Early Access

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...

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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...

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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...

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Generalized category discovery aims to identify known medical categories and unknown new medical categories from unlabeled data by migrating knowledge from labeled datasets containing only known categories, which is crucial for disease understanding and precision medicine. Many methods have been proposed and significantly improved the performance of GCD in medical images. However, most of the exis...

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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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Functional MRI (fMRI) and single-cell transcri ptomics are pivotal in Alzheimer’s disease (AD) research, each providing unique insights into neural function and molecular mechanisms. However, integrating these complementary modalities remains largely unexplored. Here, we introduce scBIT, a novel method for enhancing AD prediction by combining fMRI with single-nucleus RNA (snRNA). scBIT leverages s...

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Orthodontic motion planning plays a crucial role in digital orthodontics by predicting tooth motion sequences to assist dentists in formulating treatment plans efficiently. Most prior work generates the entire intermediate tooth motion sequence given the initial and target tooth alignments. In practice, only the initial alignment of the patient is obtained. However, no existing method can predict ...

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Three-dimensional ultrasound localization microscopy (ULM) enables comprehensive visualization of the vasculature, thereby improving diagnostic reliability. Nevertheless, its clinical translation remains challenging, as the exponential growth in voxel count for full 3D reconstruction imposes heavy computational demands and extensive post-processing time. In this row-column array (RCA)-based 3D in ...

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4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive technique for volumetric, time-resolved blood flow quantification. However, apparent trade-offs between acquisition time, image noise, and resolution limit clinical applicability. In particular, in regions of highly transient flow, coarse temporal resolution can hinder accurate capture of physiologically relevant flow variations. D...

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Automated whole-body lesion segmentation in 18F-FDG PET/CT images marks a pivotal breakthrough in oncological diagnostics, substantially improving the accuracy and efficiency of tumor burden assessment. Manual segmentation is often plagued by significant interobserver variability, underscoring the necessity for automated solutions. The synergistic combination of PET’s exceptional sensitivity for d...

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Whole Slide Images (WSIs) have been widely used in computational pathology (CPath) for various tasks. However, obtaining high-quality annotations remains a major bottleneck. Task-aware unsupervised anomaly detection models offer a promising alternative, as they are trained solely on task-specific normal data and can be adapted to clinically defined objectives, such as cancer detection, depending o...

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Colonoscopy video generation delivers dynamic, information-rich data critical for diagnosing intestinal diseases, particularly in data-scarce scenarios. High-quality video generation demands temporal consistency and precise control over clinical attributes, but faces challenges from irregular intestinal structures, diverse disease representations, and various imaging modalities. To this end, we pr...

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Tagged magnetic resonance imaging (tMRI) is a valuable tool for visualizing and quantifying tissue deformation in vivo. Its use is often hampered, however, by tag fading, long computation times, and the challenge of ensuring diffeomorphic, incompressible motion fields. In this paper, we describe a novel integration of the harmonic phase (HARP) approach to tMRI analysis with an unsupervised deep le...

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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...

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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 represe...

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The purpose of this work is to demonstrate a robust and clinically validated method for correcting sound speed aberrations in medical ultrasound. We propose a correction method that calculates the focus delays directly from the observed two-way distributed average sound speed. The method beamforms multiple coherence images and selects the sound speed that maximizes the coherence for each image pix...

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Multimodal imaging has become an essential tool in clinical ophthalmology, offering complementary perspectives for disease diagnosis. However, current automated diagnostic approaches often fail to fully exploit the rich, complementary information provided by different imaging modalities. In this paper, to advance automated ophthalmic disease diagnosis through effective multimodal data integration,...

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Gleason grading, the clinical gold standard for prostate cancer assessment, is based on subjective evaluation of glandular architecture, resulting in interobserver variability and limited scalability. This highlights the need for automated grading systems. However, their development is hindered by the scarcity of annotated pathology data. Self-supervised learning (SSL) presents a promising solutio...

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Owing to the prohibitive cost of manual annotation for enormous medical images, self-supervised learning (SSL) has gained substantial attention and shown promise in various medical imaging tasks. Among SSL approaches, contrastive learning has emerged as a prominent one, encouraging models to encode semantic information that remains invariant between different augmented views. However, this invaria...

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Mild cognitive impairment (MCI) is the prodromal stage of dementia involving complex interactions between the brain and peripheral organs. Emerging evidence indicates that heart dysfunction and gut microbiota dysbiosis can contribute to MCI pathogenesis. Yet, these discoveries of cross-organ interactions have not been applied to assist MCI diagnosis. In this work, we propose a novel diagnostic fra...

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Sampling jitter, i.e., random deviations in the time instants when samples are taken, causes frequency-dependent noise that reduces the signal-to-noise ratio (SNR). This paper generalizes the concept of jitter to magnetoencephalography (MEG) sensor arrays that spatially sample the quasistatic magnetic field due to brain activity. It is shown that spatial jitter, i.e., random deviations in the MEG ...

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Motion remains a problem in clinical MRI, largely because all existing effective correction methods come with a penalty – constraints on pulse sequence parameters, expensive/bulky equipment, or extra steps in the workflow. The Pilot Tone (PT) is a small device that does not physically contact with the patient and only minimally impacts workflows. However, its signals can be difficult to process du...

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Limited-angle computed tomography (LACT) improves temporal resolution and reduces radiation dose, but suffers from severe artifacts due to missing projections. Clinical workflows record abundant patient- and acquisition-level metadata, yet such information remains underutilized in image reconstruction. To tackle the ill-posed LACT inverse problem, we propose a metadata-guided two-stage diffusion f...

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Low-dose computed tomography (CT) denoising is crucial for reduced radiation exposure while ensuring diagnostically acceptable image quality. Despite significant advancements driven by deep learning (DL) in recent years, existing DL-based methods, typically trained on a specific dose level and anatomical region, struggle to handle diverse noise characteristics and anatomical heterogeneity during varied scanning conditions, limiting their generalizability and robustness in clinical scenarios. In this paper, we propose FoundDiff, a foundational diffusion model for unified and generalizable LDCT denoising across various dose levels and anatomical regions. FoundDiff employs a two-stage strategy: (i) dose-anatomy perception and (ii) adaptive denoising. First, we develop a dose- and anatomy-aware contrastive language-image pre-training model (DA-CLIP) to achieve robust dose and anatomy perception by leveraging specialized contrastive learning strategies to learn continuous representations that quantify ordinal dose variations and identify salient anatomical regions. Second, we design a dose-and anatomy-aware diffusion model (DA-Diff) to perform adaptive and generalizable denoising by synergistically integrating the learned dose and anatomy embeddings from DA-CLIP into diffusion process via a novel dose and anatomy conditional block (DACB) based on Mamba. Extensive experiments on a large simulated multi-dose CT dataset spanning three anatomical regions, together with cross-dataset evaluations on Mayo-2016, CQ500, and piglet datasets, demonstrate superior denoising performance and strong generalization to unseen dose levels and anatomical regions. The codes and models are available at https: //github.com/hao1635/FoundDiff.

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Fiber tract segmentation is crucial for clinical applications such as brain function interpretation and surgical planning. Existing methods typically adopt either a cortical-parcellation-based or fiber clustering approach, but fail to simultaneously integrate heterogeneous information (e.g., streamline shape, point position, anatomical priors). In this work, we propose Fiber HGNN, a novel heteroge...

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