µCT images are commonly analysed to assess changes in bone density and architecture in preclinical murine models. Several platforms provide automated analysis of bone architecture parameters from volumetric regions of interest (ROI). However, segmentation of the regions of subchondral bone to create the volumetric ROIs remains a manual and time-consuming task. This study aimed to develop and evaluate automated pipelines for trabecular bone architecture analysis of mouse proximal tibia subchondral bone.

A segmented dataset involving 62 knees (healthy and arthritic) from 10-week male C57BL/6 mice were used to train a U-Net type architecture, with µCT scans (downsampled) input that output segmentation and bone volume density (BV/TV) of the subchondral trabecular bone. Segmentations were upsampled and used in tandem with the original scans (10µ) as input for architecture analysis along with the thresholded trabecular bone. The analysis considered the manually and U-Net segmented ROIs using two available pipelines: the ITKBoneMorphometry library and CTan (SKYSCAN). The analyses included: bone volume (BV), total volume (TV), BV/TV, trabecular number (TbN), trabecular thickness (TbTh), trabecular separation (TbSp), and bone surface density (BSBV).

There was good agreement for bone measures between the manual and U-Net pipelines utilizing ITK (R=0.88-0.98) and CTan (R=0.91-0.98). ITK and CTan showed good agreement for BV, TV, BV/TV, TbTh and BSBV (R=0.9-0.98). However, a limited agreement was seen between TbN (R=0.73) and TbSb (R=0.59) due to methodological differences in how spacing is evaluated.

This U-Net/ITK pipeline seamlessly automated both segmentation and quantification of the proximal tibia subchondral bone. This automated pipeline allows the analysis of large volumes of data, and its open-source nature may enable the standardization of stereologic analysis of trabecular bone across different research groups.

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

Forty-two 6-week old athymic female rats (Hsd:RH-Foxn1rnu, Envigo) were studied; 22 were inoculated with HeLa cervical cancer cells through intracardiac injection (day 0). Animals were randomly assigned to four groups: untreated (healthy=5, osteolytic=6), SBRT on day 14 (healthy=6, osteolytic=6), ZA on day 7 (healthy=4, osteolytic=5), and DTX on day 14 (healthy=5, osteolytic=5). Animals were euthanized on day 21. L1-L3 motion segments were compression loaded to failure and force-displacement data recorded. T13 vertebrae were stained with BaSO₄ and µCT imaged (90kVp, 44uA, 4.9µm) to visualize microdamage location and volume. Damage volume fraction (DV/BV) was calculated as the ratio of BaSO₄ to bone volume. Differences in mean load-to-failure were compared using three-way ANOVA (disease status, treatment, cells injected). Differences in mean DV/BV between treatment groups were compared using one-way ANOVA.

Treatment had a significant effect on load-to-failure (p=0.004) with ZA strengthening the healthy and osteolytic vertebrae. Reduced strength post SBRT seen in the metastatic (but not the healthy) group may be explained by greater tumor involvement secondary to higher cell injection concentrations. Untreated metastatic samples had higher DV/BV (16.25±2.54%) compared to all treatment groups (p<0.05) suggesting a benefit of treatment to bone quality.

Focal and systemic cancer treatments were shown to effect load-to-failure and microdamage accumulation in healthy and osteolytic vertebrae. Developing a better understanding of how treatments effect bone quality and mechanical stability is critical for effective management of patients with spinal metastases.

Physiotherapy is a critical element in successful conservative management of low back pain (LBP). The aim of this study was to develop and evaluate a system with wearable inertial sensors to objectively detect sitting postures and performance of unsupervised exercises containing movement in multiple planes (flexion, extension, rotation).

A set of 8 inertial sensors were placed on 19 healthy adult subjects. Data was acquired as they performed 7 McKenzie low-back exercises and 3 sitting posture positions. This data was used to train two models (Random Forest (RF) and XGBoost (XGB)) using engineered time series features. In addition, a convolutional neural network (CNN) was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed most to the models. Finally, a subset of sensor locations and channels was included in a hyperparameter grid search to identify the optimal sensor configuration and the best performing algorithm(s) for exercise classification. Models were evaluated using F1-score in a 10-fold cross validation approach.

