Deep learning-based myocardial scar segmentation from late gadolinium enhancement (LGE) cardiac MRI has shown great potential for accurate and timely diagnosis and treatment planning for structural cardiac diseases. However, the limited availability and variability of LGE images with high-quality scar labels restrict the development of robust segmentation models. To address this, we introduce CLAIM: \textbf{C}linically-Guided \textbf{L}GE \textbf{A}ugmentation for Real\textbf{i}stic and Diverse \textbf{M}yocardial Scar Synthesis and Segmentation framework, a framework for anatomically grounded scar generation and segmentation. At its core is the SMILE module (Scar Mask generation guided by cLinical knowledgE), which conditions a diffusion-based generator on the clinically adopted AHA 17-segment model to synthesize images with anatomically consistent and spatially diverse scar patterns. In addition, CLAIM employs a joint training strategy in which the scar segmentation network is optimized alongside the generator, aiming to enhance both the realism of synthesized scars and the accuracy of the scar segmentation performance. Experimental results show that CLAIM produces anatomically coherent scar patterns and achieves higher Dice similarity with real scar distributions compared to baseline models. Our approach enables controllable and realistic myocardial scar synthesis and has demonstrated utility for downstream medical imaging task.

Diffusion magnetic resonance imaging (dMRI) enables non-invasive investigation of tissue microstructure. The Standard Model (SM) of white matter aims to disentangle dMRI signal contributions from intra- and extra-axonal water compartments. However, due to the model its high-dimensional nature, extensive acquisition protocols with multiple b-values and diffusion tensor shapes are typically required to mitigate parameter degeneracies. Even then, accurate estimation remains challenging due to noise. This work introduces a novel estimation framework based on implicit neural representations (INRs), which incorporate spatial regularization through the sinusoidal encoding of the input coordinates. The INR method is evaluated on both synthetic and in vivo datasets and compared to parameter estimates using cubic polynomials, supervised neural networks, and nonlinear least squares. Results demonstrate superior accuracy of the INR method in estimating SM parameters, particularly in low signal-to-noise conditions. Additionally, spatial upsampling of the INR can represent the underlying dataset anatomically plausibly in a continuous way, which is unattainable with linear or cubic interpolation. The INR is fully unsupervised, eliminating the need for labeled training data. It achieves fast inference ($\sim$6 minutes), is robust to both Gaussian and Rician noise, supports joint estimation of SM kernel parameters and the fiber orientation distribution function with spherical harmonics orders up to at least 8 and non-negativity constraints, and accommodates spatially varying acquisition protocols caused by magnetic gradient non-uniformities. The combination of these properties along with the possibility to easily adapt the framework to other dMRI models, positions INRs as a potentially important tool for analyzing and interpreting diffusion MRI data.

Accurate diagnosis of brain disorders such as Alzheimer's disease and brain tumors remains a critical challenge in medical imaging. Conventional methods based on manual MRI analysis are often inefficient and error-prone. To address this, we propose DGG-XNet, a hybrid deep learning model integrating VGG16 and DenseNet121 to enhance feature extraction and classification. DenseNet121 promotes feature reuse and efficient gradient flow through dense connectivity, while VGG16 contributes strong hierarchical spatial representations. Their fusion enables robust multiclass classification of neurological conditions. Grad-CAM is applied to visualize salient regions, enhancing model transparency. Trained on a combined dataset from BraTS 2021 and Kaggle, DGG-XNet achieved a test accuracy of 91.33\%, with precision, recall, and F1-score all exceeding 91\%. These results highlight DGG-XNet's potential as an effective and interpretable tool for computer-aided diagnosis (CAD) of neurodegenerative and oncological brain disorders.

Multimodal brain magnetic resonance imaging (MRI) offers complementary insights into brain structure and function, thereby improving the diagnostic accuracy of neurological disorders and advancing brain-related research. However, the widespread applicability of MRI is substantially limited by restricted scanner accessibility and prolonged acquisition times. Here, we present TUMSyn, a text-guided universal MRI synthesis model capable of generating brain MRI specified by textual imaging metadata from routinely acquired scans. We ensure the reliability of TUMSyn by constructing a brain MRI database comprising 31,407 3D images across 7 MRI modalities from 13 worldwide centers and pre-training an MRI-specific text encoder to process text prompts effectively. Experiments on diverse datasets and physician assessments indicate that TUMSyn-generated images can be utilized along with acquired MRI scan(s) to facilitate large-scale MRI-based screening and diagnosis of multiple brain diseases, substantially reducing the time and cost of MRI in the healthcare system.

