Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
Motivation: 3D medical images are often acquired with anisotropic volumes to reduce scan times. Super-resolution reconstruction to recover features in the low-resolution direction would improve visualisation and clinical accuracy.
Goal(s): To train an unpaired super-resolution network for anisotropic 3D MRI and CT images.
Approach: We propose that it is possible to leverage disjoint patches from the high-resolution (in-plane) data to increase the resolution of the low-resolution (through-plane) slices.
Results: We demonstrate that our proposed modified CycleGAN architecture, performs better than the standard CycleGAN for super-resolution of MRI and CT data.
Impact: Unpaired super-resolution reconstruction of anisotropic 3D medical images, enables accurate recovery of features in the low-resolution direction of MRI and CT data.
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