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Abstract #3842

Applications of generative adversarial networks for super-resolution of cerebrovascular 4D Flow MRI

Oliver Welin Odeback1, Edward Ferdian2,3, Alistair A. Young3,4, Jonas Schollenberger5, C. Alberto Figueroa6, Tobias Granberg1,7, Alexander Fyrdahl1,7, and David Marlevi1,8
1Karolinska Institute, Stockholm, Sweden, 2Telkom University, Bandung, Indonesia, 3University of Auckland, Auckland, New Zealand, 4King's College, London, United Kingdom, 5University of California, San Francisco, CA, United States, 6University of Michigan, Ann Arbor, MI, United States, 7Karolinska University Hospital, Stockholm, Sweden, 8Massachusetts Institute of Technology, Cambridge, MA, United States

Synopsis

Keywords: AI/ML Image Reconstruction, Velocity & Flow

Motivation: 4D Flow MRI provides full-field mapping of blood flow. However, challenges remain concerning resolution and noise, particularly in the intracranial space. While convolutional neural networks (CNNs) have demonstrated potential to provide super-resolved 4D Flow MRI, enhancing near-wall flows remain difficult.

Goal(s): To evaluate various Generative Adversarial Network (GAN) setups for enhanced super-resolution and denoising of 4D Flow MRI data, focusing on vessel-boundary performance.

Approach: We trained and validated multiple GAN setups on anatomically and sequence-realistic synthetic 4D Flow MRI data.

Results: Results indicate that Wasserstein GAN outperforms established CNN approaches for near-wall velocity enhancement, suggesting improvements in boundary voxel recovery with generative networks.

Impact: This study highlights the potential of generative adversarial networks to enhance super-resolution in 4D Flow MRI, enabling more accurate intracranial flow assessments near vessel walls.

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Keywords