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Channel attention generative adversarial network for super-resolution of glioma magnetic resonance image
Zhaoyang Song1, Defu Qiu2, Xiaoqiang Zhao1
1College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China; Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China; National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China.
Background And Objective:
Glioma is the most common primary craniocerebral tumor caused by the cancelation of glial cells in the brain and spinal cord, with a high incidence and cure rate. Magnetic resonance imaging (MRI) is a common technique for detecting and analyzing brain tumors. Due to improper hardware and operation, the obtained brain MRI images are low-resolution, making it difficult to detect and grade gliomas accurately. However, super-resolution reconstruction technology can improve the clarity of MRI images and help experts accurately detect and grade glioma.
Methods:
We propose a glioma magnetic resonance image super-resolution reconstruction method based on channel attention generative adversarial network (CGAN). First, we replace the base block of SRGAN with a residual dense block based on the channel attention mechanism. Second, we adopt a relative average discriminator to replace the discriminator in standard GAN. Finally, we add the mean squared error loss to the training, consisting of the mean squared error loss, the L1 norm loss, and the generator's adversarial loss to form the generator loss function.
Results:
On the Set5, Set14, Urban100, and glioma datasets, compared with the state-of-the-art algorithms, our proposed CGAN method has improved peak signal-to-noise ratio and structural similarity, and the reconstructed glioma images are more precise than other algorithms.
Conclusion:
The experimental results show that our CGAN method has apparent improvements in objective evaluation indicators and subjective visual effects, indicating its effectiveness and superiority.
Insights
We developed a channel attention generative adversarial network (CGAN) to enhance low-resolution brain MRI scans for improved glioma detection. This method reconstructs clearer images, aiding in accurate glioma diagnosis and grading.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Glioma is a common brain tumor.
- Magnetic Resonance Imaging (MRI) is crucial for glioma detection.
- Low-resolution MRI hinders accurate glioma diagnosis.
Purpose of the Study:
- To improve the resolution of brain MRI images for better glioma detection and grading.
- To introduce a novel super-resolution reconstruction method for medical imaging.
Main Methods:
- A channel attention generative adversarial network (CGAN) was developed for super-resolution reconstruction.
- The method utilizes residual dense blocks with channel attention and a relative average discriminator.
- A combined loss function including MSE, L1 norm, and adversarial loss was employed.
Main Results:
- The CGAN method significantly improved peak signal-to-noise ratio and structural similarity compared to existing algorithms.
- Reconstructed glioma images exhibited higher precision.
- Objective and subjective evaluations demonstrated superior performance.
Conclusions:
- The proposed CGAN method effectively enhances MRI image quality for glioma analysis.
- This technique offers a superior approach for accurate glioma detection and grading.
- The results highlight the potential of AI in improving diagnostic accuracy for brain tumors.
