Abstract #0391

A Deep Learning Method for Sensitivity Enhancement in Deuterium Metabolic Imaging (DMI)

Siyuan Dong¹, Henk M. De Feyter², Monique A. Thomas², Robin A. de Graaf³, and James S. Duncan⁴

¹Department of Electrical Engineering, Yale University, New Haven, CT, United States, ²Department of Radiology and Biomedical Imaging, Yale University, School of Medicine, New Haven, CT, United States, ³Department of Radiology and Biomedical Imaging, Department of Biomedical Engineering, Yale University, School of Medicine, New Haven, CT, United States, ⁴Department of Radiology & Biomedical Imaging, Department of Electrical Engineering, Department of Statistics & Data Science, Yale University, New Haven, CT, United States

Deuterium Metabolic Imaging (DMI) is a novel approach providing 3D metabolic data from both animal models and human subjects. DMI relies on ²H MRSI in combination with administration of ²H-labeled substrates. Common to all MRI and MRSI methods, DMI's resolution is ultimately limited by the achievable SNR. This work proposes a data-driven method using a deep convolutional autoencoder to improve the SNR and increase the spatial resolution of DMI. The method was tested with simulated, phantom and in vivo experiments at various SNR levels to demonstrate its capability and precision for metabolic mapping using noisy DMI data.

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