Automated Quantitative Analysis of Bone Stability and Tumour Burden in the Metastatic Rat Spine

The spine is the most common location of metastatic disease in the skeleton. The occurrence of bone metastasis can lead to severe clinical consequences and a significant decline in quality of life. The evaluation of metastatic disease in the spine has to date been mainly qualitative. More widespread access to multiple imaging modalities has motivated the development of 3D methods to quantitatively evaluate metastatic disease in the spine. Quantitative evaluation is important both in assessing stability of the metastatic spine and the progression/ response of the tumour and bone to treatment over time. Previous studies quantifying stability in the metastatic spine have focused primarily on osteolytic tumours. Local and systemic treatments have impacted the nature of vertebral metastasis, increasing the occurrence of mixed osteolytic and osteoblastic disease. Thus, it is important to focus analyses on models able to accurately represent diverse distribution patterns found in bony metastasis. Preclinical models are widely used in studying the process of metastasis and are able to represent both osteolytic and osteoblastic disease. This proposal aims to establish the biomechanical implications of metastatic disease in the spine through the evaluation of stability and tumour burden in a preclinical model using a multifaceted engineering-based approach. It is hypothesized that the use of automated analysis techniques applied to multimodality imaging will allow quantification of the impact of metastasis on biomechanical stability, tumour burden and bony architecture in the spine, and motivate prediction models that accurately reflect vertebral integrity in both osteolytic and mixed osteolytic/osteoblastic models of spinal metastasis. Specifically, this work aims to: 1) Utilize and compare μMR and μCT based radiologic methods to quantify tumour involvement and vertebral architecture in a rat model of spinal metastasis; and 2) Evaluate the ability of 2D, 3D, and continuum based methods to quantify structural integrity in vertebral metastasis. Overall, this work will focus on developing automated methods to quantify stereologic parameters, and quality in the metastatic spine and the evaluation of stability measures from 2D structural rigidity, Finite Element analysis, image registration and experimental methods. Ultimately this work will yield automated analysis techniques and evaluate the abilities of these methods to predict failure in metastatic vertebrae.Ph

Automated quantitative microstructural analysis of metastatically involved vertebrae: effects of stereologic model and spatial resolution

Summary of background data: Preclinical models of spinal metastases allow for the application of micro-image based structural assessments, however, large data sets resulting from high resolution scanning motivate a need for robust automated analysis tools. Accurate assessment of changes in vertebral architecture, however, may depend both on the resolution of images acquired and the models used to represent the structural data. Objective: To apply a recently developed automated μCT based analysis tool to quantify the effect of diffuse metastatic disease on rat vertebral architecture at multiple resolutions. It was hypothesized that automated methods could accurately quantify differences in vertebral microstructure and that diffuse metastatic disease could be shown to have significant negative architectural effects on trabecular bone independent of stereologic model and resolution. Methods: μCT images acquired at 14 μm3 of healthy and metastatcially involved whole lumbar rat vertebrae were analyzed at high, medium and low (8.725, 17.45, and 34.9 μm3) resolutions using an automated algorithm to yield micro-structural measures of the trabecular centrum and cortical shell. The images analyzed at different resolutions were obtained via up/downsampling of the acquired image data. Trabecular thickness was evaluated with the Parfitt and Hildebrand models, and anisotropy was evaluated through calculation of mean intercept length. Results: Significant differences in microstructural parameters measured in comparing healthy and metastatically involved vertebrae were affected by resolution, however, relative anisotropy was maintained. The Parfitt and Hilderbrand models yielded similar structural differences between healthy and metastatic vertebrae, however, the Hildebrand model was limited due to segmentation accuracy required for its automated application. Conclusions: Differences in microstructural parameters generated through automated analysis at high resolution suggest that diffuse MT1 osteolytic destruction in whole rat vertebrae results primarily in loss of trabeculae in the metastatic vertebrae, as opposed to trabecular thinning. The sensitivity of the bony architectural parameters to resolution motivates the need for high resolution scanning or post-processing of images

Micro-computed tomography-based highly automated 3D segmentation of the rat spine for quantitative analysis of metastatic disease

Copyright for the article is held by the journal's parent society, the American Association of Neurological Surgeons.Noninvasive evaluation of metastatic disease in the spine has generally been limited to 2D qualitative or semiquantitative analysis techniques. This study aims to develop and evaluate a highly automated micro-CT-based quantitative analysis tool that can measure the architectural impact of metastatic involvement in whole vertebrae. Micro-CT analysis of rat whole vertebrae was conducted using a combination of demons deformable registration, level set curvature evolution, and intensity based thresholding techniques along with upsampling and edge enhancement techniques. The algorithm was applied to 6 lumbar vertebrae (L1-3) from 6 rnu/rnu rats (3 healthy rats and 3 with metastatic involvement). Osteolytic metastatic involvement was modeled via MT1 human breast cancer cells. Excellent volumetric concurrency was achieved in comparing the automated micro-CT-based segmentations of the whole vertebrae, trabecular centrums, and individual trabecular networks to manual segmentations (98.9%, 96.1%, and 98.3%, respectively; 6 specimens), and the automated segmentations were achieved in a fraction of the time. The algorithm successfully accounted for discontinuities in the cortical shell caused by vasculature and osteolytic destruction. As such, this work demonstrates the potential of this highly automated segmentation tool to permit rapid precise quantitative structural analysis of the spine with minimum user interaction in the analysis of both healthy and pathological (metastatically involved) vertebrae. Future optimization and the incorporation of lower-resolution imaging parameters may allow automated analysis of clinical CT-based measures in addition to preclinical micro-CT-based analyses of the structural impact and progression of pathological processes in the spine

