磁共振成像(MRI)技术具有非侵入、无放射性的特点，在临床疾病诊断中具有独特的优势，但是肺部空腔的特殊结构使传统质子MRI 无法对其直接成像．自旋交换光抽运(SEOP)方法可以使惰性气体原子的极化度增强4 个量级以上，从而使肺部的气体MRI 成为可能．该文介绍了超极化惰性气体肺部MRI 的最新研究进展，包括超极化气体磁共振相关参数的测量方法、肺部通气结构成像、肺部气体交换功能成像，同时比较了常用于肺部MRI气体的优点和缺点．

The ongoing pandemic of coronavirus disease 2019 (COVID-19) has been a great burden for the healthcare system in many countries because of its high transmissibility, severity, and fatality. Chest radiography and computed tomography (CT) play a vital role in the diagnosis, detection of complications, and prognostication of COVID-19. Additionally, magnetic resonance imaging (MRI), especially multi-nuclei MRI, is another important imaging technique for disease diagnosis because of its good soft tissue contrast and the ability to conduct structural and functional imaging, which has also been used to evaluate COVID-19-related organ injuries in previous studies. Herein, we briefly reviewed the recent research on multi-nuclei MRI for evaluating injuries caused by COVID-19 and the clinical H MRI techniques and their applications for assessing injuries in lungs, brain, and heart. Moreover, the emerging hyperpolarized Xe gas MRI and its applications in the evaluation of pulmonary structures and functional abnormalities caused by COVID-19 were also reviewed.© 2021 The Authors.

This study analyzes the signal-to-noise ratio (SNR) in magnetic resonance imaging. The factors that determine the SNR are derived starting from basic principles. The SNR, for a given object, is shown to be proportional to the voxel volume and the square root of the acquisition time. The noise generated by the body is derived using a cylindrical model and is shown to be proportional to the square of the radius and the square root of the length.

Magnetic Resonance (MR) images are affected by random noise which limits the accuracy of any quantitative measurements from the data. In the present work, a recently proposed filter for random noise removal is analyzed and adapted to reduce this noise in MR magnitude images. This parametric filter, named Non-Local Means (NLM), is highly dependent on the setting of its parameters. The aim of this paper is to find the optimal parameter selection for MR magnitude image denoising. For this purpose, experiments have been conducted to find the optimum parameters for different noise levels. Besides, the filter has been adapted to fit with specific characteristics of the noise in MR image magnitude images (i.e. Rician noise). From the results over synthetic and real images we can conclude that this filter can be successfully used for automatic MR denoising.

This paper proposes two new methods for the three-dimensional denoising of magnetic resonance images that exploit the sparseness and self-similarity properties of the images. The proposed methods are based on a three-dimensional moving-window discrete cosine transform hard thresholding and a three-dimensional rotationally invariant version of the well-known nonlocal means filter. The proposed approaches were compared with related state-of-the-art methods and produced very competitive results. Both methods run in less than a minute, making them usable in most clinical and research settings.Copyright © 2011 Elsevier B.V. All rights reserved.

Magnetic resonance imaging (MRI) is a crucial technique for both scientific research and clinical diagnosis. However, noise generated during MR data acquisition degrades image quality, particularly in hyperpolarized (HP) gas MRI. While deep learning (DL) methods have shown promise for MR image denoising, most of them fail to adequately utilize the long-range information which is important to improve denoising performance. Furthermore, the sample size of paired noisy and noise-free MR images also limits denoising performance.To develop an effective DL method that enhances denoising performance and reduces the requirement of paired MR images by utilizing the long-range information and pretraining.In this work, a hybrid Transformer-convolutional neural network (CNN) network (HTC-net) and a self-supervised pretraining strategy are proposed, which effectively enhance the denoising performance. In HTC-net, a CNN branch is exploited to extract the local features. Then a Transformer-CNN branch with two parallel encoders is designed to capture the long-range information. Within this branch, a residual fusion block (RFB) with a residual feature processing module and a feature fusion module is proposed to aggregate features at different resolutions extracted by two parallel encoders. After that, HTC-net exploits the comprehensive features from the CNN branch and the Transformer-CNN branch to accurately predict noise-free MR images through a reconstruction module. To further enhance the performance on limited MRI datasets, a self-supervised pretraining strategy is proposed. This strategy employs self-supervised denoising to equip the HTC-net with denoising capabilities during pretraining, and then the pre-trained parameters are transferred to facilitate subsequent supervised training.Experimental results on the pulmonary HP Xe MRI dataset (1059 images) and IXI dataset (5000 images) all demonstrate the proposed method outperforms the state-of-the-art methods, exhibiting superior preservation of edges and structures. Quantitatively, on the pulmonary HP Xe MRI dataset, the proposed method outperforms the state-of-the-art methods by 0.254-0.597 dB in PSNR and 0.007-0.013 in SSIM. On the IXI dataset, the proposed method outperforms the state-of-the-art methods by 0.3-0.927 dB in PSNR and 0.003-0.016 in SSIM.The proposed method can effectively enhance the quality of MR images, which helps improve the diagnosis accuracy in clinical.© 2024 American Association of Physicists in Medicine.

