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Abstract #2174

Assessing the potential of 7T and high-performance gradients for high-resolution R2* mapping in deep gray matter

Hendrik Mattern1,2,3, Yi-Hang Tung1, Falk Lüsebrink1,2,4, and Oliver Speck1,2,3,5
1Biomedical Magnetic Resonance, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany, 2German Center for Neurodegenerative Diseases, Magdeburg, Germany, 3Center for Behavioral Brain Sciences (CBBS), Magdeburg, Germany, 4Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany, 5Leibniz Institute for Neurobiology, Magdeburg, Germany

Synopsis

Keywords: Relaxometry, Relaxometry, Ultra-high field; 7T; R2*; high resolution; deep gray mattern; subcortical

Motivation: While conventional 7T systems provide increased SNR, their gradient systems lack the performance to acquire high-resolution GRE data with very fast sampling/many echoes.

Goal(s): To leverage a 7T with high-performance gradients to acquire more echoes than on conventional 7T systems and assess the potential for high-resolution R2* mapping.

Approach: Multi-echo GRE data with varying number of echoes was acquired at a 7T Terra.X Impulse Edition and 7T Plus. Conventional logarithmic-linear and advanced stretched exponential fits were performed.

Results: Sampling more echoes enables fitting advanced models which yield lower R2* standard deviation and root-mean-squared-errors.

Impact: Combining 7T with high-performance gradients enables high spatial and temporal resolution multi-echo GRE data. Hence, R2* mapping can be performed with novel multi-parametric fits, enabling new avenues in biophysical modeling as well as signal denoising and decomposition algorithms.

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Keywords