ArticleIEEE transactions on medical imaging2024
MedSyn: Text-Guided Anatomy-Aware Synthesis of High-Fidelity 3-D CT Images.
Article in IEEE transactions on medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Generalist foundation models from a multimodal dataset for 3D computed tomography.Nature biomedical engineering · 2026Article
- A Systematic Review on Synthetic Medical Images Generation-Recent Trends and Future Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- Integrating Anatomical Priors Into a Causal Diffusion Model.IEEE transactions on medical imaging · 2026Article
- Text-Driven Tumor Synthesis.IEEE transactions on medical imaging · 2026Article
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- Structure-aware 3D diffusion generation for kidney MRI via mask-guided noise scheduling and topology-prior constraints.Scientific reports · 2026Article
- On the transfer learning behavior of domain-specific vision-language models in screening mammography.Journal of biomedical informatics · 2026Article
- PSB-DSN: a two-stage unsupervised denoising enhancement network for OCT images based on pixel shuffle and blind spot masking.Biomedical optics express · 2026Article
- Cycle Diffusion Model for Counterfactual Image Generation.Predictive Intelligence in Medicine. PRIME (Workshop) · 2026Article
- WASABI: A Metric for Evaluating Morphometric Plausibility of Synthetic Brain MRIs.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Artificial intelligence in biomedical visualization and education: a narrative review.Frontiers in public health · 2026Review
- Opportunities and challenges in lung cancer care in the era of large language models and vision language models.Translational lung cancer research · 2025Review
- Application of Artificial Intelligence Generated Content in Medical Examinations.Advances in medical education and practice · 2025Article
- Multimodal artificial intelligence models for radiology.BJR artificial intelligence · 2025Review
- Privacy enhancing and generalizable deep learning with synthetic data for mediastinal neoplasm diagnosis.NPJ digital medicine · 2024Article
- Evaluating the Quality of Brain MRI Generators.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
This paper introduces an innovative methodology for producing high-quality 3D lung CT images guided by textual information. While diffusion-based generative models are increasingly used in medical imaging, current state-of-the-art approaches are limited to low-resolution outputs and underutilize radiology reports' abundant information. The radiology reports can enhance the generation process by providing additional guidance and offering fine-grained control over the synthesis of images. Nevertheless, expanding text-guided generation to high-resolution 3D images poses significant memory and anatomical detail-preserving challenges. Addressing the memory issue, we introduce a hierarchical scheme that uses a modified UNet architecture. We start by synthesizing low-resolution images conditioned on the text, serving as a foundation for subsequent generators for complete volumetric data. To ensure the anatomical plausibility of the generated samples, we provide further guidance by generating vascular, airway, and lobular segmentation masks in conjunction with the CT images. The model demonstrates the capability to use textual input and segmentation tasks to generate synthesized images. Algorithmic comparative assessments and blind evaluations conducted by 10 board-certified radiologists indicate that our approach exhibits superior performance compared to the most advanced models based on GAN and diffusion techniques, especially in accurately retaining crucial anatomical features such as fissure lines and airways. This innovation introduces novel possibilities. This study focuses on two main objectives: (1) the development of a method for creating images based on textual prompts and anatomical components, and (2) the capability to generate new images conditioning on anatomical elements. The advancements in image generation can be applied to enhance numerous downstream tasks.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.