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Abstract #1201

7T MRI-Synthesized Iron and Myelin Histology by Deep Learning

Sutatip Pittayapong1, Simon Hametner2,3, Beata Bachrata1,4, Wolfgang Bogner5,6, Romana Höftberger2,3, and Günther Grabner1
1Department of Medical Engineering, Carinthia University of Applied Sciences, Klagenfurt, Austria, 2Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, Vienna, Austria, 3Comprehensive Center for Clinical Neurosciences & Mental Health, Medical University of Vienna, Vienna, Austria, 4Karl Landsteiner Institute for Clinical Molecular MR in Musculoskeletal Imaging, Medical University of Vienna, Vienna, Austria, 5High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria, 6Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria

Synopsis

Keywords: Diagnosis/Prediction, Machine Learning/Artificial Intelligence, Brain Histological Synthesis

Motivation: Advanced MRI techniques aim to evaluate the existence of iron and myelin but still face limitations, particularly in white matter regions. Histological staining remains the gold standard due to its precision in assessing myelin and iron levels.

Goal(s): To synthesize histological images of iron and myelin staining from MRI.

Approach: Use deep learning to create histology-like staining images from multi-contrast MRI.

Results: Deep learning successfully generated high-resolution iron and myelin staining images.

Impact: Our research shows that deep learning can synthesize myelin and iron stainings from multi-contrast MRI. This technique enhances understanding of brain development, function, and diseases, promising advances in medical imaging and histology.

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