Improving MRI Resolution: A Cycle Consistent Generative Adversarial Network-Based Approach for 3T to 7T Translation

<p dir="ltr">Brain magnetic resonance imaging (MRI) offers intricate soft tissue contrasts that are essential for diagnosing diseases and conducting neuroscience research. At 7 Tesla (7T) magnetic field intensity, MRI enables increased resolution, enhanced tissue contrast, and improv...

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Bibliographic Details
Main Author:	Zakaria Shams Siam (22048001) (author)
Other Authors:	Rubyat Tasnuva Hasan (22048004) (author), Moajjem Hossain Chowdhury (21842429) (author), Md. Shaheenur Islam Sumon (22048007) (author), Mamun Bin Ibne Reaz (641768) (author), Sawal Hamid Bin Md Ali (22048010) (author), Adam Mushtak (22048013) (author), Israa Al-Hashimi (18131374) (author), Sohaib Bassam Zoghoul (22048016) (author), Muhammad E. H. Chowdhury (14150526) (author)
Published:	2024
Subjects:	Biomedical and clinical sciences Neurosciences Health sciences Health services and systems Information and computing sciences Artificial intelligence Cycle consistent generative adversarial network image-to-image translation magnetic resonance imaging paired dataset T1-weighted MRI Magnetic resonance imaging Training Image processing Spatial resolution Image quality Generative adversarial networks Superresolution Data models
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Improving MRI Resolution: A Cycle Consistent Generative Adversarial Network-Based Approach for 3T to 7T Translation

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