Skip to main content
. Author manuscript; available in PMC: 2022 Apr 14.
Published in final edited form as: Nat Methods. 2021 Oct 14;18(11):1401–1408. doi: 10.1038/s41592-021-01285-2

Fig. 5 |. Applying DeepInterpolation to fMRI removes thermal noise.

Fig. 5 |

(A) Structure of the DeepInterpolation encoder-decoder network for fMRI data. Instead of predicting a whole brain volume at once, the network reconstructs a local 7×7×7 cube of voxels. (B) tSNR for 10,000 voxels randomly distributed in the brain volume in raw data and after DeepInterpolation. (C) Exemplar reconstruction of a single fMRI volume. First row is a coronal section while the second row is a sagittal section through a human brain. In the second column, the temporal mean of 3D scan was removed to better illustrate the presence of thermal noise in the raw data. The local denoising network was processed throughout the whole 3D scan for denoising. The impact of DeepInterpolation on thermal noise is illustrated in the 3rd column. The 4th column shows the residual of the denoising process. Scale bar is 5 cm.