Bone turnover and the accumulation of microdamage are impacted by the presence of skeletal metastases which can contribute to increased fracture risk. Treatments for metastatic disease may further impact bone quality. The present study aims to establish a preliminary understanding of microdamage accumulation and load to failure in osteolytic vertebrae following stereotactic body radiotherapy (SBRT), zoledronic acid (ZA), or docetaxel (DTX) treatment.

Twenty-two six-week old athymic female rats (Hsd:RH-Foxn1rnu, Envigo, USA) were inoculated with HeLa cervical cancer cells through intracardiac injection (day 0). Institutional approval was obtained for this work and the ARRIVE guidelines were followed. Animals were randomly assigned to four groups: untreated (n=6), spine stereotactic body radiotherapy (SBRT) administered on day 14 (n=6), zoledronic acid (ZA) administered on day 7 (n=5), and docetaxel (DTX) administered on day 14 (n=5). Animals were euthanized on day 21. T13-L3 vertebral segments were collected immediately after sacrifice and stored in −20°C wrapped in saline soaked gauze until testing. µCT scans (µCT100, Scanco, Switzerland) of the T13-L3 segment confirmed tumour burden in all T13 and L2 vertebrae prior to testing. T13 was stained with BaSO₄ to label microdamage. High resolution µCT scans were obtained (90kVp, 44uA, 4W, 4.9µm voxel size) to visualize stain location and volume. Segmentations of bone and BaSO₄ were created using intensity thresholding at 3000HU (~736mgHA/cm³) and 10000HU (~2420mgHA/cm³), respectively. Non-specific BaSO₄ was removed from the outer edge of the cortical shell by shrinking the segmentation by 105mm in 3D. Stain volume fraction was calculated as the ratio of BaSO₄ volume to the sum of BaSO₄ and bone volume. The L1-L3 motion segments were loaded under axial compression to failure using a µCT compatible loading device (Scanco) and force-displacement data was recorded. µCT scans were acquired unloaded, at 1500µm displacement and post-failure. Stereological analysis was performed on the L2 vertebrae in the unloaded µCT scans. Differences in mean stain volume fraction, mean load to failure, and mean bone volume/total volume (BV/TV) were compared between treatment groups using one-way ANOVAs. Pearson's correlation between stain volume fraction and load to failure by treatment was calculated using an adjusted load to failure divided by BV/TV.

Stained damage fraction was significantly different between treatment groups (p=0.0029). Tukey post-hoc analysis showed untreated samples to have higher stain volume fraction (16.25±2.54%) than all treatment groups (p<0.05). The ZA group had the highest mean load to failure (195.60±84.49N), followed by untreated (142.33±53.08N), DTX (126.60±48.75N), and SBRT (95.50±44.96N), but differences did not reach significance (p=0.075). BV/TV was significantly higher in the ZA group (49.28±3.56%) compared to all others. The SBRT group had significantly lower BV/TV than the untreated group (p=0.018). Load divided by BV/TV was not significantly different between groups (p=0.24), but relative load to failure results were consistent (ZA>Untreated>DTX>SBRT). No correlations were found between stain volume fraction and load to failure.

Focal and systemic cancer treatments effect microdamage accumulation and load to failure in osteolytic vertebrae. Current testing of healthy controls will help to further separate the effects of the tumour and cancer treatments on bone quality.

Bone turnover and the accumulation of microdamage are impacted by the presence of skeletal metastases which can contribute to increased fracture risk. Treatments for metastatic disease may further impact bone quality. The present study aims to establish a preliminary understanding of microdamage accumulation and load to failure in osteolytic vertebrae following stereotactic body radiotherapy (SBRT), zoledronic acid (ZA), or docetaxel (DTX) treatment.

Twenty-two six-week old athymic female rats (Hsd:RH-Foxn1rnu, Envigo, USA) were inoculated with HeLa cervical cancer cells through intracardiac injection (day 0). Institutional approval was obtained for this work and the ARRIVE guidelines were followed. Animals were randomly assigned to four groups: untreated (n=6), spine stereotactic body radiotherapy (SBRT) administered on day 14 (n=6), zoledronic acid (ZA) administered on day 7 (n=5), and docetaxel (DTX) administered on day 14 (n=5). Animals were euthanized on day 21. T13-L3 vertebral segments were collected immediately after sacrifice and stored in −20°C wrapped in saline soaked gauze until testing. µCT scans (µCT100, Scanco, Switzerland) of the T13-L3 segment confirmed tumour burden in all T13 and L2 vertebrae prior to testing. T13 was stained with BaSO₄ to label microdamage. High resolution µCT scans were obtained (90kVp, 44uA, 4W, 4.9µm voxel size) to visualize stain location and volume. Segmentations of bone and BaSO₄ were created using intensity thresholding at 3000HU (~736mgHA/cm³) and 10000HU (~2420mgHA/cm³), respectively. Non-specific BaSO₄ was removed from the outer edge of the cortical shell by shrinking the segmentation by 105mm in 3D. Stain volume fraction was calculated as the ratio of BaSO₄ volume to the sum of BaSO₄ and bone volume. The L1-L3 motion segments were loaded under axial compression to failure using a µCT compatible loading device (Scanco) and force-displacement data was recorded. µCT scans were acquired unloaded, at 1500µm displacement and post-failure. Stereological analysis was performed on the L2 vertebrae in the unloaded µCT scans. Differences in mean stain volume fraction, mean load to failure, and mean bone volume/total volume (BV/TV) were compared between treatment groups using one-way ANOVAs. Pearson's correlation between stain volume fraction and load to failure by treatment was calculated using an adjusted load to failure divided by BV/TV.

