Quantification of the bone lacunocanalicular network from 3D X-ray phase nanotomography images

3D quantification of the lacunocanalicular network on human femoral diaphysis through synchrotron radiation-based nanoCT

Osteocytes are the major actors in bone mechanobiology. Within bone matrix, they are trapped close together in a submicrometric interconnected network: the lacunocanalicular network (LCN). The interstitial fluid circulating within the LCN transmits the mechanical information to the osteocytes that convert it into a biochemical signal. Understanding the interstitial fluid dynamics is necessary to better understand the bone mechanobiology. Due to the submicrometric dimensions of the LCN, making it difficult to experimentally investigate fluid dynamics, numerical models appear as a relevant tool for such investigation. To develop such models, there is a need for geometrical and morphological data on the human LCN. This study aims at providing morphological data on the human LCN from measurement of 27 human femoral diaphysis bone samples using synchrotron radiation nano-computed tomography with an isotropic voxel size of 100 nm. Except from the canalicular diameter, the canalicular morphological parameters presented a high variability within one sample. Some differences in terms of both lacunar and canalicular morphology were observed between the male and female populations. But it has to be highlighted that all the canaliculi cannot be detected with a voxel size of 100 nm. Hence, in the current study, only a specific population of large canaliculi that could be characterize. Still, to the authors knowledge, this is the first time such a data set was introduced to the community. Further processing will be achieved in order to provide new insight on the LCN permeability.

Influence of intramedullary pressure on Lacuno-Canalicular fluid flow: A systematic review

While most of current models investigating bone remodelling are based on matrix deformation, intramedullary pressure also plays a role. Bone remodelling is orchestrated by the Lacuno-Canalicular Network (LCN) fluid-flow. The aim of this review was hence to assess the influence of intramedullary pressure on the fluid circulation within the LCN. Three databases (Science Direct, Web of Science, and PubMed) were used. The first phase of the search returned 731 articles, of which 9 respected the inclusion/exclusion criteria and were included. These studies confirm the association between intramedullary pressure and fluid dynamics in the LCN. Among the included studies, 7 experimental studies using animal models and 2 numerical models were found. The studies were then ranked according to the nature of the applied loading, either axial compression or direct cyclic intramedullary pressure. The current review revealed that there is an influence of intramedullary pressure on LCN fluid dynamics and that this influence depends on the magnitude and the frequency of the applied pressure. Two studies confirmed that the influence was effective even without bone matrix deformation. While intramedullary pressure is closely associated with LCN fluid, there is a severe lack of studies on this topic. STATEMENT OF SIGNIFICANCE: Since the 1990's, numerical models developed to investigate fluid flow in bone submicrometric porous network are based on the flow induced by matrix deformation. Bone fluid flow is known to be involved in cells stimulation and hence directly influences bone remodeling. Different studies have shown that intramedullary pressure is also associated with bone mechanosensitive adaptation. This pressure is developed in bone due to blood circulation and is increased during loading or muscle stimulation. The current article reviews the studies investigating the influence of this pressure on bone porous fluid flow. They show that fluid flow is involved by this pressure even without bone matrix deformation. The current review article highlights the severe lack of studies about this mechanism.

Polychromatic neutron phase-contrast imaging of weakly absorbing samples enabled by phase retrieval

The use of a phase-retrieval technique for propagation-based phase-contrast neutron imaging with a polychromatic beam is demonstrated. This enables imaging of samples with low absorption contrast and/or improving the signal-to-noise ratio to facilitate e.g. time-resolved measurements. A metal sample, designed to be close to a phase pure object, and a bone sample with canals partially filled with D₂O were used for demonstrating the technique. These samples were imaged with a polychromatic neutron beam followed by phase retrieval. For both samples the signal-to-noise ratios were significantly improved and, in the case of the bone sample, the phase retrieval allowed for separation of bone and D₂O, which is important for example for in situ flow experiments. The use of deuteration contrast avoids the use of chemical contrast enhancement and makes neutron imaging an interesting complementary method to X-ray imaging of bone.

Calibration by differentiation - Self-supervised calibration for X-ray microscopy using a differentiable cone-beam reconstruction operator

High-resolution X-ray microscopy (XRM) is gaining interest for biological investigations of extremely small-scale structures. XRM imaging of bones in living mice could provide new insights into the emergence and treatment of osteoporosis by observing osteocyte lacunae, which are holes in the bone of few micrometers in size. Imaging living animals at that resolution, however, is extremely challenging and requires very sophisticated data processing converting the raw XRM detector output into reconstructed images. This paper presents an open-source, differentiable reconstruction pipeline for XRM data which analytically computes the final image from the raw measurements. In contrast to most proprietary reconstruction software, it offers the user full control over each processing step and, additionally, makes the entire pipeline deep learning compatible by ensuring differentiability. This allows fitting trainable modules both before and after the actual reconstruction step in a purely data-driven way using the gradient-based optimizers of common deep learning frameworks. The value of such differentiability is demonstrated by calibrating the parameters of a simple cupping correction module operating on the raw projection images using only a self-supervisory quality metric based on the reconstructed volume and no further calibration measurements. The retrospective calibration directly improves image quality as it avoids cupping artifacts and decreases the difference in gray values between outer and inner bone by 68% to 94%. Furthermore, it makes the reconstruction process entirely independent of the XRM manufacturer and paves the way to explore modern deep learning reconstruction methods for arbitrary XRM and, potentially, other flat-panel CT systems. This exemplifies how differentiable reconstruction can be leveraged in the context of XRM and, hence, is an important step toward the goal of reducing the resolution limit of in-vivo bone imaging to the single micrometer domain. This article is protected by copyright. All rights reserved.