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.
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DOI：
10.1002/mp.16591
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发表时间：
2023-07
期刊：
影响因子：
3.8
通讯作者：
Zimeng Li;Sa Xiao;Cheng Wang;Haidong Li;Xiuchao Zhao;Qian Zhou;Qiuchen Rao;Yuan Fang;Junshuai Xie;Lei Shi;Chaohui Ye;Xin Zhou
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文献类型：
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作者：
Zimeng Li;Sa Xiao;Cheng Wang;Haidong Li;Xiuchao Zhao;Qian Zhou;Qiuchen Rao;Yuan Fang;Junshuai Xie;Lei Shi;Chaohui Ye;Xin Zhou
背景超极化（HP）气体MRI能够清晰显示肺结构和功能。临床相关的生物标志物，如来自这种模式的通气缺陷百分比（VDP）可以量化肺通气功能。然而，长的成像时间导致图像质量下降并引起患者的不适。虽然通过欠采样k空间数据来加速MRI是可用的，但是在高加速因子下肺图像的准确重建和分割是相当具有挑战性的。目的通过有效利用不同任务中的互补信息，同时提高高加速因子下肺气体MRI图像的重建和分割性能。方法提出一种互补增强型网络，以欠采样图像为输入，输出重建图像和肺通气缺损的分割结果。所提出的网络包括重建分支和分割分支。为了有效地利用互补信息，在所提出的网络中设计了几种策略。首先，两个分支都采用编码器-解码器架构，并且它们的编码器被设计为共享卷积权重以促进知识传递。其次，设计的特征选择块有区别地将共享特征馈送到两个分支的解码器中，该解码器可以自适应地为每个任务选择合适的特征。第三，分割分支结合从重建图像获得的肺掩模，以提高分割结果的准确性。最后，通过定制的损失函数优化了所提出的网络，该损失函数有效地结合和平衡了这两项任务，以实现互惠互利。结果在肺HP 129 MRI数据集（包括43名健康受试者和42名患者）上的实验结果表明，所提出的网络在高加速因子（4，5和6）下的性能优于最先进的方法。该网络的峰值信噪比（PSNR）、结构相似性（SSIM）和Dice得分分别提高到30.89、0.875和0.892。此外，从建议的网络获得的VDP具有良好的相关性，从完全采样的图像（r = 0.984）。在最高加速因子为6时，与单任务模型相比，该网络的PSNR、SSIM和Dice得分分别提高了7.79%、5.39%和9.52%。结论该方法有效地提高了重建和分割性能在高加速因子高达6。它有助于快速和高质量的肺部成像和分割，并为肺部疾病的临床诊断提供有价值的支持。
BACKGROUND Hyperpolarized (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. PURPOSE To 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. METHODS A 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. RESULTS Experimental results on the pulmonary HP 129 Xe 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. CONCLUSION The 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.