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Abstract #0305

Accelerated respiratory-resolved 4D-MRI with separable spatio-temporal neural networks

Maarten Terpstra1,2, Matteo Maspero1,2, Joost JC Verhoeff1, and Cornelis A.T. van den Berg1,2
1Deparment of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands, 2Computational Imaging Group for MR Diagnostics & Therapy, University Medical Center Utrecht, Utrecht, Netherlands


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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