BoneDVC Service

Digital Volume Correlation

No account? Contact us

Accurate bone digital volume correlation service

Scientific background

The measurement of bone deformation under loading is important to understand the effect of musculoskeletal pathologies and to optimise their treatments.

The Digital Volume Correlation (DVC) approach applied to the bone tissue, proposed first by Bay et al. (1999) is the only method to provide a measurement of the full-field 3D deformation. It combines an elastic registration algorithm to compute the displacement to transform the undeformed image into the deformed one, and the differentiation of the displacement fields into strains. The main challenge in applying this method to bone tissue is that bone fails at small deformations (approximately 1% as reported by Bayraktar et al., 2004). Therefore, the expected strains in the elastic range are small and the DVC method needs to be very accurate.

The BoneDVC service uses the algorithm proposed by Dall'Ara et al. (2014), which has been tested for several bone structures and was found to be more accurate than other published or commercially available alternative (Dall'Ara et al., 2017; Palanca et al., 2015). The user is encouraged to look into the above-mentioned publications for the details of the algorithm. The accuracy of the BoneDVC algorithm has been tested on several bone structures images with standard desktop microCT (Palanca et al., 2016; Tozzi et al., 2017; Giorgi & Dall'Ara, 2018) or high resolution Synchrotron microCT (Palanca et al., 2017; Comini et al., 2019).

This algorithm has been also used for validating the local predictions of finite element models for different bone microstructures (Kusins et al., 2019; Oliviero et al., 2018; Costa et al., 2017; Chen et al., 2017).

BoneDVC service

The BoneDVC service estimates the field of displacements and strains in bone specimens, provided two 3D images of the undeformed and deformed bones.

A user uploads two 3D images, usually micro-Computed Tomography (microCT) scans of the specimen that has been tested in situ within the scanning machine. The first image represents the undeformed configuration ("Fixed_Image"), the second represents the deformed configuration ("Moved_Image"). As option, the user can also upload a mask ("Mask_Image") that specifies where the registration should take place. The threshold in the mask image ("Thresh") represents the limit in the grey-values above which each voxel is considered included in the region of interest where the registration should be run.

Images should be uploaded as 8-bit multi-slice DICOM files (1 file for each 3D stack) and should have the same size (voxels in every Cartesian direction). The images should have been already rigidly co-registered and the results will be provided with respect to the coordinate system of the input images. At the moment the limit of the input images is approximately 2Gb each, but this will be increased in the future with a parallel version of the elastic registration algorithm currently under development.

A DVC job is created by clicking on New Job and submitting a DVC configuration file (Figure 2; example below). This will generate a form in which a VOI's Fixed, Moved and Mask Dicom images can be selected for the corresponding Specimen (Figure 1A). Files can be selected either from Google Drive* or your Desktop. The results will be provided in the structure as reported in Figure 1B. If required, multiple specimens and VOIs can be specified in a single configuration file.

Google Drive folder structures

Figure 1: structure of the folders for input images and results.

* A Sheffield University Google account is required to access this service. When you use the Google Drive file picker for the first time this website will ask your permission to add the BoneDVC app to your Google Drive account. You can remove this app at any time in Google Drive by going to:

Settings > Managing Apps > Options > Disconnect from Drive

If you do not have a Sheffield University Google account please contact this email address to access this service online.

The user needs to provide a configuration file to specify the input parameters and the registrations. A typical configuration file looks like the one below and can be downloaded here.

Example configuration file

Figure 2: Typical example of configuration file. A template for the configuration file can be downloaded in the service.

Description of required configuration file entries

Projectname of the considered project
Vox_Dimdimension of the voxel in the images to be registered in microns
Specimen_Spec1name of the first specimen to be registered (more specimens can be provided)
VOIs_Spec1name of the volume of interest (VOI) to be analysed within the first specimen (more VOIs can be provided for the same specimen)
Thresh_Spec1threshold value for the identification of the region to be registered in the mask file
NSs_Spec1values of the nodal spacing (NS, in number of voxels) for which the registrations will be run (more than one value can be provided)
Iter_Spec1number of iterations after which to stop the registration algorithm in case it has not converged (different values for different NSs can be provided). Default value is 100.
Resultscode for deciding whether to run the registration with (1) or without (0) mask image

The algorithm will automatically compute the field of displacements by using deformable registration and the strain after differentiation of the displacement field. The user will receive the outputs in the project folder according to the chosen Specimens, VOIs and NSs.

