Early Access

Multi-source computed tomography (MSCT) significantly improves temporal resolution but suffers from severe forward and cross scatter artifacts. Software-based scatter correction methods avoid additional hardware costs and radiation dose; however, model-based methods struggle with high-order scatter estimation, while deep learning–based methods lack physical constraints. To address these limitation...

中文摘要翻译待生成

Domain adaptation methods aim to bridge the gap between datasets by enabling knowledge transfer across domains, reducing the need for additional expert annotations. However, many approaches struggle with reliability in the target domain, an issue particularly critical in medical image segmentation, where accuracy and anatomical validity are essential. This challenge is further exacerbated in spati...

中文摘要翻译待生成

Mixture-of-Experts (MoE) architectures achieve scalable learning by routing inputs to specialized subnetworks through conditional computation. However, conventional MoE designs assume homogeneous expert capability and domain-agnostic routing—assumptions that are fundamentally misaligned with medical imaging, where anatomical structure and regional disease heterogeneity govern pathological patterns...

中文摘要翻译待生成

Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a complication known as no-reflow , defined by persistent microvascular hypoperfusion that undermines tissue recovery and worsens clinical outcomes. Although prompt identification is crucial, standard clinical practice relies on perfusion magnetic resonance imaging (MRI) within 24 hours post-procedure, delaying intervention. In this work, we introduce the first-ever machine learning (ML) framework to predict no-reflow immediately after EVT by leveraging previously unexplored intra-procedural digital subtraction angiography (DSA) sequences and clinical variables. Our retrospective analysis included AIS patients treated at UCLA Medical Center (2011–2024) who achieved favorable mTICI scores (2c or 3) and underwent pre- and post-procedure MRI. No-reflow was defined as a > 15% reduction in relative cerebral blood volume or flow within the infarct core compared to the contralateral hemisphere. From DSA sequences (anteroposterior and lateral views), we extracted statistical and temporal perfusion features from the target downstream territory to train ML classifiers for predicting no-reflow. Our preliminary results demonstrate that this novel method out-performed a clinical-features baseline (AUROC: 0.9330 vs. 0.7768 (p = 0.006)), suggesting that real-time DSA perfusion dynamics may encode clinically relevant information related to microvascular integrity. This approach establishes a preliminary foundation for immediate, accurate no-reflow prediction, enabling clinicians to proactively manage high-risk patients without reliance on delayed imaging, though it warrants validation in larger, independent cohorts.

急性缺血性卒中（AIS）通过血管内血栓切除术（EVT）成功实现大血管再通后，部分患者会出现一种称为无复流（no-reflow）的并发症，其特征为持续的微血管低灌注，损害组织恢复并恶化临床结局。尽管及时识别至关重要，但标准临床实践依赖于灌注磁共振...

Coronary computed tomography angiography (CCTA) is a pivotal non-invasive imaging modality for diagnosing cardiac disease. However, due to the temporal resolution limitations, cardiac structures, specifically coronary arteries, may suffer from motion artifacts when CCTA is applied to patients with arrhythmias or high heart rates. Limited-angle CT (LA-CT) emerges as a promising alternative by signi...

中文摘要翻译待生成

Universal medical image registration through a single model handling various registration tasks has attracted increasing interest. However, existing deep learning-based methods face two major challenges in adapting to universal registration tasks: 1) they lack generalizable feature representation capabilities for cross-task registration; 2) they rely solely on model architectures with fixed parame...

中文摘要翻译待生成

Objective evaluation of quantitative-imaging (QI) methods based on how reliably they measure true values is important for clinical translation. Performing such evaluation with patient data is highly desirable but hindered by the lack of gold standards. To address this challenge, advancing on previous studies, we propose a no-gold-standard evaluation technique, NGSE-Corr, that objectively evaluates...

中文摘要翻译待生成

Virtual coronary intervention planning (VCIP) aims to optimize the hemodynamic outcomes of percutaneous coronary intervention (PCI) in patients with coronary stenosis. However, its clinical adoption remains constrained by the computational burden associated with evaluating numerous combinatorial intervention strategies, leading to time-consuming workflows and potentially suboptimal decisions in th...

中文摘要翻译待生成

Dynamic total body positron emission tomography (TB-PET) makes it feasible to measure the kinetics of the tracer in all organs of the body simultaneously which may lead to important applications in multi-organ disease and systems physiology. Since whole-body kinetics are highly heterogeneous with variable signal-to-noise ratios, parametric images should ideally comprise not only point estimates bu...

中文摘要翻译待生成

Unsupervised anomaly detection (UAD) in brain MRI is crucial for early diagnosis, yet generalizing existing methods across diverse diseases, sequences, and missing data scenarios remains a significant challenge. Current reconstruction-based methods often fail to detect subtle anomalies, while conventional translation methods lack flexibility regarding input sequences. To address these limitations,...