Stained damage fraction was significantly different between treatment groups (p=0.0029). Tukey post-hoc analysis showed untreated samples to have higher stain volume fraction (16.25±2.54%) than all treatment groups (p<0.05). The ZA group had the highest mean load to failure (195.60±84.49N), followed by untreated (142.33±53.08N), DTX (126.60±48.75N), and SBRT (95.50±44.96N), but differences did not reach significance (p=0.075). BV/TV was significantly higher in the ZA group (49.28±3.56%) compared to all others. The SBRT group had significantly lower BV/TV than the untreated group (p=0.018). Load divided by BV/TV was not significantly different between groups (p=0.24), but relative load to failure results were consistent (ZA>Untreated>DTX>SBRT). No correlations were found between stain volume fraction and load to failure.

Focal and systemic cancer treatments effect microdamage accumulation and load to failure in osteolytic vertebrae. Current testing of healthy controls will help to further separate the effects of the tumour and cancer treatments on bone quality.

Spinal stenosis is a condition resulting in the compression of the neural elements due to narrowing of the spinal canal. Anatomical factors including enlargement of the facet joints, thickening of the ligaments, and bulging or collapse of the intervertebral discs contribute to the compression. Decompression surgery alleviates spinal stenosis through a laminectomy involving the resection of bone and ligament. Spinal decompression surgery requires appropriate planning and variable strategies depending on the specific situation. Given the potential for neural complications, there exist significant barriers to residents and fellows obtaining adequate experience performing spinal decompression in the operating room. Virtual teaching tools exist for learning instrumentation which can enhance the quality of orthopaedic training, building competency and procedural understanding. However, virtual simulation tools are lacking for decompression surgery. The aim of this work was to develop an open-source 3D virtual simulator as a teaching tool to improve orthopaedic training in spinal decompression.

A custom step-wise spinal decompression simulator workflow was built using 3D Slicer, an open-source software development platform for medical image visualization and processing. The procedural steps include multimodal patient-specific loading and fusion of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) data, bone threshold-based segmentation, soft tissue segmentation, surgical planning, and a laminectomy and spinal decompression simulation. Fusion of CT and MRI elements was achieved using Fiducial-Based Registration which aligned the scans based on manually placed points allowing for the identification of the relative position of soft and hard tissues. Soft tissue segmentation of the spinal cord, the cerebrospinal fluid, the cauda equina, and the ligamentum flavum was performed using Simple Region Growing Segmentation (with manual adjustment allowed) involving the selection of structures on T1 and/or T2-weighted scans. A high-fidelity 3D model of the bony and soft tissue anatomy was generated with the resulting surgical exposure defined by labeled vertebrae simulating the central surgical incision. Bone and soft tissue resecting tools were developed by customizing manual 3D segmentation tools. Simulating a laminectomy was enabled through bone and ligamentum flavum resection at the site of compression. Elimination of the stenosis enabled decompression of the neural elements simulated by interpolation of the undeformed anatomy above and below the site of compression using Fill Between Slices to reestablish pre-compression neural tissue anatomy.

The completed workflow allows patient specific simulation of decompression procedures by staff surgeons, fellows and residents. Qualitatively, good visualization was achieved of merged soft tissue and bony anatomy. Procedural accuracy, the design of resecting tools, and modeling of the impact of bone and ligament removal was found to adequately encompass important challenges in decompression surgery.

This software development project has resulted in a well-characterized freely accessible tool for simulating spinal decompression surgery. Future work will integrate and evaluate the simulator within existing orthopaedic resident competency-based curriculum and fellowship training instruction. Best practices for effectively teaching decompression in tight areas of spinal stenosis using virtual simulation will also be investigated in future work.

Quantitative assessment of metastatic involvement of the bony spine is important for assessing disease progression and treatment response. Quantification of metastatic involvement is challenging as tumours may appear as osteolytic (bone resorbing), osteoblastic (bone forming) or mixed. This investigation aimed to develop an automated method to accurately segment osteoblastic lesions in a animal model of metastatically involved vertebrae, imaged with micro computed tomography (μCT).