The optimal hardware configuration was identified as a 3-sensor setup using lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XBG model achieved the highest exercise (F1=0.94±0.03) and posture (F1=0.90±0.11) classification scores. The CNN achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F1=0.94±0.02) and the accelerometer channel alone for posture classification (F1=0.91±0.03).

This study demonstrates the potential of a 3-sensor lower body wearable solution (e.g. smart pants) that can identify proper sitting postures and exercises in multiple planes, suitable for low back pain. This technology has the potential to improve the effectiveness of LBP rehabilitation by facilitating quantitative feedback, early problem diagnosis, and possible remote monitoring.

Access to health care, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure low back physiotherapy exercise participation without the direct supervision of a medical professional. The aim of this study was to develop and evaluate the potential for performing automatic, unsupervised video-based monitoring of at-home low back physiotherapy exercises using a single mobile phone camera.

24 healthy adult subjects performed seven exercises based on the McKenzie low back physiotherapy program while being filmed with two smartphone cameras. Joint locations were automatically extracted using an open-source pose estimation framework. Engineered features were extracted from the joint location time series and used to train a support vector machine classifier (SVC). A convolutional neural network (CNN) was trained directly on the joint location time series data to classify exercises based on a recording from a single camera. The models were evaluated using a 5-fold cross validation approach, stratified by subject, with the class-balanced accuracy used as the performance metric.

Optimal performance was achieved when using a total of 12 pose estimation landmarks from the upper and lower body, with the SVC model achieving a classification accuracy of 96±4% and the CNN model an accuracy of 97±2%.

This study demonstrates the feasibility of using a smartphone camera and a supervised machine learning model to effectively assess at-home low back physiotherapy adherence. This approach could provide a low-cost, scalable method for tracking adherence to physical therapy exercise programs in a variety of settings.

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.

Rodents are often used as preclinical models for investigating the biomechanical consequences of spinal pathologies and interventions. Growth plates are present within rat vertebrae throughout life and may alter the vertebral biomechanics. This study investigates the biomechanical response of rat-tail vertebrae to axial compressive loading using μCT imaging and image registration to spatially resolve strain fields.

The sixth caudal vertebrae of eight immunocompromised (rnu/rnu) rats were μCT scanned (17.5 ×17.5×17.5μm/pixel) in both loaded (27N-32N axial compression) and unloaded configurations. Image registration was used to calculate strain and displacement fields in the bone due to the applied load by finding a spatial mapping between the two scans. Strain was resolved to varying spatial resolutions; high strain gradient regions, such as the growth plates, were analyzed to higher spatial resolutions.

Axial strains calculated by image registration ranged from 2% in tension to 16% in compression with an average axial strain of 1.6% in compression. In seven rats the majority of the strain measured within the vertebrae was concentrated in the growth plate. Very soft growth plates in three specimens resulted in maximum axial strains from 10–16% in compression. The remaining four rats with strain concentrations in the growth plate had maximum axial strains ranging from 2.2%–3.2%. Centrally located strain concentrations of lower magnitudes and more limited spatial extent were observed in the trabecular bone.

The majority of the strain within the rat vertebrae was absorbed by the growth plates. The amount of strain within the growth plate is important to consider when interpreting biomechanical data on rat vertebrae. Load application to rodent vertebrae will first compress the growth plate and only following compression of this structure cause significant development of displacement and strains within the trabecular and cortical bone. This insight into the biomechanical response of rat vertebrae is apparent through the application of image registration to analyse vertebral body behaviour; such information would not be evident in analysing preclinical whole vertebral body response using finite element modeling or experimental testing protocols.

To compare strains measured in a whole rat-tail vertebra by image registration (IM) with those predicted by solid finite element analysis (FEA). Quantification of bone strain allows better understand fracture risk, bone healing and turnover.

The sixth caudal vertebra of an rnu/rnu rat was μCT scanned (17.5×17.5×17.5μm/voxel) while loaded (27N axial compression) and unloaded. IM was used to calculate strain and displacement fields in the bone due to the applied load by finding a spatial mapping between the two scans. Strain was resolved to varying spatial resolution; high strain gradient regions (ie growth plates) were analyzed to higher spatial resolutions. A FE model was created of the unloaded vertebra, consisting of tetrahedral elements with transversely isotropic material properties. Elements were assigned elastic moduli based upon μCT image intensities. Growth plate moduli ranged from 0–150kPa and the bone moduli ranged from 0.2–15000MPa. Vertebral geometry was created through segmentation of μCT images. Displacement boundary conditions were obtained by matching cranial and caudal surfaces in the unloaded and loaded scans. The displacement fields of the two methods were compared by using the fields calculated to deform the unloaded scan to match the loaded scan. The strains were compared by plotting FEA measured axial strain against IM calculated axial strain.

