Image quality improvement of single-shot turbo spin-echo magnetic resonance imaging of female pelvis using a convolutional neural network
Abstract
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…
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Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 21 participants or records.
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