3D CT
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Related Articles from SNS
Tracing the Oracle: Improving Diffusion Timestep Scheduling for 3D CT Reconstruction
arXiv:2606.06236v1 Announce Type: new Abstract: Pretrained diffusion models demonstrate impressive potential in solving highly ill-posed 3D computed tomography (CT) inverse problems, while the inference process suffers from significant computational overhead. Furthermore, existing uniform timestep schedules fail to capture the non-uniform evolution of the reverse conditional diffusion stochastic differential equation, thereby introducing substantial truncation errors. To overcome this...
Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation
Announce Type: new Abstract: Modern 3D medical vision-language models (VLMs) can generate fluent radiology-style text while exhibit critically low pathology detection and output diversity, collapsing to generic templates that under-report rare yet critical findings. We identify this failure mode as Template Collapse. This failure stems from the unique constraints of 3D medical imaging, e.g., limited data, severe label imbalance, and weak signals from volumetric encoders.
Multi-planar 2D-U-Net Segmentation of 3D-CT Abdominal Organs augmented by Spatial Occurrence Maps
arXiv:2606.07717v1 Announce Type: cross Abstract: This work proposes a lightweight 2D-U-Net-based framework for segmenting five abdominal organs in large field-of-view 3D CT scans. The method combines coarse-to-fine segmentation, predictions from multiple anatomical planes, and additional fuzzy 3D spatial maps that provide anatomical location cues to improve segmentation accuracy.
Foundation VAEs for 3D CT Reconstruction, Augmentation, and Generation
Announce Type: new Abstract: Variational autoencoders (VAEs) compress high resolution CT volumes into compact latents while preserving clinically relevant structure. However, training CT-specific VAEs from scratch or heavily fine-tuning them incurs substantial computational and engineering cost, and often degrades under heterogeneous scanners, protocols, and diseases. This paper makes a progressive stride toward training-free medical VAEs by leveraging a critical observation: a single...
MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts
arXiv:2606.00967v3 Announce Type: replace Abstract: Generative models for volumetric medical images have found many applications in medical imaging, ranging from data augmentation to serving as priors for inverse problems. For these applications, generating high-resolution 3D images with strong controllability is essential but remains highly challenging. Existing approaches typically control generation either through radiology reports used as text prompts or through full image segmentation.
MedSyn2: Flexible Control of 3D CT Generation via Text and Semantically-Defined Segmentation Prompts
Announce Type: replace Abstract: Generative models for volumetric medical images have found many applications in medical imaging, ranging from data augmentation to serving as priors for inverse problems. For these applications, generating high-resolution 3D images with strong controllability is essential but remains highly challenging. Existing approaches typically control generation either through radiology reports used as text prompts or through full image segmentation.
Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes
arXiv:2606.04365v1 Announce Type: new Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attributes. We curated 2,619 CT volumes from 788 patients at one academic medical center, with multi-granularity side- and per-lesion...
Astra: a generalizable report generation foundation model for 3D computed tomography
Announce Type: new Abstract: CT interpretation requires radiologists to review hundreds of volumetric slices per examination, making reporting time-consuming and highly expertise-dependent. Automated CT report generation offers a promising route to improving clinical efficiency, yet the field still lacks a generalizable CT report generation foundation model that supports multi-region reporting and remains robust across external real-world cohorts. Intrinsic inconsistencies in reporting style...
Pompeii victim ID'd as a likely doctor
Archaeologists used a combination of advanced CT scans and 3D digital reconstruction to identify one of the Pompeii victims who died in 79 CE during the eruption of Mount Vesuvius as most likely having been a Roman doctor, according to an announcement by the Pompeii Archaeological Park. As previously reported, the eruption of Mount Vesuvius released thermal energy roughly equivalent to 100,000 times the atomic bombs dropped on Hiroshima and Nagasaki at the end of World War II, spewing molten...
Astra: a generalizable report generation foundation model for 3D computed tomography
arXiv:2605.31437v2 Announce Type: replace Abstract: CT interpretation requires radiologists to review hundreds of volumetric slices per examination, making reporting time-consuming and highly expertise-dependent. Automated CT report generation offers a promising route to improving clinical efficiency, yet the field still lacks a generalizable CT report generation foundation model that supports multi-region reporting and remains robust across external real-world cohorts. Intrinsic...