The temporal interpolation task for 4D medical imaging, plays a crucial role in clinical practice of respiratory motion modeling. Following the simplified linear-motion hypothesis, existing approaches adopt optical flow-based models to interpolate intermediate frames. However, realistic respiratory motions should be nonlinear and quasi-periodic with specific frequencies. Intuited by this property, we resolve the temporal interpolation task from the frequency perspective, and propose a Fourier basis-guided Diffusion model, termed FB-Diff. Specifically, due to the regular motion discipline of respiration, physiological motion priors are introduced to describe general characteristics of temporal data distributions. Then a Fourier motion operator is elaborately devised to extract Fourier bases by incorporating physiological motion priors and case-specific spectral information in the feature space of Variational Autoencoder. Well-learned Fourier bases can better simulate respiratory motions with motion patterns of specific frequencies. Conditioned on starting and ending frames, the diffusion model further leverages well-learned Fourier bases via the basis interaction operator, which promotes the temporal interpolation task in a generative manner. Extensive results demonstrate that FB-Diff achieves state-of-the-art (SOTA) perceptual performance with better temporal consistency while maintaining promising reconstruction metrics. Codes are available.

Ovarian tumor, as a common gynecological disease, can rapidly deteriorate into serious health crises when undetected early, thus posing significant threats to the health of women. Deep neural networks have the potential to identify ovarian tumors, thereby reducing mortality rates, but limited public datasets hinder its progress. To address this gap, we introduce a vital ovarian tumor pathological recognition dataset called \textbf{ViTaL} that contains \textbf{V}isual, \textbf{T}abular and \textbf{L}inguistic modality data of 496 patients across six pathological categories. The ViTaL dataset comprises three subsets corresponding to different patient data modalities: visual data from 2216 two-dimensional ultrasound images, tabular data from medical examinations of 496 patients, and linguistic data from ultrasound reports of 496 patients. It is insufficient to merely distinguish between benign and malignant ovarian tumors in clinical practice. To enable multi-pathology classification of ovarian tumor, we propose a ViTaL-Net based on the Triplet Hierarchical Offset Attention Mechanism (THOAM) to minimize the loss incurred during feature fusion of multi-modal data. This mechanism could effectively enhance the relevance and complementarity between information from different modalities. ViTaL-Net serves as a benchmark for the task of multi-pathology, multi-modality classification of ovarian tumors. In our comprehensive experiments, the proposed method exhibited satisfactory performance, achieving accuracies exceeding 90\% on the two most common pathological types of ovarian tumor and an overall performance of 85\%. Our dataset and code are available at https://github.com/GGbond-study/vitalnet.

Visual question answering (VQA) in medical imaging aims to support clinical diagnosis by automatically interpreting complex imaging data in response to natural language queries. Existing studies typically rely on distinct visual and textual encoders to independently extract features from medical images and clinical questions, which are subsequently combined to generate answers. Specifically, in computed tomography (CT), such approaches are similar to the conventional practices in medical image analysis. However, these approaches pay less attention to the spatial continuity and inter-slice correlations in the volumetric CT data, leading to fragmented and imprecise responses. In this paper, we propose a novel large language model (LLM)-based framework enhanced by a graph representation of salient features. Different from conventional multimodal encoding strategies, our approach constructs a cross-modal graph integrating both visual and textual features, treating individual CT slices and question tokens as nodes within the graph. We further leverage an attentive graph convolutional network to dynamically fuse information within this structure. The resulting aggregated graph features then serve as a soft prompt to guide a large language model in generating accurate answers. Extensive experiments on the M3D-VQA benchmark demonstrate that our approach consistently outperforms baselines across multiple evaluation metrics, offering more robust reasoning capabilities.

