Efficient structurally-strengthened generative adversarial network for MRI reconstruction

Zhou, Wenzhong; Du, Heng; Mei, Wenbo; Fang, Liping

doi:10.1016/j.neucom.2020.09.008

Cited by 16 publications

(13 citation statements)

References 22 publications

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“…For the reconstruction task, the target objective function can be defined as,

\begin{equation}\mathop {{\mathrm{argmin}}}\limits_{\theta _e^r{\mathrm{, }}\theta _d^r} {L_r}({\bf{x}},H(f_{2{\mathrm{D}}}^{ - 1}({{\bf{y}}_u});\theta _e^r,\theta _d^r))\end{equation}

where

\theta _e^r

and

\theta _d^r

denote the parameters of the Recon Encoder and Recon Decoder, respectively. The reconstruction loss L r is defined as a sum of L 1 and L 2 loss functions, which not only prevents over‐smoothing but ensures robust convergence 32 …”

Section: Methodsmentioning

confidence: 99%

Complementation‐reinforced network for integrated reconstruction and segmentation of pulmonary gas MRI with high acceleration

Xiao

Wang

et al. 2023

Medical Physics

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BackgroundHyperpolarized (HP) gas MRI enables the clear visualization of lung structure and function. Clinically relevant biomarkers, such as ventilated defect percentage (VDP) derived from this modality can quantify lung ventilation function. However, long imaging time leads to image quality degradation and causes discomfort to the patients. Although accelerating MRI by undersampling k‐space data is available, accurate reconstruction and segmentation of lung images are quite challenging at high acceleration factors.PurposeTo simultaneously improve the performance of reconstruction and segmentation of pulmonary gas MRI at high acceleration factors by effectively utilizing the complementary information in different tasks.MethodsA complementation‐reinforced network is proposed, which takes the undersampled images as input and outputs both the reconstructed images and the segmentation results of lung ventilation defects. The proposed network comprises a reconstruction branch and a segmentation branch. To effectively exploit the complementary information, several strategies are designed in the proposed network. Firstly, both branches adopt the encoder‐decoder architecture, and their encoders are designed to share convolutional weights for facilitating knowledge transfer. Secondly, a designed feature‐selecting block discriminately feeds shared features into decoders of both branches, which can adaptively pick suitable features for each task. Thirdly, the segmentation branch incorporates the lung mask obtained from the reconstructed images to enhance the accuracy of the segmentation results. Lastly, the proposed network is optimized by a tailored loss function that efficiently combines and balances these two tasks, in order to achieve mutual benefits.ResultsExperimental results on the pulmonary HP 129Xe MRI dataset (including 43 healthy subjects and 42 patients) show that the proposed network outperforms state‐of‐the‐art methods at high acceleration factors (4, 5, and 6). The peak signal‐to‐noise ratio (PSNR), structural similarity (SSIM), and Dice score of the proposed network are enhanced to 30.89, 0.875, and 0.892, respectively. Additionally, the VDP obtained from the proposed network has good correlations with that obtained from fully sampled images (r = 0.984). At the highest acceleration factor of 6, the proposed network promotes PSNR, SSIM, and Dice score by 7.79%, 5.39%, and 9.52%, respectively, in comparison to the single‐task models.ConclusionThe proposed method effectively enhances the reconstruction and segmentation performance at high acceleration factors up to 6. It facilitates fast and high‐quality lung imaging and segmentation, and provides valuable support in the clinical diagnosis of lung diseases.

