Early Access

MRI data acquired using multi-echo spin-echo or gradient-echo sequences contain “encoded” water signals from various tissue compartments, i.e., myelin, axon, and extracellular space. Accurate separation of these components is desirable for tissue characterization in healthy and diseased states. However, this signal decomposition problem is very challenging because the encoding matrix is highly ill...

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Chest X-ray (CXR) plays a pivotal role in clinical diagnosis, and a variety of task-specific and foundation models have been developed for CXR interpretation. However, these models often struggle to adapt to new diagnostic tasks and complex reasoning scenarios. Recently, LLM-based agents have emerged as a promising paradigm for CXR analysis, enhancing model’s capability via tool coordination, mult...

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Ultra-low field magnetic resonance imaging (ULF-MRI) systems operating in open environments are highly susceptible to composite electromagnetic interference (EMI). Different imaging channels respond non-uniformly to EMI owing to their distinct coupling characteristics. Here, we investigate channel-specific interference pathways in a permanent–magnet–based low-field MRI system and show that saddle ...

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Alzheimer’s disease (AD) risk prediction relies on accurately characterizing pathological mechanisms underlying AD progression. However, existing methods struggle with heterogeneous multi-omics data and often fail to capture the spatiotemporal dynamics of the disease, limiting their predictive performance. In this paper, an integrated framework fusing spatial and temporal information is proposed t...

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Echocardiogram video synthesis has emerged as a promising solution to alleviate data scarcity for training intelligent diagnostic models and to enhance clinical education. However, existing methods are typically conditioned on a single scalar metric, such as left ventricular ejection fraction (LVEF), which fails to capture the complex temporal dynamics of cardiac motion and thus limits clinical ap...

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Visual in-context learning (ICL) enables medical image segmentation models to adapt to new tasks using only a small set of annotated support images, without additional model fine-tuning. This makes visual ICL promising for clinical deployment, yet a fundamental limitation remains. Current ICL models lack the intrinsic ability to adaptively integrate supports based on their relevance to the query. ...

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Neuroimaging AI remains constrained by a model-per-disease paradigm—where separate frameworks are trained for individual disorders—limiting knowledge transfer and direct cross-disorder comparisons. While resting-state fMRI provides a powerful subject-specific functional connectivity (FC) fingerprint, it suffers from low spatial resolution and limited clinical accessibility. Conversely, structural ...

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The ability to learn sequentially from different data sites is crucial for a deep network in solving practical medical image diagnosis problems due to privacy restrictions and storage limitations. However, adapting to the incoming site leads to catastrophic forgetting on past sites and decreases generalizability on unseen sites. Existing Continual Learning (CL) and Domain Generalization (DG) metho...

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Quantitative imaging is an important feature of spectral X-ray and CT systems, especially photon-counting CT (PCCT) imaging systems, which is achieved through material decomposition (MD) using spectral measurements. In this work, we present a novel framework that makes the PCCT imaging chain end-to-end differentiable (differentiable PCCT), with which we can leverage quantitative information in the...

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We propose a Geometric Energy Field (GEF) supervision framework to enhance the robustness and structural consistency of retinal artery/vein (A/V) segmentation. We introduce two geometrically complementary energy fields: the Distance Energy Field (DEF), which encodes soft, vessel-type-specific spatial territories by modeling pixel-wise proximity to arteries and veins, thereby explicitly capturing t...

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Accurately assessing the severity of pediatric pneumonia using AI-based lung ultrasound (LUS) video scoring remains challenging due to three main limitations: (1) limited availability of expert annotations, (2) underutilization of temporal dynamics, and (3) lack of mechanisms to capture the directional patterns and temporal evolution of diagnostic artifacts. To address these challenges, we introdu...

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Multi-energy CT (MECT) offers unique advantages in material decomposition, tissue characterization, and functional imaging, positioning it as a pivotal direction for next-generation CT. Currently, standardized scanning protocols for MECT have not yet been established. Considering growing public concern over X-ray radiation exposure, we propose a complementary sparse-view scanning protocol tailored...

