ArticleIEEE transactions on medical imaging2024
Zero-Shot Medical Image Translation via Frequency-Guided Diffusion Models.
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 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Conditional Diffusion Models for CT Image Synthesis from CBCT: A Systematic Review.Tomography (Ann Arbor, Mich.) · 2026Pooled it
- Efficient and rapid generation of high-quality CT images from CBCT using a flow matching model.Medical physics · 2026Article
- Fréchet radiomic distance (FRD): A versatile metric for comparing medical imaging datasets.Medical image analysis · 2026Article
- Temporally consistent low-light face video enhancement via video-to-video conditional diffusion.Scientific reports · 2026Article
- AI-powered segmentation and prognosis with missing MRI in pediatric brain tumors.NPJ precision oncology · 2026Article
- A Systematic Review of Diffusion Models for Medical Image-Based Diagnosis: Methods, Taxonomies, Clinical Integration, Explainability, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Article
- Multimodal medical image reconstruction and organ-wise disease classification using a hybrid deep learning-Kalman filtering framework.Frontiers in medicine · 2026Article
- Hierarchical attention and frequency fusion network for endometriotic lesion detection in laparoscopic images.Frontiers in medicine · 2026Article
- A universal medical imaging modality translation model in brain and head-and-neck radiotherapy.Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2026Article
- Probabilistic brain MR image transformation using generative models.Scientific reports · 2025Article
- Multiscale Segmentation-Guided Diffusion Model for CBCT-to-CT Synthesis.Life (Basel, Switzerland) · 2025Article
- Deep Learning for CT Synthesis in Radiotherapy.Bioengineering (Basel, Switzerland) · 2025Review
- DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model.IEEE transactions on medical imaging · 2025Article
- Denoising Diffusion Wavelet Models for Zero-shot Medical Image Translation.Knowledge-based systems · 2025Article
- InterpolAI: deep learning-based optical flow interpolation and restoration of biomedical images for improved 3D tissue mapping.Nature methods · 2025Article
- [Cross modal translation of magnetic resonance imaging and computed tomography images based on diffusion generative adversarial networks].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- A conditional point cloud diffusion model for deformable liver motion tracking via a single arbitrarily-angled x-ray projection.Physics in medicine and biology · 2025Article
- FDDM: Unsupervised Medical Image Translation with a Frequency-Decoupled Diffusion Model.Machine learning: science and technology · 2025Article
- CBCT-to-CT synthesis using a hybrid U-Net diffusion model based on transformers and information bottleneck theory.Scientific reports · 2025Article
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8 authors.
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Abstract
Recently, the diffusion model has emerged as a superior generative model that can produce high quality and realistic images. However, for medical image translation, the existing diffusion models are deficient in accurately retaining structural information since the structure details of source domain images are lost during the forward diffusion process and cannot be fully recovered through learned reverse diffusion, while the integrity of anatomical structures is extremely important in medical images. For instance, errors in image translation may distort, shift, or even remove structures and tumors, leading to incorrect diagnosis and inadequate treatments. Training and conditioning diffusion models using paired source and target images with matching anatomy can help. However, such paired data are very difficult and costly to obtain, and may also reduce the robustness of the developed model to out-of-distribution testing data. We propose a frequency-guided diffusion model (FGDM) that employs frequency-domain filters to guide the diffusion model for structure-preserving image translation. Based on its design, FGDM allows zero-shot learning, as it can be trained solely on the data from the target domain, and used directly for source-to-target domain translation without any exposure to the source-domain data during training. We evaluated it on three cone-beam CT (CBCT)-to-CT translation tasks for different anatomical sites, and a cross-institutional MR imaging translation task. FGDM outperformed the state-of-the-art methods (GAN-based, VAE-based, and diffusion-based) in metrics of Fréchet Inception Distance (FID), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), showing its significant advantages in zero-shot medical image translation.
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