2023
DOI: 10.1088/1361-6560/acdc80
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A unified hybrid transformer for joint MRI sequences super-resolution and missing data imputation

Abstract: Objective: High-resolution (HR) multi-modal magnetic resonance imaging (MRI) is crucial in clinical practice for accurate diagnosis and treatment. However, challenges such as budget constraints, potential contrast agent deposition, and image corruption often limit the acquisition of multiple sequences from a single patient. Therefore, the development of novel methods to reconstruct under-sampled images and synthesize missing sequences is crucial for clinical and research applications. Approach: In this paper, … Show more

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Cited by 7 publications
(5 citation statements)
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“…The synthesis of diagnostic images from noncontrast sequences was performed on brain MR images (Wang et al , 2023. A related work performed a preliminary study by simulating postcontrast images from precontrast images (Chung et al 2023).…”
Section: Related Workmentioning
confidence: 99%
“…The synthesis of diagnostic images from noncontrast sequences was performed on brain MR images (Wang et al , 2023. A related work performed a preliminary study by simulating postcontrast images from precontrast images (Chung et al 2023).…”
Section: Related Workmentioning
confidence: 99%
“…Li et al [91] McMRSR 2D fastMRI [92] MRI MRI superresolution RMSE; SSIM; PSNR Multi-scale contextual matching Wang et al [93] SIFormer 2D IXI; BraTS [94] MRI MRI superresolution DICE; SSIM; PSNR Super-resolution with sequential MRI Forigua et al [95] SuperFormer 3D HCP [89] MRI MRI superresolution NRMSE; SSIM; PSNR 3D ViT Wu et al [96] KTMR 2D HCP [89] MRI Image denoising SSIM; PSNR Reconstruction in the context of k-space consistency Huang et al [97] ST-GAN 2D HCP [89] MRI Image denoising SSIM; PSNR; FID GAN based Swin Transformer Wang et al [98] TED-Net 2D NIH-AAPM [99] CT Image denoising MSE; SSIM; PSNR LDCT denoising Luthra et al [100] Eformer 2D NIH-AAPM [99] CT Image denoising RMSE; SSIM; PSNR LDCT denoising with residual learning Zhang et al [101] TransCT 2D NIH-AAPM [99] CT Image denoising MSE; SSIM; PSNR LDCT denoising with high & low frequency decomposition Guo et al [102] ReconFormer 2D fastMRI [92] MRI Fast MR imaging MSE; SSIM; PSNR Multi-scale learning & lightweight Zhou et al [103] DSFormer 2D IXI MRI Fast MR imaging MSE; SSIM; PSNR Self-supervised for multi-contrast MRI reconstruction Lyu et al [104] DuDoCAF 2D FastMRI [92] MRI Fast MR imaging MSE; SSIM; PSNR Recurrent Transformer for fast multicontrast MR imaging Korkmaz et al [105] SLATER 3D IXI; fastMRI [92] MRI Image synthesis MSE; SSIM; PSNR Zero-shot learned adversarial Transformers Huang et al [106] SDAUT 2D Calgary Campinas [107] improving diagnostic accuracy and treatment planning. In recent years, generative adversarial learning (GAN) has been combined with Transformers to carry out tasks such as image generation and reconstruction, thus improving modality transfer capabilities.…”
Section: Niqe; Brisque Domain-distance Adapted Downsamplingmentioning
confidence: 99%
“…Data collection and annotation difficulties: The collection and annotation of complex biomedical data presents significant challenges in neuroscientific research and clinical practice. While techniques such as data synthesis [93] and harmonization [82] offer potential solutions for enhancing data and adapting across different domains, they can be limited by the inherent complexities and variability of biomedical data. Transfer learning [127] and zero-shot learning, [105] despite their advantages in utilizing existing datasets, often face difficulties due to discrepancies in data distribution and small sample sizes in specialized clinical domains.…”
Section: Limitations and Future Research Directionsmentioning
confidence: 99%
See 1 more Smart Citation
“…Deep supervision of feature fusion improves model performance (Qu et al 2022). Therefore, inspired by Lee et al (2015), Wang et al (2023), we incorporated a deeply-supervised mechanism between the HFFD and the LFFD to improve the reconstruction of the texture details and low-frequency information of the target contrast image. The structure of this mechanism is shown in the fourth stage in the gray part of figure 2.…”
Section: Deeply-supervised Mechanism and Loss Functionmentioning
confidence: 99%