Abstract
(1) Introduction: Computed tomography (CT) and magnetic resonance imaging (MRI) play an important role in the diagnosis and evaluation of spinal diseases, especially degenerative spinal diseases. MRI is mainly used to diagnose most spinal diseases because it shows a higher resolution than CT to distinguish lesions of the spinal canals and intervertebral discs. When it is inevitable for CT to be selected instead of MR in evaluating spinal disease, evaluation of spinal disease may be limited. In these cases, it is very helpful to diagnose spinal disease with MR images synthesized with CT images. (2) Objective: To create synthetic lumbar magnetic resonance (MR) images from computed tomography (CT) scans using generative adversarial network (GAN) models and assess how closely the synthetic images resembled the true images using visual Turing tests (VTTs). (3) Material and Methods: Overall, 285 patients aged ≥ 40 years who underwent lumbar CT and MRI were enrolled. Based on axial CT and T2-weighted axial MR images from 285 patients, an image synthesis model using a GAN was trained using three algorithms (unsupervised, semi-supervised, and supervised methods). Furthermore, VTT to determine how similar the synthetic lumbar MR images generated from lumbar CT axial images were to the true lumbar MR axial images were conducted with 59 patients who were not included in the model training. For the VTT, we designed an evaluation form comprising 600 randomly distributed axial images (150 true and 450 synthetic images from unsupervised, semi-supervised, and supervised methods). Four readers judged the authenticity of each image and chose their first- and second-choice candidates for the true image. In addition, for the three models, structural similarities (SSIM) were evaluated and the peak signal to noise ratio (PSNR) was compared among the three methods. (4) Results: The mean accuracy for the selection of true images for all four readers for their first choice was 52.0% (312/600). The accuracies of determining the true image for each reader’s first and first + second choices, respectively, were as follows: reader 1, 51.3% and 78.0%; reader 2, 38.7% and 62.0%, reader 3, 69.3% and 84.0%, and reader 4, 48.7% and 70.7%. In the case of synthetic images chosen as first and second choices, supervised algorithm-derived images were the most often selected (supervised, 118/600 first and 164/600 second; semi-supervised, 90/600 and 144/600; and unsupervised, 80/600 and 114/600). For image quality, the supervised algorithm received the best score (PSNR: 15.987 ± 1.039, SSIM: 0.518 ± 0.042). (5) Conclusion: This was the pilot study to apply GAN to synthesize lumbar spine MR images from CT images and compare training algorithms of the GAN. Based on VTT, the axial MR images synthesized from lumbar CT using GAN were fairly realistic and the supervised training algorithm was found to provide the closest image to true images.
Highlights
The generative adversarial network (GAN) is a breakthrough deep learning technology that synthesize realistic images that are almost similar to true images
Based on visual Turing tests (VTTs), the axial magnetic resonance (MR) images synthesized from lumbar Computed tomography (CT) using GAN were fairly realistic and the supervised training algorithm was found to provide the closest image to true images
Lee et al reported the synthesis of spine MR images from spine CT images using GAN, with a mean overall similarity of synthetic MR T2-weighted images evaluated by radiologists of 80.2% [23]
Summary
The generative adversarial network (GAN) is a breakthrough deep learning technology that synthesize realistic images that are almost similar to true images. GAN generates new images that did not exist in the past by receiving input of various noises from an artificial neural network and has recently received a lot of attention and has been actively studied. Existing deep learning technology, such as CNN (convolutional neural network), used one multi-layered artificial neural network, but GAN interacts with two artificial neural networks, creating a realistic image that is difficult to distinguish. The generative adversarial network (GAN) model, which has attracted attention in the field of deep learning, can generate and transform images using two adversarial artificial neural networks, unlike conventional convolutional neural network (CNN) models. Learning should be conducted to determine that the image synthesized by the generative neural network is a fake image by the discriminative neural network
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