Purpose To measure the spatial resolution in MRI quality control testing automatically based on the American College of Radiology(ACR)phantom using the support vector machine(SVM)method,and the feasibility,accuracy and measurement speed of this method are explored.Materials and Methods Quality control tests were performed using eight MRI devices at Beijing Friendship Hospital of Capital Medical University.A retrospective study was conducted on 71 MRI quality control test images collected based on ACR phantoms between 2017 and 2019.The images were preprocessed by binarization,extraction region of interest and so on.An SVM-based classification model was constructed for analyzing the spatial resolution of dot arrays in row and column directions.The dataset was randomly split into a training set and a test set.The generalization performance of the classification model in this study was evaluated through accuracy,precision,recall and F1 score on the test set.Comparing the results of spatial resolution measurements obtained by both manual and automatic method,we demonstrated the feasibility and accuracy of the method.Additionally,the time taken for the automatic spatial resolution measurement was recorded.Results In this study,the proposed method of automatically measuring the spatial resolution of ACR phantom test images using SVM was feasible,high accuracy and short time.In classification performance test,the accuracy of the spatial resolution of the row directional latices was 95%,the precision was 100%.The accuracy of the spatial resolution of the column directional latices was 97%,the precision was 100%.Among the test cases,the results of automatic measurements matched those of manual measurements in 13 out of 14 cases.On average,automatic spatial resolution measurement took 0.158 seconds per case.Conclusion This study achieves automatic measurement of spatial resolution in MRI quality control based on the ACR phantom using SVM method.The method demonstrates high accuracy and fast measurement speeds,holding significant implications for future rapid MRI quality control stability testing.

Purpose To explore an automatic detection method for quality control performance indicators of radio frequency coils based on American College of Radiology(ACR)phantom,and verify its accuracy stability and computational efficiency.Materials and Methods A retrospective study was conducted on 50 quality control images collected based on ACR phantom in Beijing Friendship Hospital,Capital Medical University from May 2017 to July 2019.The measurement and calculation methods of signal noise ratio(SNR),percent image uniformity(PIU)and percent signal ghosting(PSG)were used to automatically calculate the above indicators using a self-designed program in Python.A simple linear regression analysis on the automatically calculated SNR,PSG and PIU values compared to the manually measured results was performed,and Bland-Altman analysis was used to calculate the percentage difference to evaluate the consistency and bias between the performance indicators calculated by the two methods.The time consumption of two detection methods was compared to verify their computational complexity and efficiency.Results There was a strong correlation between the performance indicators SNR,PSG and PIU of radio frequency coils measured and calculated automatically and manually(r=0.991 4,0.992 8 and 0.909 8,all P<0.0001).The Bland-Altman results showed that most of the data fall within the 95%confidence interval and were evenly distributed.In terms of computational complexity and efficiency,compared to the complex manual delineation and calculation of 2-3 minutes per case,automatic detection could simultaneously obtain SNR,PSG and PIU values in less than 1 second.Conclusion The automatic and manual measurement methods have good consistency,and the automatic detection method is easy to operate,which is helpful for the daily quality control work and performance monitoring of radio frequency coils.

Purpose To measure the spatial resolution in MRI quality control testing automatically based on the American College of Radiology(ACR)phantom using the support vector machine(SVM)method,and the feasibility,accuracy and measurement speed of this method are explored.Materials and Methods Quality control tests were performed using eight MRI devices at Beijing Friendship Hospital of Capital Medical University.A retrospective study was conducted on 71 MRI quality control test images collected based on ACR phantoms between 2017 and 2019.The images were preprocessed by binarization,extraction region of interest and so on.An SVM-based classification model was constructed for analyzing the spatial resolution of dot arrays in row and column directions.The dataset was randomly split into a training set and a test set.The generalization performance of the classification model in this study was evaluated through accuracy,precision,recall and F1 score on the test set.Comparing the results of spatial resolution measurements obtained by both manual and automatic method,we demonstrated the feasibility and accuracy of the method.Additionally,the time taken for the automatic spatial resolution measurement was recorded.Results In this study,the proposed method of automatically measuring the spatial resolution of ACR phantom test images using SVM was feasible,high accuracy and short time.In classification performance test,the accuracy of the spatial resolution of the row directional latices was 95%,the precision was 100%.The accuracy of the spatial resolution of the column directional latices was 97%,the precision was 100%.Among the test cases,the results of automatic measurements matched those of manual measurements in 13 out of 14 cases.On average,automatic spatial resolution measurement took 0.158 seconds per case.Conclusion This study achieves automatic measurement of spatial resolution in MRI quality control based on the ACR phantom using SVM method.The method demonstrates high accuracy and fast measurement speeds,holding significant implications for future rapid MRI quality control stability testing.