Multimodal μCT/μMR based semiautomated segmentation of rat vertebrae affected by mixed osteolytic/osteoblastic metastases

Purpose: Multimodal microimaging in preclinical models is used to examine the effect of spinal metastases on bony structure; however, the evaluation of tumor burden and its effect on microstructure has thus far been mainly qualitative or semiquantitative. Quantitative analysis of multimodality imaging is a time consuming task, motivating automated methods. As such, this study aimed to develop a low complexity semiautomated multimodal μCT/μMR based approach to segment rat vertebral structure affected by mixed osteolytic/osteoblastic destruction. Methods: Mixed vertebral metastases were developed via intracardiac injection of Ace-1 canine prostate cancer cells in three 4-week-old rnu/rnu rats. μCT imaging (for high resolution bone visualization), T1-weighted μMR imaging (for bone registration), and T2-weighted μMR imaging (for osteolytic tumor visualization) were conducted on one L1, three L2, and one L3 vertebrae (excised). One sample (L1–L3) was processed for undecalcified histology and stained with Goldner's trichome. The μCT and μMR images were registered using a 3D rigid registration algorithm with a mutual information metric. The vertebral microarchitecture was segmented from the μCT images using atlas-based demons deformable registration, levelset curvature evolution, and intensity-based thresholding techniques. The μCT based segmentation contours of the whole vertebrae were used to mask the T2-weighted μMR images, from which the osteolytic tumor tissue was segmented (intensity-based thresholding). Results: Accurate registration of μCT and μMRI modalities yielded precise segmentation of whole vertebrae, trabecular centrums, individual trabeculae, and osteolytic tumor tissue. While the algorithm identified the osteoblastic tumor attached to the vertebral pereosteal surfaces, it was limited in segmenting osteoblastic tissue located within the trabecular centrums. Conclusions: This semiautomated segmentation method yielded accurate registration of μCT and μMRI modalities with application to the development of mathematical models analyzing the mechanical stability of metastatically involved vertebrae and in preclinical applications evaluating new and existing treatment effects on tumor burden and skeletal microstructure

Can micro-imaging based analysis methods quantify structural integrity of rat vertebrae with and without metastatic involvement?

This study compares the ability of μCT image-based registration, 2D structural rigidity analyses and multimodal continuum-level finite element (FE) modeling in evaluating the mechanical stability of healthy, osteolytic, and mixed osteolytic/osteoblastic metastatically involved rat vertebrae. μMR and μCT images (loaded and unloaded) were acquired of lumbar spinal motion segments from 15rnu/rnu rats (five per group). Strains were calculated based on image registration of the loaded and unloaded μCT images and via analysis of FE models created from the μCT and μMR data. Predicted yield load was also calculated through 2D structural rigidity analysis of the axial unloaded μCT slices. Measures from the three techniques were compared to experimental yield loads. The ability of these methods to predict experimental yield loads were evaluated and image registration and FE calculated strains were directly compared. Quantitatively for all samples, only limited weak correlations were found between the image-based measures and experimental yield load. In comparison to the experimental yield load, we observed a trend toward a weak negative correlation with median strain calculated using the image-based strain measurement algorithm (r=-0.405, p=0.067), weak significant correlations (p<0.05) with FE based median and 10th percentile strain values (r=-0.454, -0.637, respectively), and a trend toward a weak significant correlation with FE based mean strain (r=-0.366, p=0.09). Individual group analyses, however, yielded more and stronger correlations with experimental results. Considering the image-based strain measurement algorithm we observed moderate significant correlations with experimental yield load (p<0.05) in the osteolytic group for mean and median strain values (r=-0.840, -0.832, respectively), and in the healthy group for median strain values (r=-0.809). Considering the rigidity-based predicted yield load, we observed a strong significant correlation with the experimental yield load in the mixed osteolytic/osteoblastic group (r=0.946) and trend toward a moderate correlation with the experimental yield load in the osteolytic group (r=0.788). Qualitatively, strain patterns in the vertebral bodies generated using image registration and FEA were well matched, yet quantitatively a significant correlation was found only between mean strains in the healthy group (r=0.934). Large structural differences in metastatic vertebrae and the complexity of motion segment loading may have led to varied modes of failure. Improvements in load characterization, material properties assignments and resolution are necessary to yield a more generalized ability for image-based registration, structural rigidity and FE methods to accurately represent stability in healthy and pathologic scenarios