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 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.

A Feasibility Study on Deep Learning Reconstruction to Improve Image Quality With PROPELLER Acquisition in the Setting of T2-Weighted Gynecologic Pelvic Magnetic Resonance Imaging

“…Our study showed that, for overall image quality, radiologists preferred DL-reconstructed images over non–DL-reconstructed images ( P < 0.0001). Recently published literature has shown improvements in the image quality in the female pelvis with DL reconstruction 7,8 . To our knowledge, this is the first study to assess the DL reconstruction method for the PROPELLER sequence in patients with gynecologic malignancies.…”

“…Recently published literature has shown improvements in the image quality in the female pelvis with DL reconstruction. 7,8 To our knowledge, this is the first study to assess the DL reconstruction method for the PROPELLER sequence in patients with gynecologic malignancies. A recently published study using DL reconstruction in prostate MRI also showed improved image quality in T2-weighted FSE.…”

Artificial intelligence based image quality enhancement in liver MRI: a quantitative and qualitative evaluation

“…Moreover, the deep learning protocol showed improved image quality compared with the NAÏVE one. To the best of our knowledge, there are no studies performed on abdominal DWI; however, Misaka et al [ 17 ] conducted an interesting study on female pelvis. They built a deep learning algorithm to improve single-shot turbo spin-echo (SSTSE) sequences and compare them with TSE, considered as ground truth, and SSTSE.…”

Generative adversarial network-based post-processed image super-resolution technology for accelerating brain MRI: comparison with compressed sensing

“…With the recent development of deep-learning, superresolution (SR) technology as an image postprocessing technology can be implemented in clinical practice (13)(14)(15). In particular, generative adversarial networks (GANs) (16) are deep neural network architectures in which two networks are configured to face each other and have attracted much attention in processing natural images and medical images (17,18).…”