To test if the proposed deep learning based denoising method denoising convolutional neural networks (DnCNN) with residual learning and multi-channel strategy can denoise three dimensional MR images with Rician noise robustly.Multi-channel DnCNN (MCDnCNN) method with two training strategies was developed to denoise MR images with and without a specific noise level, respectively. To evaluate our method, three datasets from two public data sources of IXI dataset and Brainweb, including T1 weighted MR images acquired at 1.5 and 3 T as well as MR images simulated with a widely used MR simulator, were randomly selected and artificially added with different noise levels ranging from 1 to 15%. For comparison, four other state-of-the-art denoising methods were also tested using these datasets.In terms of the highest peak-signal-to-noise-ratio and global of structure similarity index, our proposed MCDnCNN model for a specific noise level showed the most robust denoising performance in all three datasets. Next to that, our general noise-applicable model also performed better than the rest four methods in two datasets. Furthermore, our training model showed good general applicability.Our proposed MCDnCNN model has been demonstrated to robustly denoise three dimensional MR images with Rician noise.

因其较高的核自旋极化度所提供的探测灵敏度，超极化129Xe气体已被成功应用于动物和人体磁共振成像（MRI）.但是，在超极化129Xe的收集-升华过程中，多种因素会导致129Xe核自旋弛豫，进而限制其应用范围.本文通过理论模型分析和实验测量，验证了温度、磁场、螺旋冷阱材质等对冷冻恢复过程中超极化129Xe弛豫的影响；同时，测量了自动收集-升华装置的稳定性.研究结果表明，升华方式和冷阱材质对129Xe极化度损耗的影响显著；自制收集-升华装置的自动化程度高、长时间稳定，129Xe极化度的恢复率可达到85.6% ± 4.7%.本研究非常有助于提升超极化129Xe在动物和人体MRI中的使用效率.

本研究旨在探索129Xe通气功能MRI与肺部病灶类型之间的关联．我们对143例患者进行129Xe通气功能MRI和肺部CT扫描，并分析了肺叶水平的通气功能与肺部病灶类型之间的关系．结果显示，大部分肺叶（74.6%）129Xe肺通气功能MRI和CT结果一致．通气功能损伤与CT病灶类型相关，且混合疾病和肺气肿/肺大疱更易引起肺通气功能障碍．此外，我们的研究显示不同肺部病灶类型会呈现出不同的通气模式．即129Xe通气功能MRI和肺部CT能够提供互补信息．本研究的初步结果将为肺部病灶引起的通气功能损伤评估提供数据支持，为临床影像学诊断提供更多的参考依据，有助于优化肺部疾病的诊断和治疗．

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.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.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.Experimental results on the pulmonary HP 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.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.© 2023 American Association of Physicists in Medicine.

To investigate the effect of beam angles and field number on functionally-guided intensity modulated radiotherapy (IMRT) normal lung avoidance treatment plans that incorporate hyperpolarised helium-3 magnetic resonance imaging (He MRI) ventilation data. Eight non-small cell lung cancer patients had pre-treatment He MRI that was registered to inspiration breath-hold radiotherapy planning computed tomography. IMRT plans that minimised the volume of total lung receiving  ⩾20 Gy (V) were compared with plans that minimised He MRI defined functional lung receiving  ⩾20 Gy (fV). Coplanar IMRT plans using 5-field manually optimised beam angles and 9-field equidistant plans were also evaluated. For each pair of plans, the Wilcoxon signed ranks test was used to compare fV and the percentage of planning target volume (PTV) receiving 90% of the prescription dose (PTV). Incorporation of He MRI led to median reductions in fV of 1.3% (range: 0.2-9.3%; p  =  0.04) and 0.2% (range: 0 to 4.1%; p  =  0.012) for 5- and 9-field arrangements, respectively. There was no clinically significant difference in target coverage. Functionally-guided IMRT plans incorporating hyperpolarised He MRI information can reduce the dose received by ventilated lung without comprising PTV coverage. The effect was greater for optimised beam angles rather than uniformly spaced fields.