Magnetic resonance (MR) imaging plays a highly important role in radiotherapy treatment planning for the segmentation of tumor volumes and organs. However, the use of MR is limited, owing to its high cost and the increased use of metal implants for patients. This study is aimed towards patients who are contraindicated owing to claustrophobia and cardiac pacemakers, and many scenarios in which only computed tomography (CT) images are available, such as emergencies, situations lacking an MR scanner, and situations in which the cost of obtaining an MR scan is prohibitive. From medical practice, our approach can be adopted as a screening method by radiologists to observe abnormal anatomical lesions in certain diseases that are difficult to diagnose by CT. The proposed approach can estimate an MR image based on a CT image using paired and unpaired training data. In contrast to existing synthetic methods for medical imaging, which depend on sparse pairwise-aligned data or plentiful unpaired data, the proposed approach alleviates the rigid registration of paired training, and overcomes the context-misalignment problem of unpaired training. A generative adversarial network was trained to transform two-dimensional (2D) brain CT image slices into 2D brain MR image slices, combining the adversarial, dual cycle-consistent, and voxel-wise losses. Qualitative and quantitative comparisons against independent paired and unpaired training methods demonstrated the superiority of our approach.

Objective : To generate synthetic spine magnetic resonance (MR) images from spine computed tomography (CT) using generative adversarial networks (GANs), as well as to determine the similarities between synthesized and real MR images. Methods : GANs were trained to transform spine CT image slices into spine magnetic resonance T2 weighted (MRT2) axial image slices by combining adversarial loss and voxel-wise loss. Experiments were performed using 280 pairs of lumbar spine CT scans and MRT2 images. The MRT2 images were then synthesized from 15 other spine CT scans. To evaluate whether the synthetic MR images were realistic, two radiologists, two spine surgeons, and two residents blindly classified the real and synthetic MRT2 images. Two experienced radiologists then evaluated the similarities between subdivisions of the real and synthetic MRT2 images. Quantitative analysis of the synthetic MRT2 images was performed using the mean absolute error (MAE) and peak signal-to-noise ratio (PSNR). Results : The mean overall similarity of the synthetic MRT2 images evaluated by radiologists was 80.2%. In the blind classification of the real MRT2 images, the failure rate ranged from 0% to 40%. The MAE value of each image ranged from 13.75 to 34.24 pixels (mean, 21.19 pixels), and the PSNR of each image ranged from 61.96 to 68.16 dB (mean, 64.92 dB). Conclusion :This was the first study to apply GANs to synthesize spine MR images from CT images. Despite the small dataset of 280 pairs, the synthetic MR images were relatively well implemented. Synthesis of medical images using GANs is a new paradigm of artificial intelligence application in medical imaging. We expect that synthesis of MR images from spine CT images using GANs will improve the diagnostic usefulness of CT. To better inform the clinical applications of this technique, further studies are needed involving a large dataset, a variety of pathologies, and other MR sequence of the lumbar spine.

Magnetic resonance imaging (MRI) plays a significant role in the diagnosis of lumbar disc disease. However, the use of MRI is limited because of its high cost and significant operating and processing time. More importantly, MRI is contraindicated for some patients with claustrophobia or cardiac pacemakers due to the possibility of injury. In contrast, computed tomography (CT) scans are much less expensive, are faster, and do not face the same limitations. In this paper, we propose a method for estimating lumbar spine MR images based on CT images using a novel objective function and a dual cycle-consistent adversarial network (DC 2 Anet) with semi-supervised learning. The objective function includes six independent loss terms to balance quantitative and qualitative losses, enabling the generation of a realistic and accurate synthetic MR image. DC 2 Anet is also capable of semi-supervised learning, and the network is general enough for supervised or unsupervised setups. Experimental results prove that the method is accurate, being able to construct MR images that closely approximate reference MR images, while also outperforming four other state-of-the-art methods.

Introduction:Assessments of the range of motion (ROM) in human joints have been widely used to evaluate the joint condition. Although maker based motion capture system is the most popular and practical method in the clinical field, there have been limitations such as the relatively long time required in procedure or influence of attached markers on natural movement. Recently, markerless motion capture systems have been actively developed due to their relatively lower cost and convenience for installation. The POM-Checker (Team Elysium Inc., Seoul, Rep of Korea), a markerless motion capture system, was developed with new algorithms to assess the ROM in human joints. However, the measure of the POM-Checker has not been compared with a golden-standard device in evaluating the ROM in the human joints. So we developed a protocol to evaluate the validation and reliability of the POM-Checker in measuring the shoulder ROM. This study will also provide a standard procedure to measure the shoulder ROM with the POM-Checker and 3D motion analysis system simultaneously.Methods/design:This protocol is for a single institution comparative study to evaluate the validity and reliability of POM-Checker. Six participants will be recruited. We will measure the angles of shoulder abduction and flexion with POM-Checker and 3D motion analysis system simultaneously. The primary outcome is the angles of shoulder abduction and flexion.Discussion:This will be the first study to analyze the validity and reliability of POM-Checker in measuring shoulder ROM. Although the sample size of this study is small, it may not influence on the results conclusively, because the measures are very precise numerical angles. Furthermore, the angles of shoulder movements will be measured with both devices simultaneously.Conclusion:The results of the study will be helpful to find out the validity and reliability of a new developed ROM measure device, POM-Checker, by comparison with a golden standard system, 3D motion capture system, in measuring the shoulder ROM. It will also contribute to use of the POM-Checker in measuring the ROMs in many human joints

No abstract