ABSTRACT
BACKGROUND: MRI has high sensitivity to thoracic myelopathy, which can assess the spinal cord injury by morphology and magnitude of cervical spinal cord compression. Additionally, it is a valuable tool for the prognosis evaluation of thoracic spinal stenosis. OBJECTIVE: To explore the value of quantitative MRI T2WI parameters in predicting surgical outcome of thoracic ossification of the ligamentum flavum, and to establish the prediction model of poor prognosis, so as to provide reference for prognosis evaluation. METHODS: From January 2010 to January 2019 at Cangzhou Central Hospital, clinical and imaging data of 87 cases of thoracic ossification of the ligamentum flavum treated by thoracic laminectomy were reviewed retrospectively. According to the JOA recovery rate at 6-month follow-up, the patients were divided into good recovery group (≥ 50%) and poor recovery group (< 50%). Age, sex, duration of disease, JOA score, Sato type of ossification, maximum spinal cord compression, cross-sectional area, distribution of hyperintense signal, signal intensity ratio, intramedullary signal size, local kyphosis, kyphosis correction, number of decompressed levels and incidence of cerebrospinal fluid were compared between two groups. Univariate analysis was used to analyze indicators with significant differences. Receiver operating characteristic curve was plotted to analyze prognosis. Areas under the curve and cut-off values were recorded. The independent predictors of poor recovery were estimated through multivariate logistic regression analysis and the prediction model was established. RESULTS AND CONCLUSION: (1) The duration of disease, JOA score, maximum spinal cord compression, cross-sectional area, signal intensity ratio and intramedullary signal size showed significant difference between good recovery and poor recovery groups (P < 0.05). (2) Receiver operating characteristic curve analysis showed that the area under the curve of the duration of disease, JOA score, maximum spinal cord compression, cross-sectional area, signal intensity ratio and intramedullary signal size was 0.670, 0.733, 0.647, 0.715, 0.753 and 0.765 respectively. The cut-off value was duration of 13 months, score 4, 29.8%, 0.25 cm2, 1.593 and 13.64 mm respectively. The duration of disease and maximum spinal cord compression had low discrimination power (the area under the curve < 0.7) in predicting poor recovery, whereas the JOA score, cross-sectional area, signal intensity ratio and intramedullary signal size had moderate discrimination power (the area under the curve 0.7-0.9). The area under the curve indicates good ability of signal intensity ratio and intramedullary signal size in combination (the area under the curve=0.791). (3) Logistic multivariate regression analysis showed that JOA score, cross-sectional area and combination of signal intensity ratio and intramedullary signal size were independent risk factors of poor recovery. A predicting model was built according to the result of the logistic regression analysis. It was shown that the area under the curve of this model was 0.890, which was significantly higher than that of the JOA score, cross-sectional area and combination of signal intensity ratio and intramedullary signal size (P < 0.05). (4) Combination of signal intensity ratio and intramedullary signal size had higher predictive ability than other MRI parameters. JOA score, together with quantitative MRI T2WI parameters may have a better predictive value for the risk of poor recovery in patients with thoracic ossification of the ligamentum flavum.
ABSTRACT
Objective@#To study the lumber spine imaging process of dual-energy X-ray absorptiometry (DXA) and parameters used to optimize the image quality.@*Methods@#A computational voxel phantom was constructed from patient computed tomography (CT) data. Using the Monte Carlo radiation transport method, a dual energy x-ray beam was simulated to scan the phantom of lumbar spine to generate a bone density image. The Figure of Merit (FOM) of each image was claculated. Parameters including the combination of the high and low energy tube voltage, the thickness of Cu filter, and the ratio of two beam energy incident photon number were optimized, which based on FOM.@*Results@#FOM reaches a minimum of 1.59 × 10-2 with the tube voltage combination of 75 and 200 kVp. With the thickness of the Cu filter from 0 mm to 3 mm, FOM decreases from 6.30×10-2 to 1.87×10-2, showing a gradually slow-down trend. With the incident photon number ratio (low energy/high energy) increasing from 1∶3 to 19∶1, FOM decreases firstly and then increases, reaching a minimum of 1.40×10-2 at 3∶1.@*Conclusions@#According to the simulation results, the combinations of low tube voltage from 70 kVp to 85 kVp and high tube voltage from 160 kVp to 200 kVp, 0.3 mm Cu filter and beam incident photon number ratio from 1 to 5 can yield the best lumbar spine image quality with the lowest patient dose.
ABSTRACT
OBJECTIVE: To develop an algorithm to measure the dimensions of an airway oriented obliquely on a volumetric CT, as well as assess the effect of the imaging parameters on the correct measurement of the airway dimension. MATERIALS AND METHODS: An airway phantom with 11 poly-acryl tubes of various lumen diameters and wall thicknesses was scanned using a 16-MDCT (multidetector CT) at various tilt angles (0, 30, 45, and 60degree). The CT images were reconstructed at various reconstruction kernels and thicknesses. The axis of each airway was determined using the 3D thinning algorithm, with images perpendicular to the axis being reconstructed. The luminal radius and wall thickness was measured by the full-width-half-maximum method. The influence of the CT parameters (the size of the airways, obliquity on the radius and wall thickness) was assessed by comparing the actual dimension of each tube with the estimated values. RESULTS: The 3D thinning algorithm correctly determined the axis of the oblique airway in all tubes (mean error: 0.91 +/- 0.82degree). A sharper reconstruction kernel, thicker image thickness and larger tilt angle of the airway axis resulted in a significant decrease of the measured wall thickness and an increase of the measured luminal radius. Use of a standard kernel and a 0.75-mm slice thickness resulted in the most accurate measurement of airway dimension, which was independent of obliquity. CONCLUSION: The airway obliquity and imaging parameters have a strong influence on the accuracy of the airway wall measurement. For the accurate measurement of airway thickness, the CT images should be reconstructed with a standard kernel and a 0.75 mm slice thickness.