Abstract #3050

Dynamic 2D fitting of 3D deuterium metabolic imaging data acquired in human brain at 7T

Sabina Frese¹, William T Clarke², Saad Jbabdi², Bernhard Strasser¹, Wolfgang Bogner^1,3, Viola Bader¹, Lukas Hingerl¹, Stanislav Motyka^1,3, Martin Krssak⁴, Siegfried Trattnig^1,5, Thomas Scherer⁴, Rupert Lanzenberger⁶, and Fabian Niess¹

¹High Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria, ²Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, ³Christian Doppler Laboratory for MR Imaging Biomarkers (BIOMAK), Vienna, Austria, ⁴Department of Medicine III, Division of Endocrinology and Metabolism, Medical University of Vienna, Vienna, Austria, ⁵Institute for Clinical Molecular MRI, Karl Landsteiner Society, St. Pölten, Austria, ⁶Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria

Synopsis

Keywords: Deuterium, Deuterium, Dynamic 2D fitting; Deuterium metabolic imaging; Brain; 7T

Motivation: Dynamic deuterium metabolic imaging (DMI) data are currently evaluated by first fitting the spectral and then the temporal domain (1D), leading to great fit uncertainty. This might be improved by fitting both domains simultaneously (2D).

Goal(s): To compare dynamic 2D-fitting of time-resolved 3D-DMI data of the human brain and compare its performance to conventional 1D-fitting.

Approach: Simulated and in vivo DMI data were fitted using 1D- and 2D-fitting algorithms and results were compared in terms of precision and accuracy.

Results: 2D-fitting yielded higher precision and accuracy than 1D-fitting for simulated DMI data. For in vivo data, both fitting approaches yielded similar results.

Impact: Improving fitting accuracy to estimate the underlying metabolic kinetics from dynamic deuterium metabolic imaging (DMI) data is crucial, while establishing DMI towards clinical application. 2D-fitting approaches (simultaneous spectral and temporal fitting) could potentially improve overall robustness of the methodology.

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