A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction

Guo, Di; Zeng, Gushan; Fu, Hao; Wang, Zi; Yang, Yanchao; Qu, Xiaobo

doi:10.1016/j.jmr.2022.107354

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…proposed a deep cascade network for undersampled T2W image reconstruction, in which each regularization unit received additional supplementary information from fully sampled T1W images. Similarly, on the basis of the unfolded structure, Liu et al (2021b) employed a deep dilated convolution block in the regularization unit to extract contextual information efficiently Guo et al (2023). presented a joint reconstruction network based on the pFISTA algorithm, which iteratively solved the inverse problem using the shareable sparsity features between different contrast images.…”

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confidence: 99%

IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction

Yan,

Yang,

Jiao

et al. 2024

Phys. Med. Biol.

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Objective. Multi-contrast magnetic resonance imaging (MC MRI) can obtain more comprehensive anatomical information of the same scanning object but requires a longer acquisition time than single-contrast MRI. To accelerate MC MRI speed, recent studies only collect partial k-space data of one modality (target contrast) to reconstruct the remaining non-sampled measurements using a deep learning-based model with the assistance of another fully sampled modality (reference contrast). However, MC MRI reconstruction mainly performs the image domain reconstruction with conventional CNN-based structures by full supervision. It ignores the prior information from reference contrast images in other sparse domains and requires fully sampled target contrast data. In addition, conventional CNN-based networks are difficult to build a high-quality non-local dependency. Approach. In the paper, we propose an Image-Wavelet domain ConvNeXt-based network (IWNeXt) for self-supervised MC MRI reconstruction. Firstly, INeXt and WNeXt based on ConvNeXt reconstruct undersampled target contrast data in the image domain and refine the initial reconstructed result in the wavelet domain respectively. To generate more tissue details in the refinement stage, reference contrast wavelet sub-bands are used as additional supplementary information for wavelet domain reconstruction. Then, we design a novel attention ConvNeXt block for feature extraction, which can capture the non-local information of the MC image. Finally, the cross-domain consistency loss is designed for self-supervised learning. Especially, the frequency domain consistency loss deduces the non-sampled k-space data, while the image and wavelet domain consistency loss retain more high-frequency information in the final reconstruction. Main results. Numerous experiments are conducted on the HCP dataset and the M4Raw dataset with different sampling trajectories. Compared with DuDoRNet, our model improves by 1.651dB in the peak signal-to-noise ratio. Significance. IWNeXt is a potential cross-domain method that can enhance the accuracy of MC MRI reconstruction and reduce reliance on fully sampled target contrast images.

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confidence: 99%

IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction

Yan,

Yang,

Jiao

et al. 2024

Phys. Med. Biol.

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Frequency Domain-based Matching and Integration Network for Multi-contrast MRI Reconstruction

Wang,

Zhang,

Xiong

et al. 2023

2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)

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Deep Learning-Assisted Automatic Diagnosis of Anterior Cruciate Ligament Tear in Knee Magnetic Resonance Images

Wang,

Wu,

et al. 2024

Tomography

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Anterior cruciate ligament (ACL) tears are prevalent knee injures, particularly among active individuals. Accurate and timely diagnosis is essential for determining the optimal treatment strategy and assessing patient prognosis. Various previous studies have demonstrated the successful application of deep learning techniques in the field of medical image analysis. This study aimed to develop a deep learning model for detecting ACL tears in knee magnetic resonance Imaging (MRI) to enhance diagnostic accuracy and efficiency. The proposed model consists of three main modules: a Dual-Scale Data Augmentation module (DDA) to enrich the training data on both the spatial and layer scales; a selective group attention module (SG) to capture relationships across the layer, channel, and space scales; and a fusion module to explore the inter-relationships among various perspectives to achieve the final classification. To ensure a fair comparison, the study utilized a public dataset from MRNet, comprising knee MRI scans from 1250 exams, with a focus on three distinct views: axial, coronal, and sagittal. The experimental results demonstrate the superior performance of the proposed model, termed SGNET, in ACL tear detection compared with other comparison models, achieving an accuracy of 0.9250, a sensitivity of 0.9259, a specificity of 0.9242, and an AUC of 0.9747.

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A Joint Group Sparsity-based deep learning for multi-contrast MRI reconstruction

Cited by 3 publications

References 30 publications

IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction

IWNeXt: an image-wavelet domain ConvNeXt-based network for self-supervised multi-contrast MRI reconstruction

Frequency Domain-based Matching and Integration Network for Multi-contrast MRI Reconstruction

Deep Learning-Assisted Automatic Diagnosis of Anterior Cruciate Ligament Tear in Knee Magnetic Resonance Images

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