DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction

Zhang, Bo; Schlemper, Jo; Dey, Neel; Salehi, Seyed Sadegh Mohseni; Liu, Chi; Duncan, James S.; Sofka, Michal

doi:10.48550/arxiv.2201.10776

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…Similarly, SwinIR [53] based on residual Swin Transformer blocks [54] without any downsampling operation also showed significant advantages for image restoration tasks. Combining these networks with dual domain learning has also shown to yield superior medical image reconstruction performance [30], [44], [55]- [57]. Deploying these networks in our FTL could potentially further improve our denoising performance and will be an important direction of our future studies.…”

Section: Discussionmentioning

confidence: 97%

Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data

Zhou

Miao

Mirian

et al. 2023

IEEE Trans. Radiat. Plasma Med. Sci.

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Positron emission tomography (PET) with a reduced injection dose, i.e., low-dose PET, is an efficient way to reduce radiation dose. However, low-dose PET reconstruction suffers from a low signal-to-noise ratio (SNR), affecting diagnosis and other PET-related applications. Recently, deep learning-based PET denoising methods have demonstrated superior performance in generating high-quality reconstruction. However, these methods require a large amount of representative data for training, which can be difficult to collect and share due to medical data privacy regulations. Moreover, low-dose PET data at different institutions may use different low-dose protocols, leading to nonidentical data distribution. While previous federated learning (FL) algorithms enable multiinstitution collaborative training without the need of aggregating local data, it is challenging for previous methods to address the large domain shift caused by different low-dose PET settings, and the application of FL to PET is still underexplored. In this work, we propose a federated transfer learning (FTL) framework for low-dose PET denoising using heterogeneous low-dose data. Our experimental results on simulated multiinstitutional data demonstrate that our method can efficiently utilize heterogeneous low-dose data without compromising data privacy for achieving superior low-dose PET

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Section: Discussionmentioning

confidence: 97%

Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data

Zhou

Miao

Mirian

et al. 2023

IEEE Trans. Radiat. Plasma Med. Sci.

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“…Applications of the transformer to image denoising, motion deblurring, and defocus deblurring have been reported ( 65 ). In the area of image reconstruction alone, transformers have demonstrated superior performance compared to conventional convolution neural networks ( 66 , 67 ).…”

Section: Deep Learning For Accelerationmentioning

confidence: 99%

“…Adaptions of the reconstruction techniques described above can involve, as examples, training on both the image domain and k-space or getting the neural network to uncover the optimal undersampling pattern in k-space. The first method, also known as dual-domain reconstruction, makes the reasonable and logical assumption that providing both object and frequency domain data for training should improve reconstruction quality ( 67 , 68 ). While this is, indeed, the finding, it also comes to no surprise that the improvement in reconstruction quality is modest, since the information contained in k-space is identical, no more and no less, than the information contained in the image.…”

Section: Deep Learning For Accelerationmentioning

confidence: 99%

Emerging MRI techniques for molecular and functional phenotyping of the diseased heart

Cheng

2022

Front. Cardiovasc. Med.

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Recent advances in cardiac MRI (CMR) capabilities have truly transformed its potential for deep phenotyping of the diseased heart. Long known for its unparalleled soft tissue contrast and excellent depiction of three-dimensional (3D) structure, CMR now boasts a range of unique capabilities for probing disease at the tissue and molecular level. We can look beyond coronary vessel blockages and detect vessel disease not visible on a structural level. We can assess if early fibrotic tissue is being laid down in between viable cardiac muscle cells. We can measure deformation of the heart wall to determine early presentation of stiffening. We can even assess how cardiomyocytes are utilizing energy, where abnormalities are often precursors to overt structural and functional deficits. Finally, with artificial intelligence gaining traction due to the high computing power available today, deep learning has proven itself a viable contender with traditional acceleration techniques for real-time CMR. In this review, we will survey five key emerging MRI techniques that have the potential to transform the CMR clinic and permit early detection and intervention. The emerging areas are: (1) imaging microvascular dysfunction, (2) imaging fibrosis, (3) imaging strain, (4) imaging early metabolic changes, and (5) deep learning for acceleration. Through a concerted effort to develop and translate these areas into the CMR clinic, we are committing ourselves to actualizing early diagnostics for the most intractable heart disease phenotypes.

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“…Later, imaging model has been integrated into the learning pipeline, and model-based learning has achieved the state-of-the-art performance [1,9,13,18,19,21,22,23,26,27,30]. More recently, transformers have been integrated into the CNN-based networks for MRI undersampled reconstruction [4,6,16,29]. These networks can reconstruct MR images with a high fidelity.…”

Section: Introductionmentioning

confidence: 99%

Undersampled MRI Reconstruction with Side Information-Guided Normalisation

Liu¹,

Wang²,

Cheng³

et al. 2022

Preprint

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Magnetic resonance (MR) images exhibit various contrasts and appearances based on factors such as different acquisition protocols, views, manufacturers, scanning parameters, etc. This generally accessible appearance-related side information affects deep learning-based undersampled magnetic resonance imaging (MRI) reconstruction frameworks, but has been overlooked in the majority of current works. In this paper, we investigate the use of such side information as normalisation parameters in a convolutional neural network (CNN) to improve undersampled MRI reconstruction. Specifically, a Side Information-Guided Normalisation (SIGN) module, containing only few layers, is proposed to efficiently encode the side information and output the normalisation parameters. We examine the effectiveness of such a module on two popular reconstruction architectures, D5C5 and OUCR. The experimental results on both brain and knee images under various acceleration rates demonstrate that the proposed method improves on its corresponding baseline architectures with a significant margin.

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DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction

Cited by 4 publications

References 26 publications

Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data

Federated Transfer Learning for Low-Dose PET Denoising: A Pilot Study With Simulated Heterogeneous Data

Emerging MRI techniques for molecular and functional phenotyping of the diseased heart

Undersampled MRI Reconstruction with Side Information-Guided Normalisation

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