Purpose To explore an automatic detection method for quality control performance indicators of radio frequency coils based on American College of Radiology(ACR)phantom,and verify its accuracy stability and computational efficiency.Materials and Methods A retrospective study was conducted on 50 quality control images collected based on ACR phantom in Beijing Friendship Hospital,Capital Medical University from May 2017 to July 2019.The measurement and calculation methods of signal noise ratio(SNR),percent image uniformity(PIU)and percent signal ghosting(PSG)were used to automatically calculate the above indicators using a self-designed program in Python.A simple linear regression analysis on the automatically calculated SNR,PSG and PIU values compared to the manually measured results was performed,and Bland-Altman analysis was used to calculate the percentage difference to evaluate the consistency and bias between the performance indicators calculated by the two methods.The time consumption of two detection methods was compared to verify their computational complexity and efficiency.Results There was a strong correlation between the performance indicators SNR,PSG and PIU of radio frequency coils measured and calculated automatically and manually(r=0.991 4,0.992 8 and 0.909 8,all P<0.0001).The Bland-Altman results showed that most of the data fall within the 95%confidence interval and were evenly distributed.In terms of computational complexity and efficiency,compared to the complex manual delineation and calculation of 2-3 minutes per case,automatic detection could simultaneously obtain SNR,PSG and PIU values in less than 1 second.Conclusion The automatic and manual measurement methods have good consistency,and the automatic detection method is easy to operate,which is helpful for the daily quality control work and performance monitoring of radio frequency coils.

Objective To analyze and compare the changes in local functional activity of the brain under different recovery times after a 90-day,-6° head down bed rest(HDBR)experiment,clarify the mechanism of brain function remodeling during gravity adaptation,and provide imaging basis and guidance for the model construction and health assessment.Methods 36 healthy male volunteers underwent resting-state functional magnetic resonance imaging(rs-fMRI)scans before HDBR(PRE),3 days after 90-day HDBR(R+3),and 26-28 days after 90-day HDBR(R+26-28),and calculated regional homogeneity(ReHo)and low-frequency amplitude(ALFF)and perform statistical analysis between different time points.Results Compared with PRE-status,ReHo value in the left inferior parietal gyrus and ALFF value in the right posterior cingulate gyrus decreased significantly in R+3 status,while ReHo value in the left calcarine,right lingual gyrus and ALFF value in the precuneus,left paracentral lobule,and right postcentral gyrus increased significantly.At R+26-28 status,more brain areas showed significant changes.Conclusion 90 days of long-term HDBR and subsequent recovery will lead to changes in the neural functional activities of the brain's default network,sensorimotor network and visual network,and obvious adaptive remodeling will occur after a longer period of gravity re-adaptation and recovery.

Aging is a major risk factor for diseases such as osteoarthritis (OA) and osteoporosis. However, they are not necessarily the results of aging, and the relationship between changes in bone and cartilage associated with aging and disease progression is still unclear. Studies have shown that the development and progression of OA is not a simple cartilage wear process, while its occurrence involves complex biological, chemical and mechanical changes in the tissues of the entire joint, especially the interaction of mechanics and biochemistry between cartilage and subchondral bone. Aging contributes to the occurrence and development of OA, but it is not the cause of OA. Changes associated with aging provide a foundation for OA to start, making joints more susceptible to other factors such as abnormal biomechanics and biochemistry, thereby promoting the development of OA. Therefore, understanding the basic mechanisms by which aging affects joint tissue may provide new targets for slowing or preventing the development of OA. In this paper, the related research progresses are reviewed from three aspects, i.e. age-related changes in cartilage and subchondral bone, mechanical conduction and angiogenesis.

This paper proposed a mobile vital-signs monitoring system based on ZigBee localization and wireless transmission technology for the elderly in nursing home. The system can monitor the vital-signs (pulse, ECG and blood oxygen), localize human body and warn in emergency. The validity and accuracy of this system were testified by the experiments of mobile acquisition and storage of pulse. In these experiments, the measurement of pulse ranged from 50 to 170 time a minute, the mean error of which was less than 3%. The mean error of localizing was less than 4 m. And the data transmission rate was 250 kbps. The system, which effectively conducts the real-time monitoring of the health and safety situation for the elderly, has a great significance for protecting the elderly's life safety.
Aged ; Equipment Design ; Homes for the Aged ; Humans ; Monitoring, Physiologic ; instrumentation ; Nursing Homes

This paper proposed a mobile vital-signs monitoring system based on ZigBee localization and wireless transmission technology for the elderly in nursing home. The system can monitor the vital-signs (pulse, ECG and blood oxygen), localize human body and warn in emergency. The validity and accuracy of this system were testified by the experiments of mobile acquisition and storage of pulse. In these experiments, the measurement of pulse ranged from 50 to 170 time a minute, the mean error of which was less than 3%. The mean error of localizing was less than 4 m. And the data transmission rate was 250 kbps. The system, which effectively conducts the real-time monitoring of the health and safety situation for the elderly, has a great significance for protecting the elderly's life safety.