The displacement fields calculated by both methods were able to spatially align the unloaded scan to the loaded scan (Mean Voxel Intensity Difference: FEA=441HU, IM=328HU, Unregistered=969HU). IM and FEA show very limited agreement in axial strain measurement (R2=0.388, Slope=0.75, X-Intercept=0.0037) although both calculated high axial strains in the growth plates and low axial strains in the trabecular and cortical bone. Good agreement was found in the mean axial strain measured by both methods (IM= −0.044, FEA=−0.037). IM was better able to deal with difficulties in quantifying bone strain due to the growth plate than FEA.

IM presents advantages over FEA in measuring strain in complex whole bone trabecular structures, however has lower spatial resolution than is possible with FEA.

Purpose: There is a clinical need for novel effective local therapies to treat spinal metastases at significant risk for fracture. Photodynamic therapy (PDT) is a promising cancer treatment that employs wavelength specific light combined with a photosensitizing agent to induce localized tumour destruction by photochemical generation of singlet oxygen. Using minimally invasive techniques developed for vertebroplasty to deliver light within the vertebral body, PDT is proposed as a potential new treatment for spinal metastases; however, the effects of PDT on bone are largely unknown. This study aims to determine if PDT affects the structural integrity of normal vertebral bone.

Methods: Sixteen Wistar rats were randomly assigned to control, 1-week treatment or 6-week treatment groups. Rats treated with PDT received an intracardiac injection of 2mg/kg BPD-MA activated at 15 minutes post-injection through administration of a non-thermal 690nm diode laser positioned adjacent to the L3 vertebral body via fluoroscopic guidance (150J at 150mW). Rats were sacrificed at 1-week or 6-weeks following a single treatment. |In vitro & #956;CT scans were taken of L2-L4 and 3D stereological quantities measured using a semiautomated volume shrinkage thresholding technique within the trabecular bone centrum. L2, L3 and L4 vertebral bodies were individually tested biomechanically to failure in axial compression. Yield stress and stiffness were calculated from generated load displacement curves.

Results: Bone surface area and bone volume significantly increased with treatment, through trabecular thickening, at both 1-week and 6-weeks vs. control group. The treated group demonstrated an increase in yield stress at 6-weeks vs. control (27%, p=0.023). An increase in stiffness (45%, p=0.010) was found in the 1-week treatment group vs. control, but was not maintained in the 6-week group.

Conclusions: PDT is a promising new treatment for spinal metastases that appears to strengthen vertebral bone. Further research must determine the mechanism of this action and verify if similar effects will occur in metastatically-involved vertebrae. If PDT proves to be effective in both destroying tumour cells and in strengthening remaining bone, it may provide a very attractive minimally invasive treatment option for patients with spinal metastases.

Funding : Other Education Grant

Funding Parties : Canadian Breast Cancer Foundation, Ontario Chapter

Purpose: Effectively quantifying metastatic tumour involvement in the spine requires accurate vertebral segmentation. Automated techniques such as thresholding or region growing have difficulty defining boundaries between tumour tissue and surrounding soft tissue if lytic disease breaches the vertebral cortical shell. It is hypothesized that the application of image registration techniques may afford a potential solution to automating segmentation of metastically-involved vertebrae with cortical shell destruction. The objective of this study is to validate deformable registration as a means to automate the segmentation of tumour-bearing vertebrae through the transformation of atlas segmentations.

Methods: CT scans were collected from 6 patients (T4-L5) with spinal metastases secondary to breast cancer. Healthy levels from the patients were cropped and segmented using a combination of thresholding and manual delineation (Amira 3.1.1, TGS Berlin) to obtain the atlas for each vertebral level. After spatial alignment, metastatically involved vertebral levels were segmented by a registration of the atlas scan by automated affine registration (Amira) and refined by demons deformable registration (ITK, NLM Bethesda). The algorithm was tested through comparison of 10 vertebral bodies (thoracic and lumbar) segmented using the automated approach against a gold standard segmentation produced by semi-manual thresholding. The quality of the automatic segmentation was determined by calculating how many voxels were concurrently within both automatic and manual segmentation of the scan.

