Volume 45, Issue 7

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...

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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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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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Glaucoma is a leading cause of irreversible blindness worldwide, with asymptomatic early stages often delaying diagnosis and treatment. Early and accurate diagnosis requires integrating complementary information from multiple ocular imaging modalities. However, most existing studies rely on single- or dual-modality imaging, such as fundus and optical coherence tomography (OCT), for coarse binary classification, thereby restricting the exploitation of complementary information and hindering both early diagnosis and stage-specific treatment. To address these limitations, we propose glaucoma lesion evaluation and analysis with multimodal imaging (GLEAM), the first publicly available tri-modal glaucoma dataset comprising scanning laser ophthalmoscopy fundus images, circumpapillary OCT images, and visual field pattern deviation maps, annotated with four disease stages, enabling effective exploitation of multimodal complementary information and facilitating accurate diagnosis and treatment across disease stages. To effectively integrate cross-modal information, we propose hierarchical attentive masked modeling (HAMM) for multimodal glaucoma classification. Our framework employs hierarchical attentive encoders and light decoders to focus cross-modal representation learning on the encoder. The attention module, named multimodal-channel graph attention (MCGA), boosts glaucoma classification performance by emulating two key clinical reasoning steps: first, it uses a multi-head modality gating mechanism to replicate ophthalmologists’ confidence scoring of fundus, OCT, and VF modalities; then, MCGA leverages a relational graph attention network to cross-examine structural-functional consistencies of weighted modalities. The experiments on GLEAM demonstrate that tri-modal fusion significantly outperforms single-modal and dual-modal configurations. Moreover, our proposed HAMM achieves superior performance compared with state-of-the-art multimodal learning methods. The dataset and code are publicly available via https://github.com/microewing/HAMM.

青光眼是全球不可逆失明的主要原因，其无症状早期阶段常常延误诊断和治疗。早期准确诊断需要整合来自多种眼部成像模式的互补信息。然而，大多数现有研究依赖于单模态或双模态成像，如眼底和光学相干断层扫描（OCT），用于粗略的二元分类...

Accurate identification of neurological disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and Autism Spectrum Disorder (ASD) is challenging due to subtle early-stage symptoms and heterogeneous brain dynamics. Resting-state functional MRI (rs-fMRI) enables the construction of functional brain networks, where Graph Neural Networks (GNNs) have shown promise for disease classificat...

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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...

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Clinically, bi-parametric MRI (bp-MRI), including T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient map, offers essential prior localization of biopsy and focal therapy for suspicious clinically significant prostate cancer (csPCa), and accurate csPCa delineation from bp-MRI is crucial for better outcomes. However, due to the complexity and high variability in appe...

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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...

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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...

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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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Focused transmits are the most commonly used transmit strategy for echocardiograms, but suffer from relatively low frame rates, and in 3D, even lower volume rates. Fast imaging based on unfocused transmits has disadvantages such as motion decorrelation and limited harmonic imaging capabilities. This work introduces a patient-adaptive focused transmit and receive scheme that has the ability to dras...

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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...

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Spatial Transcriptomics (ST) technology detects gene expression from tissue biopsies, playing an emerging role in cancer diagnosis and precision medicine. However, the high cost of ST technology limits its broader application. Recently, deep learning approaches have provided insight into predicting gene expression based on H&E-stained histopathology images. Nevertheless, the relationship between morphological features and gene expression is highly complex. To address these challenges, we propose DiffBulk, a novel two-stage framework that leverages conditional diffusion models to learn expressive image representations enriched with gene expression information. In the first stage, we introduce a gene-to-image conditional diffusion model equipped with a permutationinvariant open-embedding gene encoder, which enables unified training across diverse gene panels. In the second stage, diffusion-derived features are fused with representations from a pathology foundation model, effectively bridging the domain gap and improving downstream gene expression prediction. We evaluate DiffBulk on high-quality Xenium ST data curated from the HEST dataset and the CrunchDAO challenge, constructing tile-level pseudo-bulk datasets for training and evaluation. Extensive experiments demonstrate that DiffBulk consistently outperforms state-of-the-art baselines across all metrics for gene expression prediction. These findings highlight the potential of diffusion-based gene-image representation learning and suggest promising directions for future research.

空间转录组学（ST）技术从组织活检中检测基因表达，在癌症诊断和精准医学中发挥着新兴作用。然而，ST技术的高成本限制了其更广泛的应用。最近，深度学习方法为基于H&E染色组织病理学图像预测基因表达提供了思路。然而，m…

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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Age-related macular degeneration with abnormal blood vessel growth (neovascular AMD) is the leading cause of vision loss in elderly populations. While anti-VEGF injections are the standard treatment, they present financial burdens for patients and vary in effectiveness. Predicting treatment efficacy is therefore crucial for patient care. Current prediction methods fail to fully integrate informati...

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Conventional registration approaches frequently underperform when applied to sparse feature alignment (e.g., retinal vessels and filamentous collagen fibers in second-harmonic generation (SHG) and bright-field (BF) images), as these tasks demand simultaneous handling of global affine registration and local deformation correction. End-to-end learning-based approaches struggle with minimal effective...

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Developing robust and effective computer-aided diagnostic (CAD) methods for thyroid ultrasound (TUS) remains a key challenge in medical imaging. Prior work has largely focused on binary or multi-class lesion classification, whereas real-world diagnosis follows standardized guidelines based on combinations of lexicon-level descriptors. These combinations naturally exhibit long-tailed distributions ...

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Objective, task-based measures of image quality (IQ) have been widely advocated for assessing and optimizing medical imaging technologies. Besides signal detection theory-based measures, information-theoretic quantities have been proposed to quantify task-based IQ. For example, task-specific information (TSI), defined as the mutual information between an image and a task variable, represents an op...

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Cardiac Magnetic Resonance (CMR) imaging is widely used to personalize heart models for cardiac digital twin analysis because of its ability to visualize soft tissues and capture dynamic functions. However, CMR images have an anisotropic nature, characterized by large inter-slice distances and misalignments from cardiac motion. These limitations result in data loss and measurement inaccuracies, hi...

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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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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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Sparse-View CT (SVCT) reconstruction improves temporal resolution and reduces radiation dose, yet its clinical use is hindered by artifacts due to view reduction and domain shifts from scanner, protocol, or anatomical variations, leading to performance degradation in out-of-distribution (OOD) scenarios. We propose a Cross-Distribution Diffusion Priors-Driven Iterative Reconstruction (CDPIR) framew...

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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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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...

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To be adopted in safety-critical domains like medical image analysis, AI systems must provide human-interpretable decisions. Variational Information Pursuit (VIP) offers an interpretable-by-design framework by sequentially querying input images for human-understandable concepts, using their presence or absence to make predictions. However, existing V-IP methods overlook sample-specific uncertainty...

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