Volume 45, Issue 5

Cardiovascular health is vital to human well-being, and cardiac magnetic resonance (CMR) imaging is considered the clinical reference standard for diagnosing cardiovascular disease. However, its adoption is hindered by long scan times, complex contrasts, and inconsistent quality. While deep learning methods perform well on specific CMR imaging sequences, they often fail to generalize across modalities and sampling schemes. The lack of benchmarks for high-quality, fast CMR image reconstruction further limits technology comparison and adoption. The CMRxRecon2024 challenge, attracting over 200 teams from 18 countries, addressed these issues with two tasks: generalization to unseen modalities and robustness to diverse undersampling patterns. We introduced the largest public multi-modality CMR raw dataset, an open benchmarking platform, and shared code. Analysis of the best-performing solutions revealed that prompt-based adaptation and enhanced physics-driven consistency enabled strong cross-scenario performance. These findings establish principles for generalizable reconstruction models and advance clinically translatable AI in cardiovascular imaging.

中文摘要翻译待生成

Medical image analysis is critical yet challenged by the need of jointly segmenting organs or tissues, and numerous instances for anatomical structures and tumor microenvironment analysis. Existing studies typically formulated different segmentation tasks in isolation, which overlooks the fundamental interdependencies between these tasks, leading to suboptimal segmentation performance and insuffic...

中文摘要翻译待生成

Multimodal ultrasound imaging, combining B-mode ultrasound, shear wave velocity, and shear wave time, is crucial for diagnosing and treating breast lesions, providing insights into lesion characteristics and tissue properties. However, challenges arise from inter-modal feature misalignment and attention shifts due to varied capture methods and an overemphasis on vibrant color data. To tackle these...

中文摘要翻译待生成

Three-dimensional ultrasound enables real-time volumetric visualization of anatomical structures. Unlike traditional 2D ultrasound, 3D imaging reduces reliance on precise probe orientation, potentially making ultrasound more accessible to clinicians with varying levels of experience and improving automated measurements and post-exam analysis. However, achieving both high volume rates and high imag...

中文摘要翻译待生成

The exploitation of label hierarchy is crucial for effective brain tumor segmentation. Nevertheless, existing methods grapple with two key limitations. First, they lack the hierarchical dependency of predictions across different label levels, rendering the network outputs less interpretable. Second, they fail to exploit the hierarchical similarity among labels, thus hindering potential accuracy en...

中文摘要翻译待生成

Vision Transformers (ViTs) demonstrate significant promise in medical image analysis but face two critical challenges: 1) their limited ability to capture local features in data-scarce scenarios, leading to data inefficiency, and 2) their high computational and storage demands of the full fine-tuning process in transfer learning, resulting in parameter inefficiency. To achieve efficient and accura...

中文摘要翻译待生成

Medical image segmentation is an important analysis task in clinical practice and research. Deep learning has massively advanced the field, but current approaches are mostly based on models trained for a specific task. Training such models or adapting them to a new condition is costly due to the need for labeled data. The emergence of vision foundation models, especially Segment Anything Model (SA...

中文摘要翻译待生成

Synthesizing missing modalities in multi-parametric MRI (mpMRI) is vital for accurate tumor diagnosis, yet remains challenging due to incomplete acquisitions and modality heterogeneity. Diffusion models have shown strong generative capability, but conventional approaches typically operate in the image domain with high memory costs and often rely solely on noise-space supervision, which limits anat...

中文摘要翻译待生成

General movement assessment (GMA) is a non-invasive method used to evaluate neuromotor behavior in infants under six months of age and is considered a reliable tool for the early detection of cerebral palsy (CP). However, traditional GMA relies on the subjective judgment of multiple internationally certified physicians, making it time-consuming and limiting its accessibility for widespread use. Fu...

中文摘要翻译待生成

Photon counting detector-based CT (PCCT) systems provide spectral count measurements, enabling material decomposition (MD) for quantitative imaging. Maximum-likelihood estimation (MLE) for MD offers asymptotically unbiased and efficient (minimum variance) results but is usually solved iteratively, making the entire process computationally expensive and time-consuming. Conversely, representative em...

中文摘要翻译待生成

With the growing global threat of coronary artery disease (CAD), automated CAD diagnosis techniques based on coronary CT angiography (CCTA) have been developed. However, their clinical applicability remains limited due to the heterogeneity of stenosis and plaque attributes, as well as confounders within the causal relationships of CAD diagnosis. This work introduces the Attribute-Decoupled Interve...

中文摘要翻译待生成

Reconstructing neurons from large electron microscopy (EM) datasets for connectomic analysis presents a significant challenge, particularly in segmenting neurons of complex morphologies. Previous deep learning-based neuron segmentation methods often rely on pixel-level image context and produce extensive oversegmented fragments. Detecting these split errors and merging the split neuron segments ar...