Radiomics seeks to apply standardized features extracted from medical images for the purpose of decision-support as well as diagnosis and treatment planning. Here we investigate the application of radiomic-based features for the delineation of osteoblastic vertebral metastases. Osteoblastic lesions affect bone deposition and bone quality, resulting in a change in the texture of bony material physically seen through μCT imaging. We hypothesize that radiomics based features will be sensitive to changes in osteoblastic lesion bone texture and that these changes will be useful for automating segmentation.

Osteoblastic metastases were generated via intracardiac injection of human ZR-75-1 breast cancer cells into a preclinical athymic rat model (n=3). Four months post inoculation, ex-vivo μCT images (µCT100, Scanco) were acquired of each rodent spine focused on the metastatically involved third lumbar vertebra (L3) at 7µm/voxel and resampled to 34µm/voxel.

The trabecular bone within each vertebra was isolated using an atlas and level-set based segmentation approach previously developed by our group. Pyradiomics, an open source Radiomics library written in python, was used to calculate 3D image features at each voxel location within the vertebral bone. Thresholding of each radiomic feature map was used to isolate the osteoblastic lesions.

The utility of radiomic feature-based segmentation of osteoblastic bone tissue was evaluated on randomly selected 2D sagittal and axial slices of the μCT volume. Feature segmentations were compared to ground truth osteoblastic lesion segmentations by calculating the Dice Similarity Coefficient (DSC). Manually defined ground truth osteoblastic tumor segmentations on the μCT slices were informed by histological confirmation of the lesions.

The radiomic based features that best segmented osteoblastic tissue while optimizing computational time were derived from the Neighbouring Gray Tone Difference Matrix (NGTDM). Measures of coarseness yielded the best agreement with the manual segmentations (DSC=707%) followed by contrast, strength and complexity (DSC=6513%, 5428%, and 4826%, respectively).

This pilot study using a radiomic based approach demonstrates the utility of the NGTDM features for segmentation of vertebral osteoblastic lesions. This investigation looked at the utility of isolated features to segment osteoblastic lesions and found modest performance in isolation. In future work we will explore combining these features using machine learning based classifiers (i.e. decision forests, support vector machines, etc.) to improve segmentation performance.

Strain is a robust indicator of bone failure initiation. Previous work has demonstrated the measurement of vertebral trabecular bone strain by Digital Volume Correlation (DVC) of µCT scan in both a loaded and an unloaded configuration. This project aims to improve previous strain measurement methods relying on image registration, improving resolution to resolve trabecula level strain and to improve accuracy by applying feature based registration algorithms to µCT images of vertebral trabecular bone to quantify strain. It is hypothesised that extracting reliable corresponding feature points from loaded and unloaded µCT scans can be used to produce higher resolution strain fields compared to DVC techniques.

The feature based strain calculation algorithm has two steps: 1) a displacement field is calculated by finding corresponding feature points identified in both the loaded and unloaded µCT scans 2) strain fields are calculated from the displacement fields. Two methods of feature point extraction, Scale Invariant Feature Transform (SIFT) and Skeletonisation, were applied to unloaded (fixed) and loaded (moving) µCT images of a rat tail vertebra. Spatially non-uniform displacement fields were generated by automatically matching corresponding feature points in the unloaded and loaded scans. The Thin Plate Spline method and a Moving Least Squares Meshless Method were both tested for calculating strain from the displacement fields. Verification of the algorithms was performed by testing against known artificial strain/displacement fields. A uniform and a linearly varying 2% compressive strain field were applied separately to an unloaded 2D sagittal µCT slice to simulate the moving image.

SIFT was unable to reliably match identified feature points leading to large errors in displacement. Skeletonisation generated a more accurate and precise displacement field. TPS was not tolerant to small displacement field errors, which resulted in inaccurate strain fields. The Meshless Methods proved much more resilient to displacement field errors. The combination of Skeletonisation with the Meshless Method resulted in best performance with an accuracy of −405µstrain and a detection limit of 1210µstrain at a strain resolution of 221.5µm. The DVC algorithm verified using the same validation test yielded a similar detection limit (1190µstrain), but with a lower accuracy for the same test (2370µstrain) for a lower resolution strain field (770µm) (Hardisty, 2009).

The Skeletonisation algorithm combined with the Meshless Method calculated strain at a higher resolution, but with a similar detection limit, to that of traditional DVC methods. Future improvements to this method include the implementation of subpixel feature point identification and adapting this method of strain measurement into a 3D domain. Ultimately, a hybrid DVC/feature registration algorithm may further improve the ability to measure trabecular bone strain using µCT based image registration.