Objective:To achieve the conversion from dual-energy cone-beam CT (DECBCT) at the kilovolt (KV) level to projections at the megavolt (MV) level using an improved CycleGAN network, in order to provide a potential reference benchmark and real-time monitoring of in vivo doses delivered by exit beams for the safe implementation of advanced techniques such as online adaptive radiotherapy. Methods:Simulated patient data were generated using a 4D extended cardiac torso (XCAT) model, and projections were generated based on the geometric parameters of Varian′s onboard cone-beam CT. Furthermore, relative electron density (RED) images were derived from DECBCT images using an iterative dual-energy decomposition algorithm. The SE-CycleGAN and CycleGAN networks were trained to generate MV projection images using DECBCT projections and RED images, respectively. The performance of both methods was evaluated using metrics including structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE).Results:SE-CycleGAN significantly outperformed CycleGAN in all evaluation metrics ( Z = -23.92, -26.17, -25.54, -26.80, -11.54, -11.21, P<0.05), particularly in learning global information. Besides, although both methods generated satisfactory MV projections, training using DECBCT projections as input yielded better effects than training using RED images. For all the 3 636 sets of projections in the test set, the SE-CycleGAN and CycleGAN networks using DECBCT projections as input respectively yielded SSIMs of 0.997 7±0.000 7 and 0.997 1±0.001 6, PSNRs of 39.625 0±4.684 4 and 36.272 2±5.566 3, and RMSEs of 0.004 1±0.002 7 and 0.006 3±0.0043, respectively. In contrast, the SE-CycleGAN and CycleGAN networks using RED projections as input respectively yielded SSIMs of 0.996 8±0.001 0 and 0.996 2±0.001 5, PSNRs of 38.548 7±3.637 4 and 36.007 3±4.437 8, and RMSEs of 0.004 3±0.002 2 and 0.006 1±0.0037, respectively. Conclusions:This study proposed a new method to establish reference benchmarks for in vivo dose monitoring based on DECBCT and deep learning technologies. This method is accurate and effective according to the preliminary validation using virtual simulation experiments.

Objective:To achieve the conversion from dual-energy cone-beam CT (DECBCT) at the kilovolt (KV) level to projections at the megavolt (MV) level using an improved CycleGAN network, in order to provide a potential reference benchmark and real-time monitoring of in vivo doses delivered by exit beams for the safe implementation of advanced techniques such as online adaptive radiotherapy. Methods:Simulated patient data were generated using a 4D extended cardiac torso (XCAT) model, and projections were generated based on the geometric parameters of Varian′s onboard cone-beam CT. Furthermore, relative electron density (RED) images were derived from DECBCT images using an iterative dual-energy decomposition algorithm. The SE-CycleGAN and CycleGAN networks were trained to generate MV projection images using DECBCT projections and RED images, respectively. The performance of both methods was evaluated using metrics including structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and root mean square error (RMSE).Results:SE-CycleGAN significantly outperformed CycleGAN in all evaluation metrics ( Z = -23.92, -26.17, -25.54, -26.80, -11.54, -11.21, P<0.05), particularly in learning global information. Besides, although both methods generated satisfactory MV projections, training using DECBCT projections as input yielded better effects than training using RED images. For all the 3 636 sets of projections in the test set, the SE-CycleGAN and CycleGAN networks using DECBCT projections as input respectively yielded SSIMs of 0.997 7±0.000 7 and 0.997 1±0.001 6, PSNRs of 39.625 0±4.684 4 and 36.272 2±5.566 3, and RMSEs of 0.004 1±0.002 7 and 0.006 3±0.0043, respectively. In contrast, the SE-CycleGAN and CycleGAN networks using RED projections as input respectively yielded SSIMs of 0.996 8±0.001 0 and 0.996 2±0.001 5, PSNRs of 38.548 7±3.637 4 and 36.007 3±4.437 8, and RMSEs of 0.004 3±0.002 2 and 0.006 1±0.0037, respectively. Conclusions:This study proposed a new method to establish reference benchmarks for in vivo dose monitoring based on DECBCT and deep learning technologies. This method is accurate and effective according to the preliminary validation using virtual simulation experiments.

