Aniket Tolpadi1,2, Francesco Caliva1, Misung Han1, Valentina Pedoia1, and Sharmila Majumdar1
1Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, 2Bioengineering, University of California, Berkeley, Berkeley, CA, United States
1Radiology and Biomedical Imaging, UCSF, San Francisco, CA, United States, 2Bioengineering, University of California, Berkeley, Berkeley, CA, United States
A recurrent encoder-decoder network predicts T2 maps from undersampled T2 echos, allowing for eightfold reduction in quantitative MRI acquisition time while showing strong correlation to ground truth, low prediction error rates, fidelity to T2 values, and retainment of textures.
Figure 2: Performance of 3 and 4-echo pipelines in predicting fully sampled T2 maps in an example of holdout set. Pearson’s r is calculated for each map with respect to ground truth. (a) Predicted 4-echo pipeline maps showed fidelity to ground truth up to R=6, and for (b) the 3-echo pipeline, up to R=3. Up to these acceleration factors, maps reconstitute T2 values and preserve NP/AF delineation, and can thus quantitatively assess disc health and reflect early degenerative changes.
Figure 1: Recurrent encoder-decoder architecture used to predict T2 maps from spatially undersampled MAPSS T2 echos. Initial recurrent network includes connections between processing streams of each echo to exploit temporal correlations. Subsequent encoder-decoder network exploits spatial correlation and predicts final T2 map. The network could be configured for any number of input T2 echos, but the number of filters throughout the encoder-decoder network portion are presented as for 4-echo inputs.
