Abstract #0521

OG-DNN: Orientation-Grasp Deep Neural Network for Quantitative Susceptibility Mapping

Kuo-Wei Lai^1,2, Jeremias Sulam¹, Manisha Aggarwal³, Peter van Zijl^2,3, and Xu Li^2,3

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States, ²F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ³Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, MD, United States

We designed a method called Orientation-Grasp Deep Neural Network (OG-DNN) for Quantitative Susceptibility Mapping (QSM). OG-DNN has dynamically adaptive convolutional filters that adjust themselves according to the input B₀ orientation in the subject frame of reference. Our experimental results demonstrate that OG-DNN can reconstruct high-quality and consistent susceptibility maps from MR phase data acquired at different head orientations with respect to B₀ within a consistent subject frame of reference. OG-DNN is expected to provide improved flexibility in practice and may potentially facilitate the development of deep learning-based Susceptibility Tensor Imaging (STI) reconstructions.

This abstract and the presentation materials are available to members only; a login is required.

Join Here