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Iodine plays a central role in thyroid physiology, and knowledge of its spatial distribution may aid diagnostic framing and follow-up of neoplastic thyroid pathologies. Conventional histology is the gold standard for thyroid assessment but consumes biological material, limiting volumetric context and further analyses, and offers limited specificity for iodine. We present a virtual histology (VH) w...

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Contrast-enhanced CT (CECT) is essential for clinical evaluation of vessel structures and function. However, high contrast agent dose increases the risk of renal injury. Reducing contrast agent dose decreases the contrast between vessels and surrounding tissues, which complicates diagnosis. Despite their potential in CECT synthesis, existing methods often suffer from edge unclarity, contrast anoma...

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Breast cancer is the most frequently diagnosed malignancy among women worldwide and a leading cause of cancer-related mortality. Dynamic contrast-enhanced magnetic resonance imaging plays a central role in tumor characterization and treatment monitoring, particularly in patients receiving neoadjuvant chemotherapy. However, existing artificial intelligence models for breast magnetic resonance imagi...

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Magnetic resonance imaging (MRI) can estimate three-dimensional (3D) time-resolved blood velocity fields using 4D-flow MRI, providing rich relative pressure field information. Clinical alternatives—catheterization and Doppler echocardiography—provide one-dimensional pressure drops. While the accuracy of one-dimensional pressure drops from 4D-flow has been explored, the accuracy of 3D relative pres...

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Although artificial intelligence (AI) has shown promising performance in several medical tasks, accurate dementia etiology diagnosis with AI remains challenging due to complex overlapping symptoms among diseases. Scaling up the dataset size by combining the cross-center samples may bring a gain in the pursuit of performance, while the inherent data heterogeneity across centers or populations induc...

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Automatic tooth segmentation is essential for computer-aided diagnosis and treatment planning in dentistry. Proposal generation plays a pivotal role in accurately delineating instance boundaries and is critical for improving segmentation accuracy. However, existing methods suffer from dispersive offset bias, spatial information loss, and spillover-induced centroid shift, all of which are difficult...

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Myocardial echocardiography segmentation is useful for cardiac function assessment, yet remains challenging due to continuous myocardial motion and deformation during the cardiac cycle, as well as boundary ambiguity caused by speckle noise. Existing memory-based segmentation methods typically construct memory banks by accumulating single-frame representations, where the stored memory items fail to...

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Histopathological analysis of stained tissue remains central to biomedical research and clinical care. Virtual staining (VS) offers a promising alternative, with potential to reduce costs and streamline workflows, yet hallucinations pose serious risks to clinical reliability. Here, we formalize the problem of hallucination detection in VS and propose a scalable post-hoc baseline method: Neural Hal...

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Accurate automatic segmentation of the gross tumor volume (GTV) in nasopharyngeal carcinoma (NPC) is critical for radiotherapy planning, yet conventional deterministic models do not adequately characterize the inherent uncertainty associated with ambiguous tumor boundaries on magnetic resonance imaging (MRI). Existing evidential deep learning (EDL) methods for uncertainty quantification typically ...

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Nuclei segmentation is a fundamental but challenging task in computational pathology due to diverse morphologies, blurred boundaries, and staining variations. Despite remarkable progress, existing models often suffer from structural instability under morphological and staining variations. We attribute this instability to disrupted frequency–spatial consistency and address it through FreqPath-Net, ...

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Chromium-compensated Gallium Arsenide (GaAs:Cr) has emerged as a promising sensor material for photon counting detectors (PCDs) due to its excellent resistivity and charge carrier mobility. However, due to the limitations of the Cr compensation process, the GaAs:Cr wafer thickness is less than 1 mm, which distorts its X-ray absorption efficiency and limits its application in clinical computed tomo...

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Array topology in magnetocardiography system dictates spatial resolution and clinical applicability, impacting topographic reconstruction, which affects the accuracy of feature and source localization. Existing array designs rely on task-driven or empirical criteria without accounting for intrinsic imaging properties to ensure complete high-precision reconstruction and broad applicability, and ana...

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Limited-angle cardiac CT reconstruction is a severely ill-posed problem, where incomplete angular coverage leads to strong artifacts and structural distortions. Although diffusion-based methods have shown strong potential for improving reconstruction quality, their high computational cost, large memory demand, and slow inference remain major barriers to practical clinical deployment. To address th...

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