Results: Deformable registration successfully segmented metastatically involved vertebrae with and without breach of the cortical shell. Similar performance was evident when using an atlas from an adjacent level as compared to using an atlas of the identical vertebral level. Quality of the automatic segmentation ranged from 87.67%–96.22% concurrency. Comparisons of inter-user semi-manual segmentations yielded a similar maximum of 96% concurrency. Analysis speed was 10 to 15 times faster using the automated technique.

Conclusions: By maintaining the atlas morphology, atlas-based segmentations are able to accurately differentiate between trans-cortical tumours and surrounding soft tissue, overcoming problems inherent to more conventional automated segmentation techniques. Clinical application of this segmentation algorithm centers on tumour quantification and tracking progression of treatment effect and metastatic disease pathology. Funding: Other Education Grant Funding Parties: Canadian Breast Cancer Research Alliance, Sunnybrook & Women`s College Research Institute

Purpose: The objective of this study was to establish an automated and objective method to quantitatively characterize the extent, spatial distribution, and temporal progression of metastatic disease in the bony spine.

Methods: Serial patient CT scans from GE Light-speed Plus CT Scanners were standardized to 120kVp, 1.25mm/2.5mm slice interval/ thickness, standard reconstruction, and 0.468mm/0.468mm pixel spacing. From 3D reconstructed CT images, trabecular regions within vertebral bodies (VBs) were segmented through atlas-based deformable registration (ITK, NLM, Bethesda). Voxel intensity histograms (voxel counts vs. Hounsfield Units) were used to characterize 32 healthy and 11 metastatically involved vertebrae (T5 to L5). Healthy histograms were fitted to Gaussian regression curves and compared using one-way repeated measures ANOVA (p< 0.05). Tumours were segmented as connected areas with voxel intensities between specified thresholds (Amira 3.1.1, TGS, Berlin).

Results: Histograms of healthy vertebrae were found to be Gaussian distributions (avg. RMSD = 30 voxel counts). The Gaussian mean & #956; ranged from 120 to 290HU, presumably due to inter-patient differences in age and activity. However, the histogram data sets were not significantly different (p> 0.8) across intra-patient vertebral levels T5-L5. Consequently, the Gaussian parameters, & #956; and standard deviation & #963;, determined from fitted healthy histograms could be used in adjacent metastatic levels to define patient-specific lytic and blastic thresholds for tumor segmentation. The ideal lytic and blastic segmentation thresholds were determined to be & #956;−& #963; and & #956;+2& #963; respectively: i.e. while histograms of metastatic VBs were non-Gaussian (RMSD of 56 voxels), subtracting from them the tumourous regions segmented accordingly restored the Gaussian nature of the distributions (RMSD of 24 voxels). Metastatic involvement can then be quantified from histograms of metastases in terms of: (1) lytic/ blastic volumes from areas under the curves; (2) severity of the pathologic involvement from the distribution and range; (3) tumor progression over time or treatment effects by taking the difference between sequential scans.

Conclusions: This proposed histogram-based method for characterizing spinal metastases shows great potential in extending the quantitative capacity of CT-based radiographic evaluations, especially in tracking meta-static progression and treatment effectiveness in clinical research applications. Funding: Other Education Grant Funding Parties: NSERC and CBCRA

Using serial CT scans, this project aims to develop a clinical research tool that analyzes changes in vertebral density in spines involved with metastatic disease. Tracking of total vertebral body and tumor volume alone was investigated. A program was developed to semi-automate the segmentation of the region of interest followed by image registration to superimpose the segmentation onto spatially aligned serial scans. Based on analysis of a simulated metastatic vertebra, generating a voxel distribution histogram from the vertebral body best quantified density in serial scans. This quantification method may improve clinical decision-making and treatment options for patients with vertebral metastases.

To develop a clinical research tool to serially track tumor involvement in vertebrae with metastatic disease by quantifying changes in CT attenuation.

