Data-driven synthetic MRI FLAIR artifact correction via deep neural network
Data-driven synthetic MRI FLAIR artifact correction via deep neural network
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DOI：
10.1002/jmri.26712
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发表时间：
2019-11-01
影响因子：
4.4
通讯作者：
Kim, Dong-Hyun
中科院分区：
文献类型：
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作者：
Ryu, Kanghyun;Nam, Yoonho;Kim, Dong-Hyun
背景通过合成MRI方法进行FLAIR（液体衰减反转恢复）成像会导致大脑中出现伪影，从而导致诊断局限性。伪影的主要来源是部分容积效应和流动，这是很难纠正的分析建模。在这项研究中，开发了一种基于深度学习（DL）的合成FLAIR方法，该方法不需要对信号进行分析建模。目的用DL方法校正FLAIR合成图像中的伪影。研究类型回顾性。受试者共有80例受试者有临床适应症（60.6 +/- 16.7岁，38名男性，42名女性）分为三组：训练集（56例受试者，62.1 ± 14.8岁，25例男性，31例女性），验证集（1例受试者，62岁，男性）和测试集（23例受试者，57.3 ± 20.4岁，13例男性，10例女性）。场强/序列3 T MRI，使用多动态多回波采集（MDME）序列进行合成MRI和常规FLAIR序列。评估计算未校正合成FLAIR和DL校正FLAIR的归一化均方根（NRMSE）和结构相似性（SSIM）。此外，三名神经放射科医生对三个FLAIR数据集进行盲法评分，评价脑沟/脑室周围和脑室内/脑池空间区域的图像质量和伪影。进行成对Student t检验和Wilcoxon检验。结果在定量评估中，NRMSE从4.2%提高到2.9%（P < 0.0001），SSIM从0.85提高到0.93（P < 0.0001）。此外，当使用DL校正FLAIR时，白色物质、灰质和脑脊液（CSF）区域的NRMSE值分别从1.58%显著改善至1.26%（P <0.001）、3.1%改善至1.5%（P <0.0001）和2.7%改善至1.4%（P < 0.0001）。对于定性评估，DL校正实现了整体质量的改善，脑沟和脑室周围区域以及脑室内和脑池空间区域的伪影更少。数据结论DL方法为校正合成FLAIR中的伪影提供了一种有前途的方法。技术功效：第1阶段J. Magn. Reson。Imaging 2019;50：1413-1423.
Background FLAIR (fluid attenuated inversion recovery) imaging via synthetic MRI methods leads to artifacts in the brain, which can cause diagnostic limitations. The main sources of the artifacts are attributed to the partial volume effect and flow, which are difficult to correct by analytical modeling. In this study, a deep learning (DL)-based synthetic FLAIR method was developed, which does not require analytical modeling of the signal. Purpose To correct artifacts in synthetic FLAIR using a DL method. Study Type Retrospective. Subjects A total of 80 subjects with clinical indications (60.6 +/- 16.7 years, 38 males, 42 females) were divided into three groups: a training set (56 subjects, 62.1 +/- 14.8 years, 25 males, 31 females), a validation set (1 subject, 62 years, male), and the testing set (23 subjects, 57.3 +/- 20.4 years, 13 males, 10 females). Field Strength/Sequence 3 T MRI using a multiple-dynamic multiple-echo acquisition (MDME) sequence for synthetic MRI and a conventional FLAIR sequence. Assessment Normalized root mean square (NRMSE) and structural similarity (SSIM) were computed for uncorrected synthetic FLAIR and DL-corrected FLAIR. In addition, three neuroradiologists scored the three FLAIR datasets blindly, evaluating image quality and artifacts for sulci/periventricular and intraventricular/cistern space regions. Statistical Tests Pairwise Student's t-tests and a Wilcoxon test were performed. Results For quantitative assessment, NRMSE improved from 4.2% to 2.9% (P < 0.0001) and SSIM improved from 0.85 to 0.93 (P < 0.0001). Additionally, NRMSE values significantly improved from 1.58% to 1.26% (P < 0.001), 3.1% to 1.5% (P < 0.0001), and 2.7% to 1.4% (P < 0.0001) in white matter, gray matter, and cerebral spinal fluid (CSF) regions, respectively, when using DL-corrected FLAIR. For qualitative assessment, DL correction achieved improved overall quality, fewer artifacts in sulci and periventricular regions, and in intraventricular and cistern space regions. Data Conclusion The DL approach provides a promising method to correct artifacts in synthetic FLAIR. Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1413-1423.