Gain More Insights from Your Mineralized Tissue Specimens
Multiscale Bone Acquisitions Down to the Nanoscale
X-ray imaging is invaluable in skeletal research both for sample characterization and for bone morphometry measurements. MicroCT (µCT) is a common type of X-ray technology used for the non-destructive generation of 3D datasets. Unlike other microscopy approaches, X-ray imaging can be used with intact samples; no cutting or sectioning is required.
Bone Morphometry Measurements
Easy Quantification of Bone Microarchitecture
Insights into bone structure and condition are revealed using X-ray imaging and parameters such as trabecular thickness, BV/TV, or cortical to trabecular bone ratio are important measurables to study. Consistency and repeatability of bone morphometry measurements calculated from µCT datasets rely on the use of standard procedures for sample preparation, image acquisition and image processing1. The ZEISS Xradia range of X-ray instruments provides high contrast capabilities that make these standard acquisitions quick and easy.
Valuable and Precise Bone Morphometry Assessments
The ZEISS Xradia Context µCT is ideal for acquisition of specimens from millimeters to centimeters in size and generates unrivalled image quality and contrast. The ZEISS Xradia Versa X-ray microscope uses two-stage magnification to generate higher resolution insights, even in larger specimens, and this is fueling excitement in the skeletal research community2. Combining either of these tools with the Dragonfly Pro Bone Analysis Module3 provides a powerful and robust solution for assessing and quantifying bone microarchitecture.
Assessing Quality and Mechanical Properties of Bone
From Micro- To Nano- Structural Analysis
Multiscale acquisition of bone provides a wealth of information about bone architecture across different length scales. The multiple objective lenses of the ZEISS Xradia Versa X-ray microscope enable characterization of bone across these length scales to reveal details of the hierarchical structure2.
Assessing Bone Health and Structure Using Osteocyte Lacunae
Localization, orientation, and volume of osteocyte lacunae can be quantified using high resolution X-ray microscope data4. These insights can be challenging to reach using traditional µCT due to limitations in resolution and contrast5.
Measuring Strain Distribution in Bone Tissue in situ
Understanding strain distribution is crucial for further investigating the structure-function relationship of bone. How changes in bone tissue or biomaterial architecture influence its mechanical properties and the load transfer within the bone, joint or surrounding tissue can all be assessed. In situ imaging is a powerful approach to perform such 4D assessments since specimens can be imaged inside an in-situ rig at high resolution using the Xradia Versa XRM and then subjected to compression prior to repeating this routine as required. The 3D full-field strain distribution and magnitude of each sample can then be assessed using Digital Volume Correlation (DVC)5.
Improved Throughput of 3D Analyses in Bone
Simultaneous Reduction of Noise and Acquisition Time with Deep Learning Reconstruction
3D X-ray assessment of bone microstructure demands a high number of datapoints for reproducibility and robustness. Most commercial 3D X-ray systems use the Feldkamp-Davis-Kress (FDK) algorithm, which generates good quality images but requires a relatively high number of projections and/or long exposure times to reduce noise and artifacts. ZEISS Deep Learning reconstruction (DeepRecon) requires far fewer 2D projection images, thereby reducing data acquisition times and improving the µCT throughput by up to 10 times.
Without the need of any additional X-ray beam-line hardware, DeepRecon significantly increases the throughput of 3D musculoskeletal analysis in addition to reducing noise to reveal structure and subtle differences in grayscale.
Imaging in Action
Future Technology Centre, University of Portsmouth
M.L. Bouxsein et al. (2010), https://doi.org/10.1002/jbmr.141
N.K. Wittig et al (2022), https://doi.org/10.1016/j.jsb.2021.107822
Dragonfly Pro Bone Analysis Module, https://theobjects.com/dragonfly/bone-analysis.html
S. Suniaga et al (2018), https://doi.org/10.1038/s41598-018-21776-1
Bonithon et al., Acta Biomaterialia, 2021, https://doi.org/10.1016/j.actbio.2021.03.068