OBJECTIVES: To propose a single repetition time (TR) quantitative magnetic susceptibility imaging method for the lumbar spine using bipolar readout gradient, and compare the quantitative magnetic susceptibility measurement using single TR and dual TR methods for the lumbar spine with different bone densities. METHODS: A translation correction method was proposed to correct spatial misalignment along the frequency encoding direction between positive and negative gradient readout images, and the phase difference between the images was eliminated using a phase correction method. The data of lumbar vertebrae L1-L5 were collected using single TR and dual TR methods from 6 normal individuals, 2 patients with osteopenia, and 2 patients with osteoporosis. The magnetic susceptibility map was reconstructed, the quantitative results of single TR before and after correction were compared with those of the dual TR method. RESULTS: The linear regression result of the lumbar spine magnetic susceptibility values obtained by the single TR method before calibration and the dual TR method is Y=0.64*X-11.61. The linear regression result of the lumbar spine magnetic susceptibility values corrected by the single TR method and the dual TR method is Y=1.03*X+0.25. The results of the corrected single TR method were highly consistent with those of the dual TR method, and the calibrated single TR method could effectively distinguish osteopenia and osteoporosis patients from normal individuals. CONCLUSIONS The calibrated single TR bipolar readout gradient method can generate artifact-free lumbar spine quantitative magnetic susceptibility distribution maps and reduce data acquisition time by 50%.
Humans ; Lumbar Vertebrae/pathology* ; Magnetic Resonance Imaging/methods* ; Female ; Middle Aged ; Male ; Osteoporosis/diagnosis* ; Adult ; Bone Density ; Aged ; Bone Diseases, Metabolic/diagnosis*

Objective:To explore the application value of radiomics models based on MRI of vertebrae and paravertebral muscles in identifying potential vertebral fragility fracture risk in osteoporosis and osteopenia.Methods:This cross-sectional study collected data from patients who underwent both dual-energy X-ray absorptiometry (DXA) and lumbar MRI at the Third Affiliated Hospital of Southern Medical University between January 2014 and December 2023,retrospectively. Based on DXA results, patients were categorized into osteoporosis group ( n=302) and osteopenia group ( n=264), with fracture and non-fracture patients matched at 1∶1 ratio by propensity score matching based on age, gender, and body mass index. The fourth lumbar vertebra was selected as the region of interest (ROI) for the vertebral body, and the bilateral psoas major, erector spinae, and multifidus muscles were selected as the ROIs for the paraspinal muscles. A total of 7 259 radiomics features were extracted from these ROIs. The dataset was divided into a training set and a test set in an 8∶2 ratio by simple random sampling (osteoporosis group 241 and 61 cases, osteopenia group 211 and 53 cases). The T-score was used to establish the clinical model. After feature normalization and dimensionality reduction, logistic regression was applied to build three radiomics models: vertebral model, paraspinal muscle model, and vertebral-paraspinal muscle model. The T-score was then combined with the radiomics model that achieved the highest area under the receiver operating characteristic curve (AUC) in the test set to construct a clinical-radiomics combined model. Model performance was evaluated using the AUC. The DeLong test was used to compare the diagnostic efficacy between models. Results:In the test set, the vertebral-paravertebral muscle model achieved the highest AUC among radiomics models and was selected for combination with the T-score. In identifying potential vertebral fragility fractures of osteoporosis group, the AUC (95% CI) of the clinical model, vertebral model, paraspinal muscle model, vertebral-paraspinal muscle model, and clinical-radiomics model were 0.523 (0.373-0.672), 0.869 (0.779-0.959), 0.608 (0.464-0.752), 0.876 (0.791-0.961), and 0.860 (0.769-0.952), respectively. For osteopenia group, the corresponding AUC(95% CI) were 0.625 (0.467-0.783), 0.696 (0.547-0.845), 0.706 (0.563-0.848), 0.816 (0.702-0.930), and 0.820 (0.710-0.930). The DeLong test showed that the vertebral model for identifying the potential vertebral fracture risk in osteoporosis group had better performance than the paraspinal muscle model ( Z=3.28, P=0.001). While for osteopenia group, there was no significant difference in diagnostic performance between the vertebral model and the paraspinal muscle model ( Z=0.09, P=0.932). The recognition efficacy of the clinical model and the vertebral-paraspinal muscle model was significantly different ( Z=3.69, 1.98; P<0.001, P=0.047), while there was no significant difference between the clinical-radiomics combined model and the vertebral-paraspinal muscle model ( Z=1.51, 0.12; P=0.131, 0.904). Conclusion:The MRI-based vertebral-paraspinal muscle radiomics model can effectively identify osteoporosis or osteopenia patients with potential fragility fracture risk. In osteopenia group, the efficacy of the MRI radiomics models based on the vertebra and paraspinal muscles in identifying potential vertebral fragility fracture risk is comparable.

