Typical magnetic resonance imaging (MRI) usually shows distinct anisotropic spatial resolution in imaging plane and slice-select direction. Image super-resolution (SR) techniques are widely used as an alternative method to isotropic MRI reconstruction. In this work, we propose to reconstruct isotropic magnetic resonance (MR) volumes via 3D convolutional neural network in an end-to-end manner. 3D SRCNN is utilized to preliminarily validate the idea and it produces quantitative and qualitative results significantly superior to traditional methods, such as Cube-Avg and NLM methods.
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