A sparse voxel-space diffusion method with structure-adaptive modulation achieves up to 10x training speedup and state-of-the-art results for 3D medical image denoising and super-resolution.
Brain MRI super-resolution using 3D generative adversarial networks
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abstract
In this work we propose an adversarial learning approach to generate high resolution MRI scans from low resolution images. The architecture, based on the SRGAN model, adopts 3D convolutions to exploit volumetric information. For the discriminator, the adversarial loss uses least squares in order to stabilize the training. For the generator, the loss function is a combination of a least squares adversarial loss and a content term based on mean square error and image gradients in order to improve the quality of the generated images. We explore different solutions for the upsampling phase. We present promising results that improve classical interpolation, showing the potential of the approach for 3D medical imaging super-resolution. Source code available at https://github.com/imatge-upc/3D-GAN-superresolution
verdicts
UNVERDICTED 2representative citing papers
Enhanced GAN with RRDG generator and patch discriminator for 3D brain MRI super-resolution achieves SOTA metrics and introduces anatomical fidelity evaluation via pre-trained network.
citing papers explorer
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Structure-Adaptive Sparse Diffusion in Voxel Space for 3D Medical Image Enhancement
A sparse voxel-space diffusion method with structure-adaptive modulation achieves up to 10x training speedup and state-of-the-art results for 3D medical image denoising and super-resolution.
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Enhanced generative adversarial network for 3D brain MRI super-resolution
Enhanced GAN with RRDG generator and patch discriminator for 3D brain MRI super-resolution achieves SOTA metrics and introduces anatomical fidelity evaluation via pre-trained network.