Abstract #3715

The role of training on the robustness of domain-transform manifold learning

Danyal Bhutto^1,2, Bo Zhu², Jeremiah Zhe Liu^3,4, Stephen Cauley^2,5, Neha Koonjoo^2,5, Bruce R Rosen^2,5, and Matthew S Rosen^2,5,6

¹Biomedical Engineering, Boston University, Boston, MA, United States, ²Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ³Google, Mountain View, CA, United States, ⁴Biostatistics, Harvard University, Cambridge, MA, United States, ⁵Harvard Medical School, Boston, MA, United States, ⁶Physics, Harvard University, Boston, MA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionDomain-transform manifold learning is a trained reconstruction approach where care needs to be taken to appropriately represent the forward encoding model during training, including for example the numerical properties of the source sensor data, phase relationship of complex sensor data, and field-of-view to prevent artifacts arising in the reconstruction. Here, we study the role that the training corpus and the numeral properties of the training have on the performance of the reconstruction of MRI data and demonstrate reconstruction artifacts that result from inference on out-of-training-distribution data if the training data is not augmented sufficiently.

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