CLIMB generates controllable longitudinal brain MRI images from baseline scans using a Mamba-based latent diffusion model and Gaussian-aligned autoencoder, reporting SSIM 0.9433 on the ADNI dataset of 6306 scans.
author Kabas, B
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Contrast-X benchmark and FlowMI model enable synthesis of contrast-enhanced images from arbitrary non-contrast modality inputs using multi-modal flow matching.
AA-ViT synthesizes contrast-enhanced brain MRI from pre-contrast T1/T2/FLAIR using edge-aware dense encoding and frequency-guided losses, achieving higher PSNR/SSIM than four baselines on BraTS2021.
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CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder
CLIMB generates controllable longitudinal brain MRI images from baseline scans using a Mamba-based latent diffusion model and Gaussian-aligned autoencoder, reporting SSIM 0.9433 on the ADNI dataset of 6306 scans.
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Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching
Contrast-X benchmark and FlowMI model enable synthesis of contrast-enhanced images from arbitrary non-contrast modality inputs using multi-modal flow matching.
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AA-ViT: Anatomically Aware Vision Transformer with Structural and Frequency Guidance for Contrast Enhanced Brain MRI Synthesis
AA-ViT synthesizes contrast-enhanced brain MRI from pre-contrast T1/T2/FLAIR using edge-aware dense encoding and frequency-guided losses, achieving higher PSNR/SSIM than four baselines on BraTS2021.
- A Plug-and-Play Method for Guided Multi-contrast MRI Reconstruction based on Content/Style Modeling