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“…For the reconstruction task, the target objective function can be defined as,

\begin{equation}\mathop {{\mathrm{argmin}}}\limits_{\theta _e^r{\mathrm{, }}\theta _d^r} {L_r}({\bf{x}},H(f_{2{\mathrm{D}}}^{ - 1}({{\bf{y}}_u});\theta _e^r,\theta _d^r))\end{equation}

where

\theta _e^r

and

\theta _d^r

Section: Methodsmentioning

confidence: 99%

Complementation‐reinforced network for integrated reconstruction and segmentation of pulmonary gas MRI with high acceleration

Xiao

Wang

et al. 2023

Medical Physics

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“…In this paper, we simplify the generator architecture in Ref. 22, and then use the simplified network architecture as the backbone architecture of the generator in the proposed SOGAN. As shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…As described in Ref. 22 , in the encoder block, the conv _ i and conv _ o perform the convolution operations with the strides being 2 and 1, respectively. Thus, conv _ i also performs a downsampling operation.…”

Section: Methodsmentioning

confidence: 99%

Spatial orthogonal attention generative adversarial network for MRI reconstruction

Zhou

Mei

et al. 2020

Medical Physics

Self Cite

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Purpose: Recent studies have witnessed that self-attention modules can better solve the vision understanding problems by capturing long-range dependencies. However, there are very few works designing a lightweight self-attention module to improve the quality of MRI reconstruction. Furthermore, it can be observed that several important self-attention modules (e.g., the non-local block) cause high computational complexity and need a huge number of GPU memory when the size of the input feature is large. The purpose of this study is to design a lightweight yet effective spatial orthogonal attention module (SOAM) to capture long-range dependencies, and develop a novel spatial orthogonal attention generative adversarial network, termed as SOGAN, to achieve more accurate MRI reconstruction. Methods: We first develop a lightweight SOAM, which can generate two small attention maps to effectively aggregate the long-range contextual information in vertical and horizontal directions, respectively. Then, we embed the proposed SOAMs into the concatenated convolutional autoencoders to form the generator of the proposed SOGAN. Results: The experimental results demonstrate that the proposed SOAMs improve the quality of the reconstructed MR images effectively by capturing long-range dependencies. Besides, compared with state-of-the-art deep learning-based CS-MRI methods, the proposed SOGAN reconstructs MR images more accurately, but with fewer model parameters. Conclusions: The proposed SOAM is a lightweight yet effective self-attention module to capture long-range dependencies, thus, can improve the quality of MRI reconstruction to a large extent. Besides, with the help of SOAMs, the proposed SOGAN outperforms the state-of-the-art deep learning-based CS-MRI methods.

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“…Besides the reviewed works above, deep learning methods consisting of single P image can be seen in [92,[142][143][144][145][146][147][148][149][150][151][152][153][154][155][156][157], etc. Other unrolling form deep learning methods can be seen in [158][159][160][161][162][163][164][165][166][167][168][169][170], etc.…”

Section: Parallel Imagingmentioning

confidence: 99%

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

Xie

2022

Electronics

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Compressed sensing (CS) and its medical applications are active areas of research. In this paper, we review recent works using deep learning method to solve CS problem for images or medical imaging reconstruction including computed tomography (CT), magnetic resonance imaging (MRI) and positron-emission tomography (PET). We propose a novel framework to unify traditional iterative algorithms and deep learning approaches. In short, we define two projection operators toward image prior and data consistency, respectively, and any reconstruction algorithm can be decomposed to the two parts. Though deep learning methods can be divided into several categories, they all satisfies the framework. We built the relationship between different reconstruction methods of deep learning, and connect them to traditional methods through the proposed framework. It also indicates that the key to solve CS problem and its medical applications is how to depict the image prior. Based on the framework, we analyze the current deep learning methods and point out some important directions of research in the future.

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Efficient structurally-strengthened generative adversarial network for MRI reconstruction

Cited by 16 publications

References 22 publications

Complementation‐reinforced network for integrated reconstruction and segmentation of pulmonary gas MRI with high acceleration

Complementation‐reinforced network for integrated reconstruction and segmentation of pulmonary gas MRI with high acceleration

Spatial orthogonal attention generative adversarial network for MRI reconstruction

A Review of Deep Learning Methods for Compressed Sensing Image Reconstruction and Its Medical Applications

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