Highly accelerated, model-free diffusion tensor MRI reconstruction using neural networks
Highly accelerated, model-free diffusion tensor MRI reconstruction using neural networks
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DOI：
10.1002/mp.13400
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发表时间：
2019-04-01
期刊：
影响因子：
3.8
通讯作者：
Ennis, Daniel B.
中科院分区：
文献类型：
--
作者：
Aliotta, Eric;Nourzadeh, Hamidreza;Ennis, Daniel B.
本研究的目的是开发一种神经网络，从高速加速扫描中准确地执行扩散张量成像(DTI)重建。材料和方法这项回顾性研究使用了2013-2018年间获得的数据，并得到了当地机构审查委员会的批准。使用健康志愿者(N=10)采集的DTI数据训练神经网络DiffNet，从采集的扩散编码方向为3到20个方向的DTI数据的小子集重建分数各向异性(FA)和平均扩散系数(MD)图。然后在志愿者和多形性胶质母细胞瘤患者(GBM，N=12)中使用DiffNet和传统重建技术重建FA和MD图。在志愿者扫描中对准确性和精确度进行量化，并在重建过程中进行比较。通过评估DTI获得的肿瘤体积与增强后T1加权MRI所定义的肿瘤体积之间的一致性，比较重建患者数据中肿瘤勾画的准确性。用受试者工作特征曲线下面积进行比较。结果DiffNet FA重建在所有加速度因素下均比常规重建更准确和精确。DiffNet允许仅使用三个扩散编码方向进行重建，且偏差显著低于使用六个方向的传统方法(0.010.01vs0.06+/-0.01，P
PurposeThe purpose of this study was to develop a neural network that accurately performs diffusion tensor imaging (DTI) reconstruction from highly accelerated scans.Materials and MethodsThis retrospective study was conducted using data acquired between 2013 and 2018 and was approved by the local institutional review board. DTI acquired in healthy volunteers (N=10) was used to train a neural network, DiffNet, to reconstruct fractional anisotropy (FA) and mean diffusivity (MD) maps from small subsets of acquired DTI data with between 3 and 20 diffusion-encoding directions. FA and MD maps were then reconstructed in volunteers and in patients with glioblastoma multiforme (GBM, N=12) using both DiffNet and conventional reconstructions. Accuracy and precision were quantified in volunteer scans and compared between reconstructions. The accuracy of tumor delineation was compared between reconstructed patient data by evaluating agreement between DTI-derived tumor volumes and volumes defined by contrast-enhanced T1-weighted MRI. Comparisons were performed using areas under the receiver operating characteristic curves (AUC).ResultsDiffNet FA reconstructions were more accurate and precise compared with conventional reconstructions for all acceleration factors. DiffNet permitted reconstruction with only three diffusion-encoding directions with significantly lower bias than the conventional method using six directions (0.010.01 vs 0.06 +/- 0.01, P