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

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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