Four-dimensional (4D) respiratory-resolved imaging is crucial for managing respiratory motion in radiotherapy, enabling irradiation of highly mobile tumours. However, acquiring high-quality 4D-MRI requires long acquisition (typically ≥5 minutes) and long iterative reconstructions, limiting treatment efficiency. Recently, deep learning has been proposed to accelerate undersampled MRI reconstruction. However, it has not been established whether deep learning may reconstruct high-quality 4D-MRI from accelerated acquisitions. This work proposes a small deep learning model that exploits the spatio-temporal information present in 4D-MRI, allowing to split the reconstruction into two separated branches, enabling high-quality retrospectively-accelerated 4D-MRI acquired in ~60 seconds and reconstructed in 16 seconds.
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