  Organ motion poses an unresolved challenge in image-guided interventions. In
the pursuit of solving this problem, the research field of time-resolved
volumetric magnetic resonance imaging (4D MRI) has evolved. However, current
techniques are unsuitable for most interventional settings because they lack
sufficient temporal and/or spatial resolution or have long acquisition times.
In this work, we propose a novel approach for real-time, high-resolution 4D MRI
with large fields of view for MR-guided interventions. To this end, we trained
a convolutional neural network (CNN) end-to-end to predict a 3D liver MRI that
correctly predicts the liver's respiratory state from a live 2D navigator MRI
of a subject. Our method can be used in two ways: First, it can reconstruct
near real-time 4D MRI with high quality and high resolution (209x128x128 matrix
size with isotropic 1.8mm voxel size and 0.6s/volume) given a dynamic
interventional 2D navigator slice for guidance during an intervention. Second,
it can be used for retrospective 4D reconstruction with a temporal resolution
of below 0.2s/volume for motion analysis and use in radiation therapy. We
report a mean target registration error (TRE) of 1.19 $\pm$0.74mm, which is
below voxel size. We compare our results with a state-of-the-art retrospective
4D MRI reconstruction. Visual evaluation shows comparable quality. We show that
small training sizes with short acquisition times down to 2min can already
achieve promising results and 24min are sufficient for high quality results.
Because our method can be readily combined with earlier methods, acquisition
time can be further decreased while also limiting quality loss. We show that an
end-to-end, deep learning formulation is highly promising for 4D MRI
reconstruction.
