Volume 45, Issue 5

Stemmed from our novel single-photon imaging concept of detector self-collimation—which leverages detectors themselves as collimators to overcome the inherent resolution-sensitivity trade-off in conventional SPECT—this study presents the design and evaluation of the first full-ring self-collimation SPECT (SC-SPECT) scanner for small animal imaging. The system features four concentric detector ring...

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Precise landmark annotation in cardiac ultrasound images is fundamental for quantitative cardiac health assessment. However, the time-intensive nature of manual annotation typically constrains clinicians to annotate only selected key frames, limiting comprehensive temporal analysis capabilities. While recent automated landmark detection methods have demonstrated success for key-frame analysis, the...

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Intraoperative anomalies cause deviations from the ideal surgical workflow, heightening the risk of consequential errors and complications. Their reliable recognition has traditionally relied on continuous surgeon monitoring, yet automated anomaly detection systems are now indispensable for the safe advancement of assistive and autonomous surgery. However, existing approaches struggle with domain ...

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Cone-beam Computed Tomography (CBCT) is essential for target localization and treatment planning in image-guided radiotherapy for lung cancer. Dynamic reconstruction is an ill-posed inverse problem, as each motion state is captured by only one single projection. Several supervised learning methods have been proposed, but they rely on training with paired simulated or real datasets, and reconstruct...

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Imbalanced class distributions among different organs pose significant challenges in real-world semi-supervised multi-organ segmentation. Integrating anatomical priors offers a promising research direction to mitigate these imbalances. In this paper, we explore the capabilities of Multimodal Large Language Models (MLLM) to extract robust, generic textual anatomical insights serving as prior knowle...

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Well-designed and trained deep neural networks can solve inverse electromagnetic problems much faster than conventional solvers. However, they need a physics framework to ensure producing physically correct results. Since most physics-guided deep learning inverse solvers require substantial training with numerous epochs, each involving solving a forward problem, their accuracy and efficiency are l...

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Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications where annotations are severely scarce and multimodal analysis is required. However, existing methods typically filter out domain-specific information to improve generalization, which inadvertently limits cross-domain performance and degrades source-domain accuracy. To...

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Multi-modal learning is extensively applied to diagnose brain diseases such as epilepsy and Alzheimer’s disease. However, incomplete multi-modal data, where some modalities are unavailable or difficult to collect, limits the effectiveness of conventional methods. Additionally, existing approaches often overlook semantic relationships between neighbors with the same-label and latent information in ...

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While Generative Adversarial Nets (GANs) and Diffusion Models (DMs) have achieved impressive results in natural image synthesis, their core strengths – creativity and realism – can be detrimental in medical applications, where accuracy and fidelity are paramount. These models instead risk introducing hallucinations and replication of unwanted acquisition noise. Here, we propose YODA (You Only Deno...

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Efficient convolutional neural network (CNN) architecture design has attracted growing research interests. However, they typically apply single receptive field (RF), small asymmetric RFs, or pyramid RFs to learn different feature representations, still encountering two significant challenges in medical image classification tasks: i) They have limitations in capturing diverse lesion characteristics...

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The segmentation of pelvic fracture fragments in CT and X-ray images is crucial for trauma diagnosis, surgical planning, and intraoperative guidance. However, accurately and efficiently delineating the bone fragments remains a significant challenge due to complex anatomy and imaging limitations. The PENGWIN challenge, organized as a MICCAI 2024 satellite event, aimed to advance automated fracture ...

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The reconstruction of monocular endoscope video scenes is essential for enhancing the application and analysis of surgical endoscopic images. However, restricted by the narrow space of endoscopic movement and the obstruction of vision within cavities, it is difficult for most conventional methods to perform high-quality reconstruction. To address these challenges, a novel dynamic growing 3D Gaussi...

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Surgical phase recognition (SPR) is essential for surgical workflow analysis and provides immediate guidance during procedures. Existing methods aggregate frame-level information into a global representation and treat the task as frame-wise classification. However, this pipeline lacks a feedback mechanism for integrating historical information into local temporal modeling. To address this limitati...

