Data‐driven synthetic MRI FLAIR artifact correction via deep neural network

Abstract

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. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2019;50:1413–1423.

Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment

“…To apply the DL-based artifact correction in the present study, a pretrained CNN from an original work by Ryu et al [10] was used. The network architecture was based on the residual nets (RESNET) architecture [12] with several modifications.…”

“…This network used two combined loss functions, namely the mean absolute error and perceptual loss [13]. While this DL-based method has shown promising results in correcting artifacts in synthetic FLAIR, the previous validation study of the method relied on a dataset from a single scanner and only a small number of test data for 20 subjects [10]. Moreover, the network of the previous study was entirely trained on images obtained from a single scanner (GE Discovery 750W GE Healthcare, Milwaukee, USA) in a single institution [10].…”

“…Two recent studies using deep learning (DL) have introduced the improvement of the synthetic FLAIR image quality [10,11]. Although those studies employed different methodological approaches-convolutional neural network with perceptual loss function (CNN) vs. pixel-wise neural network with conditional generative adversarial network (GAN) loss function-both of them showed remarkable potential to solve this issue [10,11]. However, the studies used DL-based correction for a synthetic FLAIR image employed a limited number of study participants at each institution; therefore, these new approaches should be validated in a different scan environment to establish their clinical utility.…”

“…However, the studies used DL-based correction for a synthetic FLAIR image employed a limited number of study participants at each institution; therefore, these new approaches should be validated in a different scan environment to establish their clinical utility. Thus, we aimed to investigate the capability of the already-trained DL model with CNN [10] in a different scanning environment from the perspective of ameliorating the image quality of synthetic FLAIR images.…”

NMR Relaxation Measurements on Complex Samples Based on Real-Time Pure Shift Techniques

“…For complex samples with a lower concentration, such as quinine and azithromycin, the influence can be obviously observed, but satisfactory calculated relaxation results are still obtained. Moreover, some special data processing, such as deep learning techniques [30], may be suitable for suppressing these undesired signals and recovering clean pure shift spectra for relaxation calculations. Additionally, due to the reduced sample slices adopted in the ZS-based pure shift scheme, real-time ZS-IR/CPMG experiments intrinsically suffer from low signal intensity.…”