COVID-19 is a severe and acute viral disease that can cause symptoms consistent with pneumonia in which inflammation is caused in the alveolous regions of the lungs leading to a build-up of fluid and breathing difficulties. Thus, the diagnosis of COVID using CT scans has been effective in assisting with RT-PCR diagnosis and severity classifications. In this paper, we proposed a new data quality control pipeline to refine the quality of CT images based on GAN and sliding windows. Also, we use class-sensitive cost functions including Label Distribution Aware Loss(LDAM Loss) and Class-balanced(CB) Loss to solve the long-tail problem existing in datasets. Our model reaches more than 0.983 MCC in the benchmark test dataset.

Background and AimsTransthoracic echocardiography (TTE) is a widely available tool for diagnosing and managing heart failure but has limited predictive value for survival. Cardiopulmonary exercise test (CPET) performance strongly correlates with survival in heart failure patients but is less accessible. We sought to develop an artificial intelligence (AI) algorithm using TTE and electronic medical records to predict CPET peak oxygen consumption (peak VO2) [≤] 14 mL/kg/min. MethodsAn AI model was trained to predict peak VO2 [≤] 14 mL/kg/min from TTE images, structured TTE reports, demographics, medications, labs, and vitals. The training set included patients with a TTE within 6 months of a CPET. Performance was retrospectively tested in a held-out group from the development cohort and an external validation cohort. Results1,127 CPET studies paired with concomitant TTE were identified. The best performance was achieved by using all components (TTE images, all structured clinical data). The model performed well at predicting a peak VO2 [≤] 14 mL/kg/min, with an AUROC of 0.84 (development cohort) and 0.80 (external validation cohort). It performed consistently well using higher ([≤] 18 mL/kg/min) and lower ([≤] 12 mL/kg/min) cut-offs. ConclusionsThis multimodal AI model effectively categorized patients into low and high risk predicted peak VO2, demonstrating the potential to identify previously unrecognized patients in need of advanced heart failure therapies where CPET is not available.

Multiple Sclerosis (MS) is a chronic neurological disorder affecting millions worldwide. Early detection is vital to prevent long-term disability. Magnetic Resonance Imaging (MRI) plays a crucial role in MS diagnosis, yet differentiating MS lesions from other brain anomalies remains a complex challenge. To develop and evaluate a novel deep learning framework-2DRK-MSCAN-for the early and accurate detection of MS lesions using MRI data. The proposed approach is validated using three publicly available MRI-based brain tumor datasets and comprises three main stages. First, Gradient Domain Guided Filtering (GDGF) is applied during pre-processing to enhance image quality. Next, an EfficientNetV2L backbone embedded within a U-shaped encoder-decoder architecture facilitates precise segmentation and rich feature extraction. Finally, classification of MS lesions is performed using the 2DRK-MSCAN model, which incorporates deep diffusion residual kernels and multiscale snake convolutional attention mechanisms to improve detection accuracy and robustness. The proposed framework achieved 99.9% accuracy in cross-validation experiments, demonstrating its capability to distinguish MS lesions from other anomalies with high precision. The 2DRK-MSCAN framework offers a reliable and effective solution for early MS detection using MRI. While clinical validation is ongoing, the method shows promising potential for aiding timely intervention and improving patient care.

Knee osteoarthritis (KOA) is a progressive degenerative joint disease and a leading cause of disability worldwide. Manual diagnosis of KOA from X-ray images is subjective and prone to inter- and intra-observer variability, making early detection challenging. While deep learning (DL)-based models offer automation, they often require large labeled datasets, lack interpretability, and do not provide quantitative feature measurements. Our study presents an automated KOA severity assessment system that integrates a pretrained DL model with image processing techniques to extract and quantify key KOA imaging biomarkers. The pipeline includes contrast limited adaptive histogram equalization (CLAHE) for contrast enhancement, DexiNed-based edge extraction, and thresholding for noise reduction. We design customized algorithms that automatically detect and quantify joint space narrowing (JSN) and osteophytes from the extracted edges. The proposed model quantitatively assesses JSN and finds the number of intercondylar osteophytes, contributing to severity classification. The system achieves accuracies of 88% for JSN detection, 80% for osteophyte identification, and 73% for KOA classification. Its key strength lies in eliminating the need for any expensive training process and, consequently, the dependency on labeled data except for validation. Additionally, it provides quantitative data that can support classification in other OA grading frameworks.