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Acoustic wave detection techniques like ultrasound (US) and photoacoustic (PA) tomography are widely used in biomedical imaging but often require expensive transducer arrays and complex setups for 3D imaging. To overcome these challenges, recent research has explored using an ergodic relay (ER) with a single-element transducer. This approach improves imaging speed at low cost and reduces complexit...

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Surgical instrument segmentation under Federated Learning (FL) is a promising direction, which enables multiple surgical sites to collaboratively train the model without centralizing datasets. However, there exist very limited FL works in surgical data science, and FL methods for other modalities do not consider inherent characteristics in surgical domain: i) different scenarios show diverse anato...

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Brain functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used to identify altered brain network patterns in attention-deficit/hyperactivity disorder (ADHD). Current graph neural network (GNN) approaches using FCNs predominantly emphasize node features while underutilizing edge information. Moreover, these GNN-bas...

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Estimating the extents of E/MEG source activities is crucial for exploring brain dynamics at high spatiotemporal resolution. In this study, we introduce a novel ESI method − Block-Champagne, a Bayesian framework designed to accurately estimate both the locations and extents of extended sources. Our approach leverages a block-sparsity constraint that models each voxel and its neighbors as a single ...

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Real-time tissue tracking is a fundamental task in liver ultrasound applications. Due to the periodic nature of liver motion, historical trajectories can offer valuable priors for target localization, particularly when foreground–background distinction is weak. However, existing trackers often exploit these trajectories as shortcuts, relying excessively on periodic respiratory patterns rather than...

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Delayed treatment of infantile retinal disease can reduce its effectiveness and may cause severe and irreversible damage. Automated diagnosis of infant retinal diseases faces challenges including subtle early lesions, diverse clinical phenotypes, imaging variations, and imbalanced data. To address these, which cannot be well addressed by existing general foundation models, we propose structure-awa...

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We propose an ultrasound approach which provides, with one single examination and one single device, access to three bone biomarkers: anatomy, tissue quality and blood flow. It unlocks ultrasound imaging inside bone by accounting for ultrasound wave speed heterogeneity and anisotropic wave refraction. This study reports the first in vivo evaluation with a comparison to peripheral Quantitative Comp...

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In recent years, deep learning technology has automated the diagnosis of gastrointestinal (GI) tract disease, enabling doctor-machine collaborative diagnosis. However, the images captured by wireless capsule endoscopy (WCE) easily suffer from varying brightness levels of low-light degradation due to the complex structure of GI tract and the limitations of the light source, which impacts both human...

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Adapting Vision Transformers (ViTs) for medical imaging is constrained by the scarcity of data and high-quality annotations, hindering effective training and robust generalization. Visual prompt learning offers a parameter-efficient solution for domain adaptation, but its success depends on accurate and task-relevant semantic guidance—a resource rarely available in real-world clinical practice des...

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Histopathological analysis constitutes the diagnostic cornerstone in disease characterization, employing diverse staining methodologies to elucidate tissue architecture. While hematoxylin and eosin (H&E) remains the foundational technique, ancillary modalities, including specialized histochemical stains, immune-histochemistry (IHC), and multiplex immune-fluorescence (mpIF), yield critical compleme...

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Accurate monitoring of pulmonary nodules’ growth is crucial for preventing lung cancer progression and improving patient outcomes. Yet, identifying high-risk nodules in computed tomography (CT) scans remains challenging due to subtle growth patterns, irregular follow-up intervals, and the limitations of current diagnostic tools. Existing methods often depend on single-timepoint analyses or assume ...

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Quantitative susceptibility mapping (QSM) is a magnetic resonance imaging technique that quantifies tissue magnetic susceptibility by deconvolving the measured signal phase data. Accurate background field removal is essential for QSM, especially in surface regions of the brain, such as the cerebral cortex, where the background field interference is substantial. Existing methods have errors in esti...

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