Objective:To explore the application value of radiomics models based on MRI of vertebrae and paravertebral muscles in identifying potential vertebral fragility fracture risk in osteoporosis and osteopenia.Methods:This cross-sectional study collected data from patients who underwent both dual-energy X-ray absorptiometry (DXA) and lumbar MRI at the Third Affiliated Hospital of Southern Medical University between January 2014 and December 2023,retrospectively. Based on DXA results, patients were categorized into osteoporosis group ( n=302) and osteopenia group ( n=264), with fracture and non-fracture patients matched at 1∶1 ratio by propensity score matching based on age, gender, and body mass index. The fourth lumbar vertebra was selected as the region of interest (ROI) for the vertebral body, and the bilateral psoas major, erector spinae, and multifidus muscles were selected as the ROIs for the paraspinal muscles. A total of 7 259 radiomics features were extracted from these ROIs. The dataset was divided into a training set and a test set in an 8∶2 ratio by simple random sampling (osteoporosis group 241 and 61 cases, osteopenia group 211 and 53 cases). The T-score was used to establish the clinical model. After feature normalization and dimensionality reduction, logistic regression was applied to build three radiomics models: vertebral model, paraspinal muscle model, and vertebral-paraspinal muscle model. The T-score was then combined with the radiomics model that achieved the highest area under the receiver operating characteristic curve (AUC) in the test set to construct a clinical-radiomics combined model. Model performance was evaluated using the AUC. The DeLong test was used to compare the diagnostic efficacy between models. Results:In the test set, the vertebral-paravertebral muscle model achieved the highest AUC among radiomics models and was selected for combination with the T-score. In identifying potential vertebral fragility fractures of osteoporosis group, the AUC (95% CI) of the clinical model, vertebral model, paraspinal muscle model, vertebral-paraspinal muscle model, and clinical-radiomics model were 0.523 (0.373-0.672), 0.869 (0.779-0.959), 0.608 (0.464-0.752), 0.876 (0.791-0.961), and 0.860 (0.769-0.952), respectively. For osteopenia group, the corresponding AUC(95% CI) were 0.625 (0.467-0.783), 0.696 (0.547-0.845), 0.706 (0.563-0.848), 0.816 (0.702-0.930), and 0.820 (0.710-0.930). The DeLong test showed that the vertebral model for identifying the potential vertebral fracture risk in osteoporosis group had better performance than the paraspinal muscle model ( Z=3.28, P=0.001). While for osteopenia group, there was no significant difference in diagnostic performance between the vertebral model and the paraspinal muscle model ( Z=0.09, P=0.932). The recognition efficacy of the clinical model and the vertebral-paraspinal muscle model was significantly different ( Z=3.69, 1.98; P<0.001, P=0.047), while there was no significant difference between the clinical-radiomics combined model and the vertebral-paraspinal muscle model ( Z=1.51, 0.12; P=0.131, 0.904). Conclusion:The MRI-based vertebral-paraspinal muscle radiomics model can effectively identify osteoporosis or osteopenia patients with potential fragility fracture risk. In osteopenia group, the efficacy of the MRI radiomics models based on the vertebra and paraspinal muscles in identifying potential vertebral fragility fracture risk is comparable.