Description of results files

disp.txt list of displacements applied to each node (from output_map.txt)
elements.txt list of elements
nodes.txt list of node coordinates
output_map.txt list of nodes coordinates and corresponding displacements along x, y and z directions
results_elements.txtlist of principal strains computed at the element centroids. The first column contains the list of element numbers, while the next columns contain the list of first, second and third principal strains respectively
results_elements_xyz.txtlist of strains in the three directions computed at the element centroids
results.txtlist of principal strains computed at the nodes. The first column contains the list of node numbers, while the next columns contain the list of first, second and third principal strains respectively
results_xyz.txtlist of strains in the three directions computed at the nodes
configuration_file.txtconfiguration file used for the analyses
cronLog-*.txtlog file(s) written during the analyses

References

Bayraktar HH, Morgan EF, Niebur GL, Morris GE, Wong EK, Keaveny TM. Comparison of the elastic and yield properties of human femoral trabecular and cortical bone tissue. J Biomech. 2004 Jan;37(1):27-35. PMID: 14672565

Chen Y, Dall'Ara E, Sales E, Manda K, Wallace R, Pankaj P, Viceconti M. Micro-CT based finite element models of cancellous bone predict accurately displacement once the boundary condition is well replicated: A validation study. J Mech Behav Biomed Mater. 2017 Jan;65:644-651. doi: 10.1016/j.jmbbm.2016.09.014. Epub 2016 Sep 17. PMID: 27741494

Comini F, Palanca M, Cristofolini L, Dall'Ara E. Uncertainties of synchrotron microCT-based digital volume correlation bone strain measurements under simulated deformation. J Biomech. 2019 Mar 27;86:232-237. doi: 10.1016/j.jbiomech.2019.01.041. Epub 2019 Jan 30. PMID: 30736962

Costa MC, Tozzi G, Cristofolini L, Danesi V, Viceconti M, Dall'Ara E. Micro Finite Element models of the vertebral body: Validation of local displacement predictions. PLoS One. 2017 Jul 11;12(7):e0180151. doi: 10.1371/journal.pone.0180151. eCollection 2017. PMID: 28700618

Dall'Ara E, Barber D, Viceconti M. About the inevitable compromise between spatial resolution and accuracy of strain measurement for bone tissue: a 3D zero-strain study. J Biomech. 2014 Sep 22;47(12):2956-63. doi: 10.1016/j.jbiomech.2014.07.019. Epub 2014 Jul 30. PMID: 25113806

Giorgi M, Dall'Ara E. Variability in strain distribution in the mice tibia loading model: A preliminary study using digital volume correlation. Med Eng Phys. 2018 Dec;62:7-16. doi: 10.1016/j.medengphy.2018.09.001. Epub 2018 Sep 20. PMID: 30243888

Kusins J, Knowles N, Ryan M, Dall'Ara E, Ferreira L. Performance of QCT-Derived scapula finite element models in predicting local displacements using digital volume correlation. J Mech Behav Biomed Mater. 2019 May 14;97:339-345. doi: 10.1016/j.jmbbm.2019.05.021. PMID: 31153115

Oliviero S, Giorgi M, Dall'Ara E. Validation of finite element models of the mouse tibia using digital volume correlation. J Mech Behav Biomed Mater. 2018 Oct;86:172-184. doi: 10.1016/j.jmbbm.2018.06.022. Epub 2018 Jun 18. PMID: 29986291

Palanca M, Bodey AJ, Giorgi M, Viceconti M, Lacroix D, Cristofolini L, Dall'Ara E. Local displacement and strain uncertainties in different bone types by digital volume correlation of synchrotron microtomograms. J Biomech. 2017 Jun 14;58:27-36. doi: 10.1016/j.jbiomech.2017.04.007. Epub 2017 Apr 20. PMID: 28457604

Palanca M, Cristofolini L, Dall'Ara E, Curto M, Innocente F, Danesi V, Tozzi G. Digital volume correlation can be used to estimate local strains in natural and augmented vertebrae: An organ-level study. J Biomech. 2016 Dec 8;49(16):3882-3890. doi: 10.1016/j.jbiomech.2016.10.018. Epub 2016 Oct 20. PMID: 27814973

Palanca M, Tozzi G, Cristofolini L, Viceconti M, Dall'Ara E. Three-dimensional local measurements of bone strain and displacement: comparison of three digital volume correlation approaches. J Biomech Eng. 2015 Jul;137(7). doi: 10.1115/1.4030174. Epub 2015 Jun 2. PMID: 25807338

Tozzi G, Dall'Ara E, Palanca M, Curto M, Innocente F, Cristofolini L. Strain uncertainties from two digital volume correlation approaches in prophylactically augmented vertebrae: Local analysis on bone and cement-bone microstructures. J Mech Behav Biomed Mater. 2017 Mar;67:117-126. doi: 10.1016/j.jmbbm.2016.12.006. Epub 2016 Dec 8. PMID: 27992842