中文摘要翻译待生成

Incoherent k-space undersampling and deep learning-based reconstruction methods have shown great success in accelerating MRI. However, the performance of most previous methods will degrade dramatically under high acceleration factors, e.g., $8\times $ or higher. Recently, denoising diffusion models (DM) have demonstrated promising results in solving this issue; however, one major drawback of the...

中文摘要翻译待生成

This paper introduces 2D Gaussian Splatting (GS) to Electrical Impedance Tomography (EIT), marking its first application in this field. Initially developed for computer vision tasks such as scene reconstruction, GS enables continuous representation and efficient rendering of high-resolution images. Building on these capabilities, we propose a novel GS-based EIT reconstruction framework that models...

中文摘要翻译待生成

Computed tomography (CT) is extensively used for accurate visualization and segmentation of organs and lesions. While deep learning models such as convolutional neural networks (CNNs) and vision transformers (ViTs) have significantly improved CT image analysis, their performance often declines when applied to diverse, real-world clinical data. Although foundation models offer a broader and more ad...

中文摘要翻译待生成

Existing multi-organ segmentation methods usually rely on large and fully labeled datasets for training. However, medical image datasets are typically decentralized by privacy constraints and partially labeled due to the high costs of full annotation in clinical practice, resulting in label inconsistency across medical centers. Federated learning offers privacy-preserving decentralized training, b...

中文摘要翻译待生成

Deformable multi-contrast image registration is a challenging yet crucial task due to the complex, non-linear intensity relationships across different imaging contrasts. Conventional registration methods typically rely on iterative optimization of the deformation field, which is time-consuming. Although recent learning-based approaches enable fast and accurate registration during inference, their ...

中文摘要翻译待生成

Three-Dimensional Gaussian representation (3DGS) has shown substantial promise in the field of computer vision, but remains unexplored in the field of magnetic resonance imaging (MRI). This study explores its potential for the reconstruction of isotropic resolution 3D MRI from undersampled k-space data. We introduce a novel framework termed 3D Gaussian MRI (3DGSMR), which employs 3D Gaussian distr...

中文摘要翻译待生成

The accurate diagnosis of early mild cognitive impairment is crucial for timely intervention and treatment of dementia. But it is challenging to distinguish from normal aging due to its complex pathology and mild symptoms. Recently, effective hyper-connectivity identified through directed hypergraph can be considered as an effective analysis approach for early detection of mild cognitive impairmen...

中文摘要翻译待生成

Ring artifacts in CT are caused by uncalibrated variations in detector pixels and are especially prevalent with emerging photon counting detectors (PCDs). Control of ring artifacts is conventionally accomplished by improving either hardware manufacturing or software correction algorithms. An alternative solution is detector autocalibration, in which two redundant samples of each line integral are ...

中文摘要翻译待生成

Magnetic resonance imaging (MRI) is an indispensable tool for clinical knee examination, which often scans 2D stacked slices from multiple views. Radiologists typically locate lesion regions in one view, and then refer to other views to formulate a comprehensive diagnosis. However, existing computer-aided diagnosis methods fall short of identifying and fusing local regions in multi-view scans, lea...

中文摘要翻译待生成

Imbalanced small datasets are common scenarios in the field of machine learning for medical imaging, especially in real-world clinical applications. Many existing works focus on synthesize new images via data generation. However, generative methods cannot ensure reliability for medical images where categories cannot be easily distinguished, such as the pathologic complete response (pCR) evaluation...

中文摘要翻译待生成

Reliable localization of cephalometric landmarks is essential for automated orthodontic analysis. Existing approaches are limited by high computational cost or complex multi-stage pipelines, hindering end-to-end optimization. In this work, we propose an end-to-end Transformer-based Cephalometric Landmark Regression network (CeLR) for high-resolution X-ray images. First, a feature extractor capture...

中文摘要翻译待生成

Cone-beam Computed Tomography (CBCT) provides real-time three-dimensional (3D) imaging support for intraoperative navigation. However, high-attenuation metal implants introduce severe metal artifacts in reconstructed CBCT images. These artifacts compromise image quality and therefore may affect diagnostic accuracy. Current CBCT metal artifact reduction (MAR) algorithms overlook the complementary i...

中文摘要翻译待生成

Due to the dynamic changes and complex time-delay characteristics of signal transmission between brain regions, this information can help identify early signs in individuals with Alzheimer’s Disease (AD). Conventional Functional Connectivity Networks (FCN) may overlook interaction delays, leading to inaccurate representations of activity between brain regions. Recognizing the varying delays betwee...

中文摘要翻译待生成