中文摘要翻译待生成

3D brain MRI studies often examine subtle morphometric differences between cohorts that are hard to detect visually. Given the high cost of MRI acquisition, these studies could greatly benefit from image syntheses, particularly counterfactual image generation, as has been the case for applications in computer vision. However, counterfactual models struggle to produce anatomically plausible MRIs du...

中文摘要翻译待生成

Reconstructing dynamic surgical scenes from endoscopic videos remains a fundamental challenge in robot-assisted surgery. Existing methods primarily focus on deformable tissues, overlooking the presence of articulated instruments. To bridge this gap, we present EndoLRMGS, the first unified framework capable of reconstructing both deformable tissue and articulated instruments in a modular approach f...

中文摘要翻译待生成

Deep learning (DL) methods can reconstruct highly accelerated magnetic resonance imaging (MRI) scans, but they rely on application-specific large training datasets and often generalize poorly to out-of-distribution data. Self-supervised deep learning algorithms perform scan-specific reconstructions, but still require complicated hyperparameter tuning based on the acquisition and often offer limite...

中文摘要翻译待生成

Computed tomography (CT) scanners are widely used to obtain detailed internal images in clinical diagnosis. Highly attenuated metallic implants resulting from strong and energy-dependent attenuation cause metal artifacts in CT scanning. However, current supervised deep network-based metal artifact reduction (MAR) methods hardly generalize in clinical diagnosis and treatment because of difficult ac...

中文摘要翻译待生成

Metadata-guided cross-modality 3D MRI synthesis aims to generate target-contrast volumes from source-modality data conditioned on clinically available metadata, which is important for enhancing clinical imaging flexibility. However, existing methods still suffer from two main limitations: 1) They neglect spatial dependencies within volumetric representations, yielding structurally ambiguous featur...

中文摘要翻译待生成

Breast density impacts cancer detection by masking tumors within fibroglandular tissue and there are disparities in screening outcomes across racial groups. However, it remains unclear whether these differences reflect inherent tissue characteristics or systemic bias. To isolate the effect of breast density on lesion detectability across racial subgroups, we conducted a retrospective case-control study using raw tomosynthesis projections from 902 women (453 cases, 451 matched controls) across BI-RADS density categories and self-reported race. Identical in-silico spiculated masses (8–15 mm) and microcalcification clusters (10–14 mm) were inserted into the projections using a calibrated lesion model. Images were reconstructed in the same manner to avoid proprietary processing in lesion detection. Lesion detectability was assessed with Channelized Hotelling Observers. Regression and causal mediation analyses examined the relationships between race, density, and detectability. As result, detectability decreased with increasing density; for masses, the area under the receiver operating characteristic curve (ROC AUC) reduced significantly from 0.93 to 0.85 (BIRADS A to D), whereas for microcalcifications AUC decreased from 0.85 to 0.78 across the same density range. Discrimination remained higher for masses than calcifications (AUC=0.89 vs. AUC=0.82). Stratified analyses showed slightly higher detectability in Non-Hispanic Black women compared with Non-Hispanic White and Asian American women, largely reflecting differences in density. Mediation analysis revealed that breast density accounted for 38–55% of the observed race-associated detectability differences. Mediation analyses have shown that density is the dominant factor in detectability. These findings support the development of calibrated detection models and personalized screening strategies that account for breast density.

中文摘要翻译待生成

Medical imaging studies often rely on a single sample per subject, assuming it is representative of their physiological traits. However, variations in how input descriptors are defined or computed (e.g. due to a lack of consensus in the scientific field) may have a crucial impact on the analysis, and are hardly considered in practice. In this paper, we propose an original strategy based on representation learning to estimate a parametric map reflecting the impact of such definitional differences on a given physiological descriptor, previously extracted from medical images. We consider the different definitions or computations of such physiological descriptors as different high-dimensional data, potentially of heterogeneous types. We specifically focus on myocardial deformation (strain), for which there is limited agreement on its definition. We first use manifold alignment to match the latent representations associated with the different definitions of this descriptor. Then, we formulate plausible distributions in the latent space to represent definitional divergence across descriptors, from which we reconstruct a high-dimensional parametric map to visualize such definitional divergence. Due to the lack of proper ground truth for this specific clinical application, we first demonstrate this methodology on toy experiments and then expand the evaluation on right ventricular strain data from subjects obtained from 3D echocardiographic image sequences, for which different types of strain are available at each point of the right ventricle endocardial surface mesh. Beyond this illustrative application, our methodology has the potential to be generalised to many other population analyses considering heterogeneous high-dimensional descriptors.

医学影像研究通常依赖于每个受试者的单个样本，假设其代表其生理特征。然而，输入描述的定义或计算方式的变化（例如由于科学领域缺乏共识）可能对分析产生关键影响，并且在实践中很少被考虑。在本文中，我们提出了一种基于代表性的原始策略...

Accurate multi-organ segmentation across heterogeneous medical images is pivotal for real-world surgical navigation. The scarcity of annotation constitutes a well-established consensus in the field, prompting semi-supervised learning to emerge as a prominent solution. However, two critical bottlenecks persist in clinical translation: (1) inter-class feature ambiguity, and (2) high multi-source sam...