Segmentation of the vertebral body and analysis of the voxel distribution within the region provides the most accurate method of quantifying changes in tumor involvement for the metastatic spine.

A quantitative method to assess the progression or regression of disease may improve clinical decision–making and treatment options for patients with spinal metastases.

The vertebral body segmentation was more accurate at tracking tumor involvement (voxel distribution histogram: 96.8% +/− 0.75% accuracy, mean density error: 4.7% +/− 0.8%) than segmenting the tumor volume alone (voxel distribution histogram: 86.1% +/− 3.6% accuracy, mean density error: 14.1% +/− 4.8%).

A program was developed to semi-automatically segment the total vertebral body and tumor volume alone from CT scans of metastatically involved vertebrae. Image registration through user-defined landmarks and surface matching was used to spatially align serial scans, and the initial segmentation was superimposed with the aligned scans. Changes within the segmentation between CT scans were tracked using mean density and a voxel distribution histogram. A cadaveric vertebra with a simulated tumor was scanned at five orientations with 20° offsets to determine the accuracy of the methods. Error primarily resulted from unavoidable re-sampling during alignment of the scans.

Photodynamic therapy (PDT) is a promising new treatment for spinal metastases; however, the effects of PDT on bone are largely unknown. This study assessed the impact of PDT on spinal stability in rats at high (non-therapeutic) drug and LASER light doses. Spinal stability was assessed using stereological measures attained from in vitro μCT scans. High doses of PDT were shown to cause a reduction in vertebral density. Postoperative paralysis was also noted in a subset of animals treated. Tumour-involved vertebrae are already mechanically weakened; as such it is essential to establish a safe and efficacious therapeutic window for vertebral PDT.

This study assessed the effect of high doses of photodynamic therapy (PDT) on biomechanical stability and bone density of lumbar vertebrae.

PDT can cause damage to the vertebral bone and induce paralysis when treatment is applied at very high doses in the rat spine.

PDT is a promising new treatment for spinal metastases however, it is important to understand its effect on vertebral bone in order to closely define the therapeutic window for safety and efficacy.

Trabecular bone density decreased from L1–L3 in normal, untreated rats. The L2 vertebra when treated with high dose PDT was shown to have decreased bone density as compared to both L1 and L3. As expected, tumour-bearing rats had lower vertebral densities than normals.

Rnu/Rnu rats were separated into normal controls, normals treated with PDT and tumour-bearing rats. Rats treated with PDT received an intercardiac injection of 2.5mg/Kg BPD-MA. The drug was activated through administration of 500J (300mA) of a non-thermal 690nm LASER adjacent to the L2 vertebral body. After one week, in vitro μCT scans were taken of L1–L3 and stereological quantities measured.

The demonstrated reduction of bone density as quantified one week following treatment is important when considering spinal stability in the potential use of PDT to treat vertebral metastases. We have observed that the therapy can induce paralysis when treatment is applied at high doses in the rat spine. The intermediate and long-term effects of PDT on bone remain unknown and require ongoing study.

Introduction: Lateral compression (LC) is the most common type of pelvic fracture, however there are no clear clinical or radiologic indications to direct conservative versus operative treatment of this pattern of injury. This study aims to determine if improved characterization of LC fracture patterns is possible through 3D radiological analysis.

Methods: CT scans of 61 patients with unilateral LC pelvic fractures were identified. The scans were segmented to generate a 3D model of the pelvis (Amira, MCS Inc). To quantify displacement of the fractured hemipelvis, the spatial orientation of three distinct anatomical landmarks (anteriof superior iliac, posterior superior iliac and ischial spines) on each side of the 3D hemipelvis were identified. Translational and rotational differences between the intact and fractured sides were compared to determine patterns of displacement with respect to a generated mid-sacral sagittal plane.

Results: 36.6% of the LC fractures were classified as non-displaced, 36.6% had an isolated single axis rotation, in another 13.3% had a pure translation with no rotation. 10% demonstrated pure rotational involvement in extension and the remaining patients, 3.3% had dual axis rotation.

Conclusion and Significance: Using 3D geometric analysis we were able to quantify patterns of LC fracture displacements not previously described. We characterized 5 subgroups of displacement patterns in LC fractures of the pelvis. Our 3D findings demonstrated a spectrum of translation and rotation motivating comparison with clinical outcome.