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Abstract #2782

Memory-Efficient Image Reconstruction using Diffusion Models for Accelerated 3D Non-Cartesian UTE imaging

Jonas Petersen1,2, David Grodzki2, Thomas Küstner1, and Stefan Sommer3,4
1Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University of Tübingen, Tübingen, Germany, 2Research & Clinical Translation, Magnetic Resonance, Siemens Healthineers AG, Erlangen, Germany, 3Swiss Center for Musculoskeletal Imaging (SCMI), Balgrist Campus, Zurich, Switzerland, 4Advanced Clinical Imaging Technology (ACIT), Siemens Healthineers International AG, Zurich and Lausanne, Switzerland

Synopsis

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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