中文摘要翻译待生成

Ordinal regression is well-known for lever-aging the underlying inherent order between successive categories to obtain additional regularization beyond traditional probabilistic classification mechanism. However, there are challenges in real-world medical grading tasks: 1) The uneven distribution of disease severity levels, characterized by a long-tailed format, complicates the ordinal regression ...

中文摘要翻译待生成

Automatic echocardiography video segmentation is crucial for accurate diagnosis of cardiovascular diseases, as high-quality segmentation significantly improves automated lesion detection. However, deep learning methods still face challenges including speckle noise, dynamic ventricular changes, limited annotations, and the requirement for real-time inference in clinical practice. In this paper, we propose MSSNet, a novel semi-supervised method based on the efficient sequence modeling architecture Mamba, to address these challenges. To enhance noise robustness and foreground tracking, we design a flexible and efficient spatiotemporal synergistic guidance (SSG) module that leverages attention weights from historical frames to guide subsequent segmentation. By incorporating stable structural context and modeling inter-frame dependencies through weight propagation, SSG effectively mitigates segmentation errors caused by strong local noise and ventricular dynamics while maintaining low computational complexity. To alleviate limited annotation, we further introduce two semi-supervised modules: region-wise adaptive cross-mix (RAC) and dynamic offset correction (DOC). RAC simulates clinically plausible samples via regional mixing to enrich semantic details and strengthen feature learning, while DOC continuously integrates features from high-quality pseudo-labels during training. Experiments on the CAMUS and EchoNet-Dynamic datasets demonstrate that MSSNet outperforms existing SOTA methods in segmentation accuracy and achieves notable improvements in inference speed. The code is available at https://github.com/SSS666-klk/MSSNet.

自动超声心动图视频分割对于心血管疾病的准确诊断至关重要，因为高质量的分割显著提高了自动病灶检测。然而，深度学习方法仍然面临挑战，包括散斑噪声、动态心室变化、有限的标注以及临床实践中实时推理的需求。在本文中，我们……

Limited-angle cone-beam computed tomography (LA-CBCT) enables rapid imaging and reduced radiation exposure, but its severely incomplete projection data lead to ill-posed reconstructions with prominent artifacts, limiting clinical applicability. Recent advances in 3D Gaussian Splatting (3D-GS) have shown promise for efficient tomographic reconstruction, yet its performance remains highly sensitive to initialization. In this work, we present SPARK (Structurally-Informed Projection-Accelerated Reconstruction), a two-stage framework that introduces a generative, structurally informed initialization for 3D-GS. In the first stage, a geometry-conditioned network directly predicts complete 3D Gaussian parameters from a sparse subset of projections, embedding learned anatomical priors to mitigate artifact propagation. In the second stage, the generated scene is refined through physics-based 3D-GS optimization, yielding high-fidelity reconstructions consistent with measured projections. Extensive experiments on public datasets demonstrate that SPARK substantially improves both image quality and convergence speed, achieving superior PSNR/SSIM in severely limited-angle scenarios compared with analytical, iterative, and deep learning baselines. Moreover, SPARK reconstructions provide enhanced inputs for downstream post-processing networks, further boosting image fidelity. These results suggest that SPARK is a promising prior-informed 3D-GS framework for simulated LA-CBCT reconstruction under limited angular coverage, providing an effective bridge between data-driven anatomical priors and physics-based projection-domain refinement.

中文摘要翻译待生成

Contrast-free myocardial infarction (MI) segmentation is essential for mitigating the health risks associated with contrast agents (CAs) in clinical diagnostics. However, existing approaches are limited by their reliance on strictly paired CINE sequences and contrastenhanced images, which are often difficult to obtain because patient conditions and imaging protocols often cause inter-modality slic...

中文摘要翻译待生成

Ultrasound molecular imaging (USMI) is an imaging approach that utilizes targeted microbubbles (MBs) to highlight biomarkers of disease. While differential targeted enhancement (DTE) is the current state-of-the-art for USMI, its reliance on destructive pulses hinders real-time clinical application. We have developed a neural network-based nondestructive USMI, validated in vivo using a transgenic m...

中文摘要翻译待生成

Synthesizing high-quality images from low-field MRI holds significant potential. Low-field MRI is cheaper, more accessible, and safer, but suffers from low resolution and poor signal-to-noise ratio. This synthesis process can reduce reliance on costly acquisitions and expand data availability. However, synthesizing high-field MRI still suffers from a clinical fidelity gap. There is a need to prese...

中文摘要翻译待生成

In recent times, geometric foundation models have demonstrated remarkable performance in depth estimation tasks, benefiting from exposure to large-scale data that enables the learning of intricate geometric structures and spatial dependencies. However, their large parameter sizes and high computational complexity pose significant challenges in meeting the efficiency requirements of downstream surg...

中文摘要翻译待生成