Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network.
Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network.
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DOI：
10.1097/md.0000000000023138
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发表时间：
2020-11-20
期刊：
影响因子：
1.6
通讯作者：
Ishida T
中科院分区：
文献类型：
--
作者：
Misaka T;Asato N;Ono Y;Ota Y;Kobayashi T;Umehara K;Ota J;Uemura M;Ashikaga R;Ishida T
我们开发了一种基于深度学习的方法来提高女性骨盆的单次激发涡轮自旋回波(SSTSE)图像的质量。我们的目的是比较基于深度学习的女性骨盆单次激发快速自旋回波(DL-SSTSE)图像与快速自旋回波(TSE)图像和常规SSTSE图像的图像质量。105名受试者分别作为训练组和测试组。我们进行了6次交叉验证。在训练过程中，将TSE图像作为输入生成低质量的图像。使用TSE图像作为地面真实图像。在测试过程中，将训练好的卷积神经网络应用于SSTSE图像。输出图像被表示为DL-SSTSE图像。除了DL-SSTSE图像外，还对SSTSE图像采用了经典的滤波方法。生成的图像被表示为F-SSTSE图像。测量臀部脂肪与子宫肌层的对比度(CR)和臀部脂肪的信噪比(SNR)。两名放射科医生使用5分制对这些图像进行分级，并从整体图像质量、对比度、噪声、运动伪影、子宫各层的边界清晰度和卵巢的显着性等方面对图像质量进行评估。使用Steel-Dwass多重比较测试比较CRS、SNR和图像质量分数。DL-SSTSE、F-SSTSE和TSE图像的CRS和SNR显著高于SSTSE图像。在整体图像质量、对比度、噪声和子宫各层边界清晰度方面，DL-SSTSE和TSE图像的得分显著高于SSTSE图像。DL-SSTSE和TSE图像在CRS、SNR和各自的得分方面无显著差异。在运动伪影方面，DL-SSTSE、F-SSTSE和SSTSE图像的得分显著高于TSE图像。在卵巢显影方面，DL-SSTSE图像显著高于F-SSTSE、SSTSE和TSE图像(P <； .001)。与SSTSE图像相比，DL-SSTSE图像显示更高的图像质量。与传统的TSE图像相比，DL-SSTSE图像具有可接受的图像质量，同时保持了SSTSE图像运动伪影稳健性和采集时间效率的优势。
We have developed a deep learning-based approach to improve image quality of single-shot turbo spin-echo (SSTSE) images of female pelvis. We aimed to compare the deep learning-based single-shot turbo spin-echo (DL-SSTSE) images of female pelvis with turbo spin-echo (TSE) and conventional SSTSE images in terms of image quality. One hundred five and 21 subjects were used as training and test sets, respectively. We performed 6-fold cross validation. In the training process, low-quality images were generated from TSE images as input. TSE images were used as ground truth images. In the test process, the trained convolutional neural network was applied to SSTSE images. The output images were denoted as DL-SSTSE images. Apart from DL-SSTSE images, classical filtering methods were adopted to SSTSE images. Generated images were denoted as F-SSTSE images. Contrast ratio (CR) of gluteal fat and myometrium and signal-to-noise ratio (SNR) of gluteal fat were measured for all images. Two radiologists graded these images using a 5-point scale and evaluated the image quality with regard to overall image quality, contrast, noise, motion artifact, boundary sharpness of layers in the uterus, and the conspicuity of the ovaries. CRs, SNRs, and image quality scores were compared using the Steel-Dwass multiple comparison tests. CRs and SNRs were significantly higher in DL-SSTSE, F-SSTSE, and TSE images than in SSTSE images. Scores with regard to overall image quality, contrast, noise, and boundary sharpness of layers in the uterus were significantly higher on DL-SSTSE and TSE images than on SSTSE images. There were no significant differences in the CRs, SNRs, and respective scores between DL-SSTSE and TSE images. The score with regard to motion artifacts was significantly higher on DL-SSTSE, F-SSTSE, and SSTSE images than on TSE images. The score with regard to the conspicuity of ovaries was significantly higher on DL-SSTSE images than on F-SSTSE, SSTSE, and TSE images (P < .001). DL-SSTSE images showed higher image quality as compared with SSTSE images. In comparison with conventional TSE images, DL-SSTSE images had acceptable image quality while keeping the advantage of the motion artifact-robustness and acquisition time efficiency in SSTSE imaging.