Proton Magnetic Resonance Spectroscopy-Detected Changes of Marrow Fat Content in a Rabbit Model of Osteoporosis Treated With Epigallocatechin-3-Gallate

Application of Medical Imaging Based on Deep Learning in the Treatment of Lumbar Degenerative Diseases and Osteoporosis with Bone Cement Screws

Objective

To explore the application value of magnetic resonance spectroscopy (MRS) and GSI-energy spectrum electronic computed tomography (CT) medical imaging based on the deep convolutional neural network (CNN) in the treatment of lumbar degenerative disease and osteoporosis.

Methods

There were 56 cases of suspected lumbar degenerative disease and osteoporosis. A group of 56 subjects were examined using 1.5 TMR spectrum (MRS) and dual-energy X-ray absorptiometry (DXA) to collect the lumbar L3 vertebral body fat ratio (FF) and L1~4 vertebral bone mineral density (BMD) value. We divided the subjects into 2 groups with T value -2.5 as the critical point. Set T value > -2.5 as the negative group and T value ≤ -2.5 as the positive group. Pearson's method is used for FF-MRS and BMD correlation analyses. A group of all patients underwent GSI-energy spectrum CT scan, and X-ray bone mineral density (DXA) test results (bone density per unit area) were used as the gold standard to analyze the diagnosis of osteoporosis by the GSI-energy spectrum CT scan method value.

Results

The differences in FF and BMD between the negative group and the positive group were statistically significant (P < 0.01), and there was a highly negative correlation between the average value of FF and BMD. 30 cases were diagnosed as osteoporosis by DXA. The accuracy of GSI-energy spectrum CT medical imaging in diagnosing osteoporosis is 89.30%. The GSI-energy spectrum CT diagnosis of osteoporosis and DXA examination results have good consistency.

Conclusion

Based on the deep convolutional neural network (CNN) MRS technology, GSI-energy spectrum CT medical imaging is used in the clinical diagnosis and treatment of lumbar degenerative lesions and osteoporosis. It has a good advantage in assessing bone quality and has good consistency with DXA examination and has better application value high.

Knee subchondral bone perfusion and its relationship to marrow fat and trabeculation on multi-parametric MRI and micro-CT in experimental CKD

The pathogenesis of chronic kidney disease (CKD) is multifactorial. In the progression of CKD arthropathy, arteriosclerosis may alter the knee subchondral bone marrow by altering blood flow through the bone vasculature. Herein, multi-parametric MRI assessment, including dynamic contrast enhanced magnetic resonance imaging (DCE-MRI), magnetic resonance spectroscopy (MRS), MRI T2*, contrast enhanced MR angiography (CE-MRA), and micro-CT were applied in a rodent nephrectomy model to: 1) investigate the blood perfusion of subchondral bone marrow and its relationship to fat water content and trabeculation pattern in CKD and 2) demonstrate the feasibility of using multi-parametric MRI parameters as imaging biomarkers to evaluate the disease’s progression. Two groups of rats in our study underwent either 1) no intervention or 2) 5/6 nephrectomy. We found that in the CKD group, perfusion amplitude A and elimination constant k_el values were significantly decreased, and vascular permeability k_ep was significantly increased. MRS showed that fat fraction (FF) was significantly lower, water fraction (WF) was significantly higher in the CKD group. Micro-CT showed a significant loss of trabecular bone. Knee subchondral bone marrow perfusion deficiency in experimental CKD may be associated with decreased fat content, increased water content, and sparse trabeculation.