Investigating the use of publicly available natural videos to learn
  Dynamic MR image reconstruction
Olivier Jaubert Michele Pascale Javier Montalt-Tordera Julius Akesson Ruta Virsinskaite Daniel Knight Simon Arridge Jennifer Steeden Vivek Muthurangu
Purpose: To develop and assess a deep learning (DL) pipeline to learn dynamic
MR image reconstruction from publicly available natural videos (Inter4K).
  Materials and Methods: Learning was performed for a range of DL architectures
(VarNet, 3D UNet, FastDVDNet) and corresponding sampling patterns (Cartesian,
radial, spiral) either from true multi-coil cardiac MR data (N=692) or from
pseudo-MR data simulated from Inter4K natural videos (N=692). Real-time
undersampled dynamic MR images were reconstructed using DL networks trained
with cardiac data and natural videos, and compressed sensing (CS). Differences
were assessed in simulations (N=104 datasets) in terms of MSE, PSNR, and SSIM
and prospectively for cardiac (short axis, four chambers, N=20) and speech
(N=10) data in terms of subjective image quality ranking, SNR and Edge
sharpness. Friedman Chi Square tests with post-hoc Nemenyi analysis were
performed to assess statistical significance.
  Results: For all simulation metrics, DL networks trained with cardiac data
outperformed DL networks trained with natural videos, which outperformed CS
(p<0.05). However, in prospective experiments DL reconstructions using both
training datasets were ranked similarly (and higher than CS) and presented no
statistical differences in SNR and Edge Sharpness for most conditions.
Additionally, high SSIM was measured between the DL methods with cardiac data
and natural videos (SSIM>0.85).
  Conclusion: The developed pipeline enabled learning dynamic MR reconstruction
from natural videos preserving DL reconstruction advantages such as high
quality fast and ultra-fast reconstructions while overcoming some limitations
(data scarcity or sharing). The natural video dataset, code and pre-trained
networks are made readily available on github.
  Key Words: real-time; dynamic MRI; deep learning; image reconstruction;
machine learning;