Objective:To evaluate the enrollment rate, mutation rate and causes of variability the clinical pathway of bronchopneumonia.Methods:The enrollment rate, completion rate, variation and reasons of the clinical pathway in Beijing Children′s Hospital, Capital Medical University from January 2012 to December 2016 were retrospectively collected.Data of patients after the clinical pathway of bronchopneumonia in other tertiary class A hospitals were gathered by questionnaires, and the enrollment rate, completion rate, variation rate and reasons were analyzed.Results:(1)At the end of 2016, 11 of the 13 hospitals included in this study had implemented the clinical pathway for 5 years, 1 hospital for 3 years, and 1 hospital for 2 years.(2) Eleven hospitals provided their enrollment rates.The enrollement rate of 2 hospitals was<50%, and that of 9 hospitals was>80%.The annual completion rate of Beijing Children′s Hospital was ≥75%, and the completion rates offered by 8 hospitals were basically >70%.(3) Since the implementation of the clinical pathway for 5 years in Beijing Children′s Hospital, a total of 427 cases were enrolled of which 93 cases were mutated (variability 21.78%). The variability of 5 hospitals was maintained at <15%.The variability of 3 hospitals decreased with the implementation years, and became qualified.The variability of 1 hospital first rebounded and then controlled; 1 hospital increased by 27.65%; 1 hospital was first controlled and rebounded; 1 hospital was always >15%.The main cause of the mutation was coexisting diseases, complications, progression of the disease, or correction of the first diagnosis, etc.Conclusions:The completion rate of tertiary class A hospitals meets the requirements of national policy.However, the enrollment rate needs to be improved, and the variation rate among different hospitals differs a lot.Further implementation of the clinical pathway should be strengthened and monitored.

Objective To study the relationship between injury site (right or left) or pathological type (hemorrhage or infarct) and atten-tion impairment in post stroke cognitive impairment (PSCI). Methods June, 2014 to June 2015, 49 patients with PSCI were assessed with Test of Attentional Performance (TAP) within 1 week of admission. The reaction time and the numbers of correct response were recorded. Results The reaction time of alertness (with or without alarm) was shorter in the patients with left infarction (Z=-2.32, t=-3.76, P<0.05), and the correct number of auditory was more (Z=2.42, P<0.05) than those with right infarction. The reaction time of incompatibility test and Go/Nogo test was shorter in patients with left hemorrhage than those with right hemorrhage (Z>2.32, t=-3.10, P<0.05). Conclusion A trend of lateralization was found in patients with PSCI, rather than in the pathology.

The development of health-related quality of life (HRQL) instrument in traditional Chinese medicine (TCM) is increasing rapidly in China, but few studies focus on their necessity. This study explores the necessity of the development of TCM instruments from both theoretical and practice perspectives, and aims to explain whether the adoption of the cross-medical style is valid. Through theoretical analysis, both TCM and Western medicine instruments show the same objectives, whereas TCM instruments are more suitable for the Chinese social behavior, customs and expectations. In practical analysis, 47 TCM instruments were identified, among which 17 had 18 corresponding Western medical instruments. In the domains layer, except for physiological, psychological and social factors, TCM instruments focus more on the harmony between body and spirit, humanity and nature or human and society and the constitution, etc. In the facts layer, TCM instruments focus on the emotions, initiative social intercourse, TCM symptoms, diet, sleep, taste, feces and urine, etc. In addition, significant differences existed in the methods of information selection. There is no need to modify cross-medical style research except when TCM characteristic terms exist, but attention must be paid to the influence of culture in different areas. Therefore, the TCM instruments can resolve the limitations of the application of Western medical instruments to the Chinese setting, while also having remarkable abilities of information coverage and detection. Both forms of instruments have the capacity and requirement to inter-communicate with each other in order to serve the whole Chinese cultural system. Generally speaking, there is no need to modify the instruments in cross-medical style research. But this point requires further demonstration in the rigorous designed clinical trials.