps without clinical information. Lesion appearance was then compared to 3D-FLAIR. ResultsAI enhanced R1 maps were superior in terms of image quality in comparison to the original 3T R1 maps, while preserving and enhancing the visibility of lesions. After exclusion of 5/31 patients (due to movement artefact or incomplete data), lesions were detected in AI Enhanced R1 maps for 14/15 (93%) MR-positive and 4/11 (36%) MR-negative patients. ConclusionAI enhanced R1 maps improved the visibility of lesions in MR positive patients, as well as providing higher sensitivity in the MR-negative group compared to either the original 3T R1-maps or 3D-FLAIR. This provides promising initial evidence that 3T quantitative maps can outperform conventional 3T imaging via enhancement by an AI model trained on 7T MRI data, without the need for pathology-specific information.

High-quality 3D fetal brain MRI reconstruction from motion-corrupted 2D slices is crucial for clinical diagnosis. Reliable slice-to-volume registration (SVR)-based motion correction and super-resolution reconstruction (SRR) methods are essential. Deep learning (DL) has demonstrated potential in enhancing SVR and SRR when compared to conventional methods. However, it requires large-scale external training datasets, which are difficult to obtain for clinical fetal MRI. To address this issue, we propose an unsupervised iterative SVR-SRR framework for isotropic HR volume reconstruction. Specifically, SVR is formulated as a function mapping a 2D slice and a 3D target volume to a rigid transformation matrix, which aligns the slice to the underlying location in the target volume. The function is parameterized by a convolutional neural network, which is trained by minimizing the difference between the volume slicing at the predicted position and the input slice. In SRR, a decoding network embedded within a deep image prior framework is incorporated with a comprehensive image degradation model to produce the high-resolution (HR) volume. The deep image prior framework offers a local consistency prior to guide the reconstruction of HR volumes. By performing a forward degradation model, the HR volume is optimized by minimizing loss between predicted slices and the observed slices. Comprehensive experiments conducted on large-magnitude motion-corrupted simulation data and clinical data demonstrate the superior performance of the proposed framework over state-of-the-art fetal brain reconstruction frameworks.

Placenta Accreta Spectrum Disorders (PAS) pose significant risks during pregnancy, frequently leading to postpartum hemorrhage during cesarean deliveries and other severe clinical complications, with bleeding severity correlating to the degree of placental invasion. Consequently, accurate prenatal diagnosis of PAS and its subtypes-placenta accreta (PA), placenta increta (PI), and placenta percreta (PP)-is crucial. However, existing guidelines and methodologies predominantly focus on the presence of PAS, with limited research addressing subtype recognition. Additionally, previous multi-class diagnostic efforts have primarily relied on inefficient two-stage cascaded binary classification tasks. In this study, we propose a novel convolutional neural network (CNN) architecture designed for efficient one-stage multiclass diagnosis of PAS and its subtypes, based on 4,140 magnetic resonance imaging (MRI) slices. Our model features two branches: the main classification branch utilizes a residual block architecture comprising multiple residual blocks, while the second branch integrates anatomical features of the uteroplacental area and the adjacent uterine serous layer to enhance the model's attention during classification. Furthermore, we implement a multitask learning strategy to leverage both branches effectively. Experiments conducted on a real clinical dataset demonstrate that our model achieves state-of-the-art performance.

Magnetic resonance (MR) tagging is an imaging technique for noninvasively tracking tissue motion in vivo by creating a visible pattern of magnetization saturation (tags) that deforms with the tissue. Due to longitudinal relaxation and progression to steady-state, the tags and tissue brightnesses change over time, which makes tracking with optical flow methods error-prone. Although Fourier methods can alleviate these problems, they are also sensitive to brightness changes as well as spectral spreading due to motion. To address these problems, we introduce the brightness-invariant tracking estimation (BRITE) technique for tagged MRI. BRITE disentangles the anatomy from the tag pattern in the observed tagged image sequence and simultaneously estimates the Lagrangian motion. The inherent ill-posedness of this problem is addressed by leveraging the expressive power of denoising diffusion probabilistic models to represent the probabilistic distribution of the underlying anatomy and the flexibility of physics-informed neural networks to estimate biologically-plausible motion. A set of tagged MR images of a gel phantom was acquired with various tag periods and imaging flip angles to demonstrate the impact of brightness variations and to validate our method. The results show that BRITE achieves more accurate motion and strain estimates as compared to other state of the art methods, while also being resistant to tag fading.

The 28th Annual Scientific Sessions of the Society for Cardiovascular Magnetic Resonance (SCMR) took place from January 29 to February 1, 2025, in Washington, D.C. SCMR 2025 brought together a diverse group of 1714 cardiologists, radiologists, scientists, and technologists from more than 80 countries to discuss emerging trends and the latest developments in cardiovascular magnetic resonance (CMR). The conference centered on the theme "Leading the Way to Accessible, Sustainable, and Efficient CMR," highlighting innovations aimed at making CMR more clinically efficient, widely accessible, and environmentally sustainable. The program featured 728 abstracts and case presentations with an acceptance rate of 86% (728/849), including Early Career Award abstracts, oral abstracts, oral cases and rapid-fire sessions, covering a broad range of CMR topics. It also offered engaging invited lectures across eight main parallel tracks and included four plenary sessions, two gold medalists, and one keynote speaker, with a total of 826 faculty participating. Focused sessions on accessibility, efficiency, and sustainability provided a platform for discussing current challenges and exploring future directions, while the newly introduced CMR Innovations Track showcased innovative session formats and fostered greater collaboration between researchers, clinicians, and industry. For the first time, SCMR 2025 also offered the opportunity for attendees to obtain CMR Level 1 Training Verification, integrated into the program. Additionally, expert case reading sessions and hands-on interactive workshops allowed participants to engage with real-world clinical scenarios and deepen their understanding through practical experience. Key highlights included plenary sessions on a variety of important topics, such as expanding boundaries, health equity, women's cardiovascular disease and a patient-clinician testimonial that emphasized the profound value of patient-centered research and collaboration. The scientific sessions covered a wide range of topics, from clinical applications in cardiomyopathies, congenital heart disease, and vascular imaging to women's heart health and environmental sustainability. Technical topics included novel reconstruction, motion correction, quantitative CMR, contrast agents, novel field strengths, and artificial intelligence applications, among many others. This paper summarizes the key themes and discussions from SCMR 2025, highlighting the collaborative efforts that are driving the future of CMR and underscoring the Society's unwavering commitment to research, education, and clinical excellence.