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arXiv · 1905.00985

Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction

Itzik Malkiel·Sangtae Ahn·Valentina Taviani·Anne Menini·Lior Wolf·Christopher J. Hardy
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Abstract

Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning. However, these techniques sometimes struggle to reconstruct sharp images that preserve fine detail while maintaining a natural appearance. In this work, we enhance the image quality by using a Conditional Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique that stabilizes the training and minimizes the degree of artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.

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Itzik Malkiel, Sangtae Ahn, Valentina Taviani, Anne Menini, Lior Wolf, Christopher J. Hardy. 2019-05-02. Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction. https://arxiv.org/abs/1905.00985

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