Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images

• URL: http://arxiv.org/abs/2212.13068v1
• Date: Mon, 26 Dec 2022 09:52:12 GMT
• Title: Transformer and GAN Based Super-Resolution Reconstruction Network for Medical Images
• Authors: Weizhi Du and Harvery Tian
• Abstract summary: Super-resolution reconstruction in medical imaging has become more popular (MRI) In this paper, we offer a deep learning-based strategy for reconstructing medical images from low resolutions utilizing Transformer and Generative Adversarial Networks (T-GAN) The integrated system can extract more precise texture information and focus more on important locations through global image matching.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Because of the necessity to obtain high-quality images with minimal radiation doses, such as in low-field magnetic resonance imaging, super-resolution reconstruction in medical imaging has become more popular (MRI). However, due to the complexity and high aesthetic requirements of medical imaging, image super-resolution reconstruction remains a difficult challenge. In this paper, we offer a deep learning-based strategy for reconstructing medical images from low resolutions utilizing Transformer and Generative Adversarial Networks (T-GAN). The integrated system can extract more precise texture information and focus more on important locations through global image matching after successfully inserting Transformer into the generative adversarial network for picture reconstruction. Furthermore, we weighted the combination of content loss, adversarial loss, and adversarial feature loss as the final multi-task loss function during the training of our proposed model T-GAN. In comparison to established measures like PSNR and SSIM, our suggested T-GAN achieves optimal performance and recovers more texture features in super-resolution reconstruction of MRI scanned images of the knees and belly.

Related papers

• Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction [68.80715727288514]
  We show how to unfold an iterative MGDUN algorithm into a novel model-guided deep unfolding network by taking the MRI observation matrix. In this paper, we propose a novel Model-Guided interpretable Deep Unfolding Network (MGDUN) for medical image SR reconstruction.
  arXiv  Detail & Related papers  (2022-09-15T03:58:30Z)
• Cross-Modality High-Frequency Transformer for MR Image Super-Resolution [100.50972513285598]
  We build an early effort to build a Transformer-based MR image super-resolution framework. We consider two-fold domain priors including the high-frequency structure prior and the inter-modality context prior. We establish a novel Transformer architecture, called Cross-modality high-frequency Transformer (Cohf-T), to introduce such priors into super-resolving the low-resolution images.
  arXiv  Detail & Related papers  (2022-03-29T07:56:55Z)
• Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution [55.52779466954026]
  Multi-contrast super-resolution (SR) reconstruction is promising to yield SR images with higher quality. Existing methods lack effective mechanisms to match and fuse these features for better reconstruction. We propose a novel network to address these problems by developing a set of innovative Transformer-empowered multi-scale contextual matching and aggregation techniques.
  arXiv  Detail & Related papers  (2022-03-26T01:42:59Z)
• HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction [38.0542877099235]
  HUMUS-Net is a hybrid architecture that combines the beneficial implicit bias and efficiency of convolutions with the power of Transformer blocks in an unrolled and multi-scale network. Our network establishes new state of the art on the largest publicly available MRI dataset, the fastMRI dataset.
  arXiv  Detail & Related papers  (2022-03-15T19:26:29Z)
• Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform [5.416279158834623]
  Loss of corresponding image resolution degrades the overall performance of medical image diagnosis. Deep learning based single image super resolution (SISR) algorithms has revolutionized the overall diagnosis framework. This work proposes generative adversarial network (GAN) with deep multi-attention modules to learn high-frequency information from low-frequency data.
  arXiv  Detail & Related papers  (2021-10-22T10:13:46Z)
• Multi-modal Aggregation Network for Fast MR Imaging [85.25000133194762]
  We propose a novel Multi-modal Aggregation Network, named MANet, which is capable of discovering complementary representations from a fully sampled auxiliary modality. In our MANet, the representations from the fully sampled auxiliary and undersampled target modalities are learned independently through a specific network. Our MANet follows a hybrid domain learning framework, which allows it to simultaneously recover the frequency signal in the $k$-space domain.
  arXiv  Detail & Related papers  (2021-10-15T13:16:59Z)
• Multi-Modal MRI Reconstruction with Spatial Alignment Network [51.74078260367654]
  In clinical practice, magnetic resonance imaging (MRI) with multiple contrasts is usually acquired in a single study. Recent researches demonstrate that, considering the redundancy between different contrasts or modalities, a target MRI modality under-sampled in the k-space can be better reconstructed with the helps from a fully-sampled sequence. In this paper, we integrate the spatial alignment network with reconstruction, to improve the quality of the reconstructed target modality.
  arXiv  Detail & Related papers  (2021-08-12T08:46:35Z)
• Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation [60.663499381212425]
  We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique. In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
  arXiv  Detail & Related papers  (2021-04-05T13:05:22Z)
• Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networks [0.0]
  We propose a novel attention-based deep learning framework to provide high-quality MRI reconstruction. We incorporate large-field contextual feature integration and attention selection in a generative adversarial network (GAN) framework.
  arXiv  Detail & Related papers  (2021-03-13T09:58:32Z)
• Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance images [1.978587235008588]
  We propose a reference-based, unpaired multi-contrast texture-transfer strategy for deep learning based in-plane and across-plane MRI super-resolution. We apply our scheme in different super-resolution architectures, observing improvement in PSNR and SSIM for 4x super-resolution in most of the cases.
  arXiv  Detail & Related papers  (2021-02-10T14:12:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.