6 citations · 10 across the 4 of their papers we have counts for
4 papers
Rician likelihood loss for quantitative MRI using self-supervised deep learning
Christopher S. Parker, Anna Schroder, Sean C. Epstein +3
Purpose: Previous quantitative MR imaging studies using self-supervised deep learning have reported biased parameter estimates at low SNR. Such systematic errors arise from the cho…
Patch-CNN: Training data-efficient deep learning for high-fidelity diffusion tensor estimation from minimal diffusion protocols
Tobias Goodwin-Allcock, Ting Gong, Robert Gray +2
We propose a new method, Patch-CNN, for diffusion tensor (DT) estimation from only six-direction diffusion weighted images (DWI). Deep learning-based methods have been recently pro…
Resolving quantitative MRI model degeneracy with machine learning via training data distribution design
Michele Guerreri, Sean Epstein, Hojjat Azadbakht +1
Quantitative MRI (qMRI) aims to map tissue properties non-invasively via models that relate these unknown quantities to measured MRI signals. Estimating these unknowns, which has t…
How can spherical CNNs benefit ML-based diffusion MRI parameter estimation?
Tobias Goodwin-Allcock, Jason McEwen, Robert Gray +2
This paper demonstrates spherical convolutional neural networks (S-CNN) offer distinct advantages over conventional fully-connected networks (FCN) at estimating scalar parameters o…