This study focuses on artificial intelligence-driven classification of glioma and Ki-67 leveling using T2w-FLAIR MRI, exploring the association of Ki-67 biomarkers with deep learning (DL) features through explainable artificial intelligence (XAI) and SHapley Additive exPlanations (SHAP). This IRB-approved study included 101 patients with glioma brain tumor acquired MR images with the T2W-FLAIR sequence. We extracted DL bottleneck features using ResNet50 from glioma MR images. Principal component analysis (PCA) was deployed for dimensionality reduction. XAI was used to identify potential features. The XGBosst classified the histologic grades of the glioma and the level of Ki-67. We integrated potential DL features with patient demographics (age and sex) and Ki-67 biomarkers, utilizing SHAP to determine the model's essential features and interactions. Glioma grade classification and Ki-67 level predictions achieved overall accuracies of 0.94 and 0.91, respectively. It achieved precision scores of 0.92, 0.94, and 0.96 for glioma grades 2, 3, and 4, and 0.88, 0.94, and 0.97 for Ki-67 levels (low: 5%≤Ki-67<10%, moderate: 10%≤Ki-67≤20, and high: Ki-67>20%). Corresponding F1-scores were 0.95, 0.88, and 0.96 for glioma grades and 0.92, 0.93, and 0.87 for Ki-67 levels. SHAP analysis further highlighted a strong association between bottleneck DL features and Ki-67 biomarkers, demonstrating their potential to differentiate glioma grades and Ki-67 levels while offering valuable insights into glioma aggressiveness. This study demonstrates the precise classification of glioma grades and the prediction of Ki-67 levels to underscore the potential of AI-driven MRI analysis to enhance clinical decision-making in glioma management.

The non-invasive assessment of central lymph node metastasis (CLNM) in patients with papillary thyroid carcinoma (PTC) plays a crucial role in assisting treatment decision and prognosis planning. This study aims to use an interpretable deep fuzzy network guided by expert knowledge to predict the CLNM status of patients with PTC from ultrasound images. A total of 1019 PTC patients were enrolled in this study, comprising 465 CLNM patients and 554 non-CLNM patients. Pathological diagnosis served as the gold standard to determine metastasis status. Clinical and morphological features of thyroid were collected as expert knowledge to guide the deep fuzzy network in predicting CLNM status. The network consisted of a region of interest (ROI) segmentation module, a knowledge-aware feature extraction module, and a fuzzy prediction module. The network was trained on 652 patients, validated on 163 patients and tested on 204 patients. The model exhibited promising performance in predicting CLNM status, achieving the area under the receiver operating characteristic curve (AUC), accuracy, precision, sensitivity and specificity of 0.786 (95% CI 0.720-0.846), 0.745 (95% CI 0.681-0.799), 0.727 (95% CI 0.636-0.819), 0.696 (95% CI 0.594-0.789), and 0.786 (95% CI 0.712-0.864), respectively. In addition, the rules of the fuzzy system in the model are easy to understand and explain, and have good interpretability. The deep fuzzy network guided by expert knowledge predicted CLNM status of PTC patients with high accuracy and good interpretability, and may be considered as an effective tool to guide preoperative clinical decision-making.

With the advancement of computer technology and imaging equipment, ultrasound has emerged as a crucial tool in breast cancer diagnosis. To gain deeper insights into the research landscape of ultrasound in breast cancer diagnosis, this study employed bibliometric methods for a comprehensive analysis spanning from 2004 to 2024, analyzing 3523 articles from 2176 institutions in 82 countries/regions. Over this period, publications on ultrasound diagnosis of breast cancer showed a fluctuating growth trend from 2004 to 2024. Notably, China, Seoul National University and Kim EK emerged as leading contributors in ultrasound for breast cancer detection, with the most published and cited journals being Ultrasound Med Biol and Radiology. The research spots in this area included "breast lesion", "dense breast" and "breast-conserving surgery", while "machine learning", "ultrasonic imaging", "convolutional neural network", "case report", "pathological complete response", "deep learning", "artificial intelligence" and "classification" are anticipated to become future research frontiers. This groundbreaking bibliometric analysis and visualization of ultrasonic breast cancer diagnosis publications offer clinical medical professionals a reliable research focus and direction.

Synthesizing high quality CT images remains a signifi-cant challenge due to the limited availability of annotat-ed data and the complex nature of CT imaging. In this work, we present PRO, a novel framework that, to the best of our knowledge, is the first to perform CT image synthesis in the projection domain using latent diffusion models. Unlike previous approaches that operate in the image domain, PRO learns rich structural representa-tions from raw projection data and leverages anatomi-cal text prompts for controllable synthesis. This projec-tion domain strategy enables more faithful modeling of underlying imaging physics and anatomical structures. Moreover, PRO functions as a foundation model, capa-ble of generalizing across diverse downstream tasks by adjusting its generative behavior via prompt inputs. Experimental results demonstrated that incorporating our synthesized data significantly improves perfor-mance across multiple downstream tasks, including low-dose and sparse-view reconstruction, even with limited training data. These findings underscore the versatility and scalability of PRO in data generation for various CT applications. These results highlight the potential of projection domain synthesis as a powerful tool for data augmentation and robust CT imaging. Our source code is publicly available at: https://github.com/yqx7150/PRO.

Multimodal medical imaging integrates diverse data types, such as structural and functional neuroimaging, to provide complementary insights that enhance deep learning predictions and improve outcomes. This study focuses on a neuroimaging prediction framework based on both structural and functional neuroimaging data. We propose a next-generation prediction model, \textbf{MultiViT2}, which combines a pretrained representative learning base model with a vision transformer backbone for prediction output. Additionally, we developed a data augmentation module based on the latent diffusion model that enriches input data by generating augmented neuroimaging samples, thereby enhancing predictive performance through reduced overfitting and improved generalizability. We show that MultiViT2 significantly outperforms the first-generation model in schizophrenia classification accuracy and demonstrates strong scalability and portability.