Identification of Blood Vessels in Cortical Bone Pores utilizing DCE-MRI and HR-pQCT
- Author(s): Gibbons, Matthew;
- Advisor(s): Kazakia, Galateia;
- et al.
Purpose: Various diseases, such as Type II Diabetes (T2D), impact the microarchitecture of bones. DCE-MRI and HR-pQCT have been used to investigate cortical bone structural changes and to understand the role blood vessels have in the development of T2D-related pathological porosity in cortical bone. The purpose of this project was to look at the cortical bone porosity, and determine the contents of the pores (whether or not they had affiliated blood vessels), so as to better understand the mechanisms driving pathological bone porosity. To this end, image processing routines were developed to quantify bone porosity and to determine blood vessel location and volume fraction.
Methods: Results from an existing DCE-MRI and HR-pQCT image processing protocol (pipeline 1) were used in this study. As part of pipeline 1, the MRI and CT images underwent a global rigid registration. Pipeline 1 also provided masks for the CT cortical bone, the blood vessels, and the bone pores. Image pipeline 2 was developed through this thesis project. Pipeline 2 has a set of threshold and dilation/erosion steps to create a mask for the MRI cortical bone. It also performs non-rigid registration of bone masks and completes registration of vessels to pores with a piecewise rigid algorithm. The overlap of the registered vessel mask with the pore mask determines the final properties and biomarkers of the blood vessel network. Numerical and physical phantoms were used to characterize the algorithms by allowing a comparison to ground truth.
Results: In bone data artifacts are visible in the MRI bone masks. The imaging pipeline overcomes these and increases MRI to CT bone Dice coefficients from 0.8 to 0.9. Blood vessel alignment, as defined by vessel voxel overlap with pore voxels, is improved with vessel overlap increasing from 20% to 90%, and vessel voxel overlap increasing from 8% to 40%. Analysis of 12 distal and ultra-distal tibia data sets did not show a statistically significant difference between normal and T2D patients (mean ~ 0.3% for each with p = 0.4). A numerical phantom study provided metrics for pipeline 2 success. It indicated that after pipeline 1 offsets between vessels and pores should be < 15 voxels and pore densities should be < 20%. A physical phantom study showed capability to identify vessels with diameters as small as 300 um.
Conclusions: Positive results with bone and phantom data indicate a proof of concept for the general approach as well as the implemented algorithms. Pipeline 2 achieved high alignment fractions for blood vessels and pores. The analysis of both bone and phantom data has led to the definition of metrics and identification of specific algorithm deficiencies. Future work on these items should result in a robust image analysis pipeline for most data sets and a set of useful metrics to distinguish problematic data sets. These algorithm improvements along with analysis of more data sets will be needed to ascertain whether there are statistically significant differences between populations for cortical bone vessel densities.