Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

We developed nanoparticle scaffolds to enhance bone regeneration in critical-sized defects and mean to improve healing rates compared to traditional scaffolds.

Current methods often track bone volume changes across entire bones, lacking precision and consistently identifying localized regions of interest in longitudinal models. Our protocol enables consistent localized region of interest tracking in solid models, improving precision and longitudinal analysis, and compared to full bone volume assessments.

These findings will allow us to more accurately quantify bone regeneration over time and more effectively communicate the potential translational impact of our work.

[Instructor] To begin, open the extracted radius bone from the comparison dataset and right click on it. Then, search for image registration wizard and select it. In the properties section, set data to the comparison dataset for the extracted radius bone and reference to the initial time point dataset for the extracted radius bone. In the image registration wizard action section, click skip for step one of four. For steps two and three of four, use the interact cursor to adjust the tab box to the common region between the datasets and click apply under action after each step. In step four of four, set metric to correlation, transformation to rigid, pre-alignment to align principal axes, and click apply under action. After aligning the datasets, right click on the comparison week dataset for the extracted radius bone, search for resample transformed image, and select it. In the properties section, set data to the comparison week dataset for the extracted radius bone, interpolation to nearest neighbor, mode to extended, preserve to voxel size, and padding value to zero, then click apply. A new transformed dataset will be generated. Click to turn on the ortho slice for the initial time point and set data to the initial time point dataset for the extracted radius. Set orientation so the plane yields a transverse cut through the radius bone. Using the slice number slider in the properties section, adjust the slice number to identify the proximal and distal slices surrounding the critical size defect. Determine and document the slice number where the fracture meets the diaphysis of the radius bone at both ends. Turn on the ortho slice for the comparison week and set data to the initial time point dataset for the extracted radius. Then, adjust orientation so the plane yields a transverse cut through the radius bone. Using the slice number slider in the properties section with the initial time point data showing the distal ortho slice, align the comparison week slice number to match the distal slice of the initial time point. Note the slice number for the comparison week datasets distal slice and repeat for the proximal slice. Click on the initial time point for the extracted radius, and in the properties section, click on the crop editor tool. Within the crop editor popup, input the minimum and maximum values in the X, Y, or Z fields. Observe the viewing window as the region of interest adjusts, then click okay to crop the dataset. Repeat the crop procedure for the comparison week dataset. To determine the volume of the initial time point dataset, right click on the transformed initial time point dataset for the extracted radius, search for material statistics, and select it. In the properties section, set data as the transformed initial time point dataset, select materials, and click apply. Click on the new material statistics dataset, then in the properties window, click on spreadsheet show. Click the tables tab above the window to view the volume of the cropped initial time point dataset. Repeat the volume analysis steps for the comparison week dataset, and then go to the tables tab to view both datasets with separate volume tabs. To visualize the change in bone volume, right click on the comparison week transformed dataset for the extracted radius, search for arithmetic, and select it. In the properties window, set input A as the comparison week transformed dataset, input B as the initial time point dataset, input C as no source, result type as input A, leave option unchecked, set result channels as like input A, and set expression as AB. Click on the resulting dataset and press F2 to rename the file, then, right click on this result dataset, search for generate surface, and select it. In the properties window, click apply, and in the popup window, click continue to create a new surf dataset. Right click on the surf dataset, search for surface view, and select it. A surface view of the arithmetic result will appear in the viewing window. To change the color of the surface view, click on the surface view in the project view window. In the properties window, open the colors dropdown, select constant, then click on color map and assign a preferred color. To view bone volume change on the initial week dataset, right click on the transformed dataset, search for extract label, and select it. In the properties section, set labels to the transformed dataset, label ID to two, and check export to binary, then click apply to generate a result dataset. Then, press F2 to rename the result file. Right click on the new result dataset, search for generate surface, and select it. In the properties window, click apply, and in the popup window, click continue to create a new surf dataset. Next, right click on the new surf dataset, search for surface view, and select it. A surface view of the arithmetic result will appear. To change the color of this surface view, click on the surface view in the project view window. In the properties window, open the colors dropdown, select constant, then click on color map and assign a preferred color. Micro CT images of three unique rat models, each treated with a polycaprolactone scaffold for six weeks, were investigated. Solid models from week zero and six were successfully aligned using shared anatomical regions, enabling direct longitudinal comparison, and a merged model was generated to confirm registration accuracy. Subtracting the week zero region of interest from the week six region of interest revealed a distinct 3D model of bone volume change within the defect site. Visual overlays of bone volume changes from week zero to week six demonstrated that the different polycaprolactone, or PCL scaffold groups, resulted in varying overall bone volume changes. However, analysis within each PCL group remained consistent across users.