OBJECTIVE: To evaluate the safety and potential efficacy of "recombinant human growth hormone (rhGH)" on the functional recovery of completed stroke patients. METHOD: Completed stroke patients were recruited. All participants were randomly assigned to the GH group (rhGH injection and rehabilitative therapy) or the control group (only rehabilitative therapy). Above all, they were closely monitored for safety. Further, for the efficacy measurement, Korean Modified Barthel Index (K-MBI), Manual Muscle strength Test (MMT), and Fugl-Meyer assessment (FMA) were assessed to determine the changes of functional recovery during 6-months of the study period. Along with it, diffusion tensor image was taken as the baseline, and a followed-up study to observe the changes in diffusion tensor tractography (DTT), during the period, and one patient in the GH group was also examined with functional MRI (fMRI). Index of fatigue on 5 point scale for the study period was also assessed. RESULTS: Twenty-two patients were enrolled, and 15 completed the study and were included in the analysis. No harmful adverse events were observed in the GH group. By comparison between the groups, the GH group showed more improvement in K-MBI than the control group (p<0.05). DTT showed less decrement of fibers in the GH group than in the control group, without statistical significance. fMRI showed an increment in the activated area. Patients in the GH group expressed no fatigue at all, during the study period (p=0.00). CONCLUSION: The administration of rhGH in long term resulted in the improvement in K-MBI, and subjectively less tiredness during the injection period.
Diffusion ; Fatigue ; Growth Hormone ; Human Growth Hormone ; Humans ; Insulin-Like Growth Factor I ; Magnetic Resonance Imaging ; Muscle Strength ; Stroke

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.