3D Brain MRI Reconstruction based on 2D Super-Resolution Technology

Zhang, Hongtao; Shinomiya, Yuki; Yoshida, Shigeaki

doi:10.1109/smc42975.2020.9283444

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…Our previous work involved repairing images according to the principle of linear interpolation, using effective pixel value interpolation instead of null values to obtain a new high-resolution MRI image. Nevertheless, our method still has a small amount of noise, and the visual effect is average [ 37 ]. Therefore, we compared our proposed nESRGAN with the previous work.…”

Section: Resultsmentioning

confidence: 99%

3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution

Zhang

Shinomiya

Yoshida

2021

Sensors

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The diagnosis of brain pathologies usually involves imaging to analyze the condition of the brain. Magnetic resonance imaging (MRI) technology is widely used in brain disorder diagnosis. The image quality of MRI depends on the magnetostatic field strength and scanning time. Scanners with lower field strengths have the disadvantages of a low resolution and high imaging cost, and scanning takes a long time. The traditional super-resolution reconstruction method based on MRI generally states an optimization problem in terms of prior information. It solves the problem using an iterative approach with a large time cost. Many methods based on deep learning have emerged to replace traditional methods. MRI super-resolution technology based on deep learning can effectively improve MRI resolution through a three-dimensional convolutional neural network; however, the training costs are relatively high. In this paper, we propose the use of two-dimensional super-resolution technology for the super-resolution reconstruction of MRI images. In the first reconstruction, we choose a scale factor of 2 and simulate half the volume of MRI slices as input. We utilize a receiving field block enhanced super-resolution generative adversarial network (RFB-ESRGAN), which is superior to other super-resolution technologies in terms of texture and frequency information. We then rebuild the super-resolution reconstructed slices in the MRI. In the second reconstruction, the image after the first reconstruction is composed of only half of the slices, and there are still missing values. In our previous work, we adopted the traditional interpolation method, and there was still a gap in the visual effect of the reconstructed images. Therefore, we propose a noise-based super-resolution network (nESRGAN). The noise addition to the network can provide additional texture restoration possibilities. We use nESRGAN to further restore MRI resolution and high-frequency information. Finally, we achieve the 3D reconstruction of brain MRI images through two super-resolution reconstructions. Our proposed method is superior to 3D super-resolution technology based on deep learning in terms of perception range and image quality evaluation standards.

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Section: Resultsmentioning

confidence: 99%

3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution

Zhang

Shinomiya

Yoshida

2021

Sensors

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“…where C 3 is a positive constant as well. Combining the captured similarities, we get the similarity measure denoted in Equation (23).…”

Section: Objective Evaluationmentioning

confidence: 99%

Improving Structural MRI Preprocessing with Hybrid Transformer GANs

Grigas,

Maskeliūnas,

Damaševičius

2023

Life

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Magnetic resonance imaging (MRI) is a technique that is widely used in practice to evaluate any pathologies in the human body. One of the areas of interest is the human brain. Naturally, MR images are low-resolution and contain noise due to signal interference, the patient’s body’s radio-frequency emissions and smaller Tesla coil counts in the machinery. There is a need to solve this problem, as MR tomographs that have the capability of capturing high-resolution images are extremely expensive and the length of the procedure to capture such images increases by the order of magnitude. Vision transformers have lately shown state-of-the-art results in super-resolution tasks; therefore, we decided to evaluate whether we can employ them for structural MRI super-resolution tasks. A literature review showed that similar methods do not focus on perceptual image quality because upscaled images are often blurry and are subjectively of poor quality. Knowing this, we propose a methodology called HR-MRI-GAN, which is a hybrid transformer generative adversarial network capable of increasing resolution and removing noise from 2D T1w MRI slice images. Experiments show that our method quantitatively outperforms other SOTA methods in terms of perceptual image quality and is capable of subjectively generalizing to unseen data. During the experiments, we additionally identified that the visual saliency-induced index metric is not applicable to MRI perceptual quality assessment and that general-purpose denoising networks are effective when removing noise from MR images.

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“…Then, the three-dimensional slices are repaired through interpolation to obtain new brain MRI data. The VGG16 [51] is employed before activation to restore the features, solve over-brightness in SRGAN, and improve performance [52]. The work of [53] is also based on ESR-GAN, where two neural networks complete the super-resolution task.…”

Section: Image Super-resolutionmentioning

confidence: 99%

A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

et al. 2022

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With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect abnormalities in brain images without an extensive manual feature extraction process. Generative adversarial network (GAN)-synthesized images have many applications in this field besides augmentation, such as image translation, registration, super-resolution, denoising, motion correction, segmentation, reconstruction, and contrast enhancement. The existing literature was reviewed systematically to understand the role of GAN-synthesized dummy images in brain disease diagnosis. Web of Science and Scopus databases were extensively searched to find relevant studies from the last 6 years to write this systematic literature review (SLR). Predefined inclusion and exclusion criteria helped in filtering the search results. Data extraction is based on related research questions (RQ). This SLR identifies various loss functions used in the above applications and software to process brain MRIs. A comparative study of existing evaluation metrics for GAN-synthesized images helps choose the proper metric for an application. GAN-synthesized images will have a crucial role in the clinical sector in the coming years, and this paper gives a baseline for other researchers in the field.

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3D Brain MRI Reconstruction based on 2D Super-Resolution Technology

Cited by 9 publications

References 9 publications

3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution

3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution

Improving Structural MRI Preprocessing with Hybrid Transformer GANs

A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI

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