Research

A Novel Two-Compartment Model for Calculating Bone Volume Fractions and Bone Mineral Densities From Computed Tomography Images

May 5, 2017

Osteoporosis is a disease characterized by a degradation of bone structures. Various methods have been developed to diagnose osteoporosis by measuring bone mineral density (BMD) of patients. However, BMDs from these methods were not equivalent and were incomparable. In addition, partial volume effect introduces errors in estimating bone volume from computed tomography (CT) images using image segmentation. In this study, a two-compartment model (TCM) was proposed to calculate bone volume fraction (BV/TV) and BMD from CT images. The TCM considers bones to be composed of two sub-materials. Various equivalent BV/TV and BMD can be calculated by applying corresponding sub-material pairs in the TCM. In contrast to image segmentation, the TCM prevented the influence of the partial volume effect by calculating the volume percentage of sub-material in each image voxel. Validations of the TCM were performed using bone-equivalent uniform phantoms, a 3D-printed trabecular-structural phantom, a temporal bone flap, and abdominal CT images. By using the TCM, the calculated BV/TVs of the uniform phantoms were within percent errors of ±2%; the percent errors of the structural volumes with various CT slice thickness were below 9%; the volume of the temporal bone flap was close to that from micro-CT images with a percent error of 4.1%. No significant difference (p > 0.01) was found between the areal BMD of lumbar vertebrae calculated using the TCM and measured using dual-energy X-ray absorptiometry. In conclusion, the proposed TCM could be applied to diagnose osteoporosis, while providing a basis for comparing various measurement methods.

Converting Computed Tomography Images into Photon Interaction Coefficients by using Stoichiometric Calibration and Parametric Fit Models

February 1, 2017

Purpose: X-ray and gamma-ray are widely applied in radiology, radiotherapy, and nuclear medicine. Linear attenuation coefficients and linear energy absorption coefficients are essential for dose calculation and image correction. In this study, a method that entails combining the stoichiometric calibration and parametric physical models was developed to convert computed tomography (CT) images into the linear attenuation coefficients and linear energy absorption coefficients.

Methods: A calibration scan was performed using standard tissue-equivalent materials to obtain the characteristics of the x-ray energy spectrum. Subsequently, relationships between CT numbers and tissue parameters were established using standard soft tissue and bone tissue data adopted from the literature. The linear attenuation coefficient and linear energy absorption coefficient were calculated using the parametric fit model.

Results: The results showed a linear relationship between CT numbers and tissue parameters. The tissue-equivalent materials differed from real human tissues, leading to considerable errors in estimation of mass attenuation coefficients when the photon energy was lower than 50 keV. Mass ttenuation coefficients and mass energy transfer coefficients of five tissues were calculated and validated using clinical CT images. The error was less than 5% and  8%, compared with the values of the International Commission on Radiation Units (ICRU) 46 report.

Conclusions: The probability of photon interaction with tissues and physical characteristics of tissues can be accurately evaluated by using the proposed method and applied in various clinical applications.