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本文引用的文献

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Neuroimage. 2021 Dec 1;244:118605. doi: 10.1016/j.neuroimage.2021.118605. Epub 2021 Sep 28.
2
Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter.神经网络在微观结构 MRI 参数估计中的应用:在白质扩散-弛豫模型中的应用。
Neuroimage. 2021 Dec 1;244:118601. doi: 10.1016/j.neuroimage.2021.118601. Epub 2021 Sep 22.
3
Training data distribution significantly impacts the estimation of tissue microstructure with machine learning.训练数据分布对机器学习估计组织微观结构有显著影响。
Magn Reson Med. 2022 Feb;87(2):932-947. doi: 10.1002/mrm.29014. Epub 2021 Sep 21.
4
Combined diffusion-relaxometry microstructure imaging: Current status and future prospects.联合扩散-弛豫成像技术的微观结构成像:现状与展望。
Magn Reson Med. 2021 Dec;86(6):2987-3011. doi: 10.1002/mrm.28963. Epub 2021 Aug 19.
5
Removal of partial Fourier-induced Gibbs (RPG) ringing artifacts in MRI.磁共振成像中部分傅里叶引起的吉布斯(RPG)伪影的消除。
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6
MESMERISED: Super-accelerating T relaxometry and diffusion MRI with STEAM at 7 T for quantitative multi-contrast and diffusion imaging.着迷:使用 7T 的 STEAM 进行超加速 T 弛豫率和扩散 MRI,用于定量多对比度和扩散成像。
Neuroimage. 2021 Oct 1;239:118285. doi: 10.1016/j.neuroimage.2021.118285. Epub 2021 Jun 17.
7
The sensitivity of diffusion MRI to microstructural properties and experimental factors.扩散磁共振成像对微观结构特性和实验因素的敏感性。
J Neurosci Methods. 2021 Jan 1;347:108951. doi: 10.1016/j.jneumeth.2020.108951. Epub 2020 Oct 2.
8
Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising.多参数定量活体脊髓 MRI 采用统一信号读出和图像去噪。
Neuroimage. 2020 Aug 15;217:116884. doi: 10.1016/j.neuroimage.2020.116884. Epub 2020 Apr 29.
9
Towards unconstrained compartment modeling in white matter using diffusion-relaxation MRI with tensor-valued diffusion encoding.利用张量值扩散编码的扩散弛豫磁共振成像实现白质中无约束的隔室建模。
Magn Reson Med. 2020 Sep;84(3):1605-1623. doi: 10.1002/mrm.28216. Epub 2020 Mar 6.
10
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当假设单室弛豫和质子密度时,扩散 MRI 数据的自由水模型的扭曲。

The distortions of the free water model for diffusion MRI data when assuming single compartment relaxometry and proton density.

机构信息

Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States of America.

Center for Health Sciences, SRI International, Menlo Park, CA, United States of America.

出版信息

Phys Med Biol. 2023 Feb 21;68(5). doi: 10.1088/1361-6560/acb30b.

DOI:10.1088/1361-6560/acb30b
PMID:36638532
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10100575/
Abstract

To document the bias of thefree water model of diffusion MRI (dMRI) signal vis-à-vis amodel which, in addition to diffusion, incorporates compartment-specific proton density (PD), T1 recovery during repetition time (TR), and T2 decay during echo time (TE).Both models assume that volume fractionof the total signal in any voxel arises from the free water compartment () such as cerebrospinal fluid or edema, and the remainder (1) from hindered water () which is constrained by cellular structures such as white matter (WM). Theandmodels are compared on a synthetic dataset, using a range of PD, T1 and T2 values. We then fit the models to anhealthy brain dMRI dataset. For bothanddata we use experimentally feasible TR, TE, signal-to-noise ratio (SNR) and physiologically plausible diffusion profiles.From the simulations we see that the difference between the estimatedandis largest for mid-range ground-truth, and it increases as SNR increases. The estimation of volume fractionis sensitive to the choice of model,or, but the estimated diffusion parameters are robust to small perturbations in the simulation.is more accurate and precise than. In the white matter (WM) regions of theimages,is lower than.In dMRI models for free water, accounting for compartment specific PD, T1 and T2, in addition to diffusion, improves the estimation of model parameters. This extra model specification attenuates the estimation bias of compartmental volume fraction without affecting the estimation of other diffusion parameters.

摘要

记录扩散磁共振成像(dMRI)信号的自由水模型的偏差,该模型除了扩散外,还结合了特定隔室的质子密度(PD)、重复时间(TR)期间的 T1 恢复和回波时间(TE)期间的 T2 衰减。这两个模型都假设任何体素中总信号的体积分数来自自由水隔室(),例如脑脊液或水肿,其余的(1)来自受限水(),受细胞结构的限制,如白质(WM)。在合成数据集上比较了和模型,使用了一系列 PD、T1 和 T2 值。然后,我们将模型拟合到健康大脑 dMRI 数据集上。对于和数据,我们使用实验上可行的 TR、TE、信噪比(SNR)和生理上合理的扩散分布。从模拟中我们可以看出,估计的和之间的差异在中等范围的真实值时最大,并且随着 SNR 的增加而增加。体积分数的估计对模型或的选择敏感,但模拟中的小扰动对估计的扩散参数具有鲁棒性。比更准确和精确。在图像的白质(WM)区域中,比。在除了扩散之外还考虑特定隔室 PD、T1 和 T2 的自由水 dMRI 模型中,改善了模型参数的估计。这种额外的模型规范减轻了隔室体积分数的估计偏差,而不影响其他扩散参数的估计。