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Abstract #3827

Reconstruction of Super-Resolution T1 Maps Using Efficient Residual Denoising Diffusion Probabilistic Models at 7T Field

Mojtaba Safari1, Zach Eidex1, Shansong Wang1, Chih-Wei Chang1, Richard L.J. Liu1, Hui Mao2, Erik H. Middlebrooks3, and Xiaofeng Yang1
1Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, United States, 2Department of Radiology and Image Science and Winship Cancer Institute, Emory University, Atlanta, GA, United States, 3Department of Radiology, Mayo Clinic, Jacksonville, FL, United States

Synopsis

Keywords: AI Diffusion Models, AI/ML Image Reconstruction

Motivation: Achieving high-resolution T1 mapping requires extended scan time due to substantially prolonged tissue T1 times at ultra-field, leaving the data quality susceptible to patient motion and other interferences.

Goal(s): Develop an efficient DDPM DL model using that produces high-resolution T1 maps from low-resolution inputs with minimal sampling steps, enhancing clinical feasibility.

Approach: The proposed method combines residual learning with a novel DDPM architecture, reducing the required sampling steps from 1000 to four. This model was trained and tested on institutional 7T MRI data.

Results: The model significantly reduced inference time by over 240 times, providing high-resolution T1 maps with improved structural detail.

Impact: The proposed model can reduce the scan time required for generating high-resolution T1 maps within a clinically acceptable time. Its capacity to produce high-quality brain images with reduced artifacts may improve diagnosis and accelerate advancements in neuroimaging research.

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Keywords