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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