Keywords: AI Diffusion Models, AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, MSK
Motivation: 3D radial sampling is crucial in UTE sequences for effectively capturing short-T2 tissue such as bone. Non-Cartesian reconstructions pose difficulties due to high memory demands and computational times.
Goal(s): This work aims to develop a diffusion-model to achieve high-quality images from undersampled k-space data within reasonable reconstruction time.
Approach: Our approach integrates a memory-efficient neural-network, employing Heun’s efficient sampling and conjugate gradient-based data consistency.
Results: The proposed reconstruction yields high SSIM and PSNR values with good generalizability across acceleration factors and body regions, demonstrating its effectiveness for 3D non-Cartesian reconstruction allowing shorter scan times.
Impact: We propose a memory-efficient diffusion model for reconstructing accelerated 3D radial UTE acquisitions, enabling high-quality, and reliable reconstructions while reducing the reconstruction time of common deep-learning methods. The model generalizes well across body parts, supporting various applications and acceleration factors.
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