Purpose To explore the ability of proton density fat fraction(PDFF)and decay constant T2* values in MRI to reflect skeletal muscle aging.Materials and Methods 3T MRI data of skeletal muscle in the middle thigh of 211 healthy adults from the Third Affiliated Hospital of Southern Medical University from August to December 2023 were prospectively collected.Gender,age,height,weight and body mass index(BMI)were recorded.PDFF value and T2* value of thigh skeletal muscle were measured at post-processing workstation,and statistical differences among different age,gender and BMI groups were analyzed.The correlation between PDFF value and T2* value of thigh skeletal muscle and age and BMI was analyzed.Results There were statistically significant differences in PDFF values of thigh skeletal muscle among different age groups(H=18.476-85.619,all P<0.01).There were significantly differences in T2*values of the left and right quadriceps muscles,hamstrings and adductors among different age groups(H=13.342-47.566,all P<0.05).There were statistically significant differences in the PDFF values of right quadriceps,left and right hamstring,adductor and sartor muscles between male and female groups(Z=-4.929--1.626,all P<0.05),while there were statistically significant differences in T2* values of left sartor muscle(Z=-2.971,P=0.003).There was no statistical significance in PDFF value of skeletal muscle of thigh in different BMI groups(P>0.05),but there were statistically significant differences in T2* value of left and right quadriceps muscle,hamstring muscle and adductor muscle(H=9.542-24.495,all P<0.05).There was a moderate positive correlation between age and PDFF value of thigh skeletal muscle(r=0.635,P<0.01),but a slight negative correlation with T2* value of left and right quadriceps,hamstring and sarcoleus(r=-0.451--0.189,all P<0.01).There was a slight positive correlation between BMI and T2* values of thigh skeletal muscle(r=0.317,P<0.01).There was a moderate negative correlation between the PDFF value and T2* value of all thigh skeletal muscles(r=-0.749--0.624,P<0.01).The PDFF and T2* values of the front and back thigh muscles(quadriceps,hamstring)were most significantly correlated with age and BMI.Conclusion PDFF based on MRI can reflect the age-related changes in the microenvironment of thigh skeletal muscle,and is a potential imaging biological marker for accurate and non-invasive quantitative evaluation of thigh skeletal muscle aging.

Objective To determine whether ultrasound (US) exposure combined with microbubble destruction could be used to enhance non-viral gene delivery in rat C6 glioma cells. Methods Microbubbles were prepared and gently mixed with plasmid DNA. The mixture of the DNA and microbubbles was administered to cultured C6 cells under different US/microbubble conditions. US parameters adopted in this study were frequency 1 MHz, output intensity 1 W/cm2, duty cycle 20%, exposure time 30 seconds. Transfection efficiency and cell viability were assessed by FACS analysis, confocal laser scanning microscopy, and Try pan blue staining. Results Microbubble with US exposure could significantly enhance the reporter gene expression as compared with other groups. No statistical significant difference was observed in the glioma cell viability between different groups. Conclusions US-mediated microbubble destruction has the potential to promote safe and efficient gene transfer into C6 cells,and it may be useful for safe clinical gene therapy of brain cancer without a viral vector system.

Objective To study the accuracy of an ultrasonographic characteristic diagnostic score system(UCDSS) which was used to classify the breast solid nodular lesions. Methods UCDSS were established by analyzing the ultrasonographic sign of the 205 breast solid nodular lesions. These lesions were classified according to the total scores of each ultrasonographie sign. ROC curve was used to evaluate the value of UCDSS. Results The area under ROC curve of UCDSS was 0. 977, the sensitivity and specificity was 92.0%, 92.3% respectively. Conclusions The classification based on the UCDSS may increase the diagnostic accuracy of the differentiation between benign and malignant breast solid nodular lesions.