Volume 45, Issue 6

Although MRI reconstruction requires a dealiasing transformation from undersampled to fully-sampled data, task-agnostic diffusion priors sample images via a denoising-based generative trajectory from an asymptotic start-point of Gaussian noise onto fully-sampled data. Since aliasing artifacts in MR images carry spatial structure deviating from Gaussian noise, this noise-governed trajectory can cau...

尽管磁共振成像重建需要从欠采样到全采样的去混叠变换，但与任务无关的扩散先验通过基于去噪的生成轨迹，从高斯噪声的渐近起点对全采样数据进行采样。由于磁共振图像中的混叠伪影具有偏离高斯噪声的空间结构，这种受噪声支配的轨迹可能会……

Targeted Alpha Therapy (TAT), using alpha-emitting radionuclides (AER) such as 225Ac, shows promise for the treatment of advanced and refractory cancers. Currently, TAT is prescribed on the basis of activity (e.g., MBq, kBq/kg), with no account taken of individual biodistribution or kinetics. The delivery of patient-specific treatment, based on absorbed dose criteria, requires in-vivo imaging of t...

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Sensitive reconstruction of peripheral airway branches is critical for bronchoscopic navigation, yet existing deep learning (DL) methods often struggle with incomplete annotations and variability in CT acquisitions. We propose ASTRA-Net, a DL framework for segmenting both annotated and previously unlabeled airway branches from CT scans. ASTRA-Net integrates four key components: 1) auxiliary anatom...

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Assessment of gastric distensibility is crucial for understanding various gastrointestinal disorders, particularly those affecting gastric motility and reflux conditions. Gastric distensibility can be altered by pathological conditions such as linitis plastica or gastric mucosal inflammation, as seen in Helicobacter pylori infections. Although distensibility is evaluated during routine endoscopy, ...

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Accurate and automated reconstruction of cortical surfaces across the human lifespan is essential for studying brain development, aging, and the early diagnosis of neurological disorders. However, traditional neuroimaging pipelines require hours per subject, limiting scalability. Existing deep learning methods typically target narrow age ranges, struggling to generalize due to substantial age-rela...

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Surgical image segmentation is vital for various stages of surgical procedures, from preoperative planning to real-time navigation and postoperative assessment. Despite advances in deep learning, current surgical image segmentation methods remain limited. They primarily target instrument segmentation and show poor generalizability across different surgical settings. While the Segment Anything Mode...

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Cone-beam computed tomography (CBCT) is a critical 3D imaging technology in the medical field, while the high radiation exposure required for high-quality imaging raises significant concerns, particularly for vulnerable populations. Sparse-view reconstruction reduces radiation by using fewer X-ray projections while maintaining image quality, yet existing methods face challenges such as high comput...

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Most prior unsupervised domain adaptation approaches for medical image segmentation are narrowly tailored to either the source-accessible setting, where adaptation is guided by source-target alignment, or the source-free setting, which typically resorts to implicit adaptation mechanisms such as pseudo-labeling and network distillation. This substantial divergence in methodological designs between ...

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Longitudinal brain analysis is essential for understanding healthy aging and identifying pathological deviations. Longitudinal registration of sequential brain MRI underpins such analyses. However, existing methods are limited by reliance on densely sampled time series, a trade-off between accuracy and temporal smoothness, and an inability to prospectively forecast future brain states. To overcome...

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