In China, there is a lack of standardised clinical imaging databases for multidimensional evaluation of cardiopulmonary diseases. To address this gap, this study protocol launched a project to build a clinical imaging technology integration and a multicentre database for early warning and stratification of cardiopulmonary dysfunction in the elderly. This study employs a cross-sectional design, enrolling over 6000 elderly participants from five regions across China to evaluate cardiopulmonary function and related diseases. Based on clinical criteria, participants are categorized into three groups: a healthy cardiopulmonary function group, a functional decrease group and an established cardiopulmonary diseases group. All subjects will undergo comprehensive assessments including chest CT scans, echocardiography, and laboratory examinations. Additionally, at least 50 subjects will undergo cardiopulmonary exercise testing (CPET). By leveraging artificial intelligence technology, multimodal data will be integrated to establish reference ranges for cardiopulmonary function in the elderly population, as well as to develop early-warning models and severity grading standard models. The study has been approved by the local ethics committee of Shanghai Changzheng Hospital (approval number: 2022SL069A). All the participants will sign the informed consent. The results will be disseminated through peer-reviewed publications and conferences.

This study presents a novel data augmentation approach to improve deep learning (DL)-based segmentation for 3D phase-contrast magnetic resonance angiography (PC-MRA) images affected by pulsation artifacts. Augmentation was achieved by simulating pulsation artifacts through the addition of periodic errors in k-space magnitude. The approach was evaluated on PC-MRA datasets from 16 volunteers, comparing DL segmentation with and without pulsation artifact augmentation to a level-set algorithm. Results demonstrate that DL methods significantly outperform the level-set approach and that pulsation artifact augmentation further improves segmentation accuracy, especially for images with lower velocity encoding. Quantitative analysis using Dice-Sørensen coefficient, Intersection over Union, and Average Symmetric Surface Distance metrics confirms the effectiveness of the proposed method. This technique shows promise for enhancing vascular segmentation in various anatomical regions affected by pulsation artifacts, potentially improving clinical applications of PC-MRA.

Automatic medical image segmentation techniques are vital for assisting clinicians in making accurate diagnoses and treatment plans. Although the U-shaped network (U-Net) has been widely adopted in medical image analysis, it still faces challenges in capturing long-range dependencies, particularly in complex and textured medical images where anatomical structures often blend into the surrounding background. To address these limitations, a novel network architecture, called recursive transformer-based U-Net (ReT-UNet), which integrates recursive feature learning and transformer technology, is proposed. One of the key innovations of ReT-UNet is the multi-scale global feature fusion (Multi-GF) module, inspired by transformer models and multi-scale pooling mechanisms. This module captures long-range dependencies, enhancing the abstraction and contextual understanding of multi-level features. Additionally, a recursive feature accumulation block is introduced to iteratively update features across layers, improving the network's ability to model spatial correlations and represent deep features in medical images. To improve sensitivity to local details, a lightweight atrous spatial pyramid pooling (ASPP) module is appended after the Multi-GF module. Furthermore, the segmentation head is redesigned to emphasize feature aggregation and fusion. During the encoding phase, a hybrid pooling layer is employed to ensure comprehensive feature sampling, thereby enabling a broader range of feature representation and improving detailed information learning. Results: The proposed method has been evaluated through ablation experiments, demonstrating generally consistent performance across multiple trials. When applied to cardiac, pulmonary nodule, and polyp segmentation datasets, the method showed a reduction in mis-segmented regions. The experimental results suggest that the approach can improve segmentation accuracy and stability compared to competing state-of-the-art methods. Experimental findings highlight the superiority of the proposed ReT-UNet over related methods and demonstrate its potential for applications in medical image segmentation.