Search

International audience; Osteocytes are the most frequent bone cells connected with each other through cell processes within tiny tubular-shaped canaliculi. The so-called osteocyte lacunar-canalicular network (LCN) plays a crucial role in bone remodeling and mineral homeostasis. Given the critical nature of these functions, it is herein hypothesized that the LCN must be structurally “overengineered” to provide network resilience. This hypothesis is tested by characterizing canalicular networks in human bone at the fundamental “building-block” level of LCN formed by two adjacent osteocytes. As the hierarchical micro- and macroscale structure of bone is influenced by anatomical location, subjected loads, and growth rate, three distinct tissue types are studied. These include femur, jaw, and heterotopic ossification (HO), a rapidly forming mineralized tissue found in soft tissue compartments following severe trauma. It is found that the LCNs at the fundamental level are composed of hundreds of canalicular segments but of only few separated groups of linked canaliculi (canalicular clusters), resulting in a strongly pronounced interconnectivity. Fluid permeability simulations on intact and artificially altered LCN suggest that the function of the LCN is not only to optimize rapid and efficient access to bone mineral, but also to maintain high permeability when inevitable local interruption of canaliculi occurs.

Spectral computed tomography acquires energy-resolved data that allows recovery of densities of constituents of an object. This can be achieved by decomposing the measured spectral projection into material projections, and passing these decomposed projections through a tomographic reconstruction algorithm, to get the volumetric mass density of each material. Material decomposition is a nonlinear inverse problem that has been traditionally solved using model-based material decomposition algorithms. However, the forward model is difficult to estimate in real prototypes. Moreover, the traditional regularizers used to stabilized inversions are not fully relevant in the projection domain. In this study, we propose a deep-learning method for material decomposition in the projection domain. We validate our methodology with numerical phantoms of human knees that are created from synchrotron CT scans. We consider four different scans for training, and one for validation. The measurements are corrupted by Poisson noise, assuming that at most 10⁵ photons hit the detector. Compared to a regularized Gauss-Newton algorithm, the proposed deep-learning approach provides a compromise between noise and resolution, which reduces the computation time by a factor of 100.

We consider the reconstruction of an image from a sequence of a few linear measurements corrupted by Poisson noise. This generic problem has many biomedical applications, such as computerized tomography, positron emission tomogra-phy, and optical microscopy. Here, we focus on a computational optics problem where the setup acquires some coefficients of the Hadamard transform of the image of the scene. We formalize this problem in a Bayesian setting where we estimate the missing Hadamard coefficients from those acquired. Then, we propose a deep-learning network that consists of two fully connected layers (FCLs) that map data from the measurement domain to the image domain, followed by convolutional layers that act in the image domain. On the one hand, we set the FCLs so that they compute the best linear solution of the problem. While the first FCL denoises the raw measurements, the second FCL completes the missing measurements from the denoised measurements. The convolutional layers undergo learning through a training phase. We also describe a framework for training the network in the presence of Poisson noise. In particular, our approach includes an estimation of the image intensity, together with a normalization scheme that allows varying noise levels to be handled during training. We compare our network to linear reconstructors and to network variants that do not address the noise issue at all, or that address it implicitly. Finally, we present results from simulated and experimental acquisitions, considering varying noise levels. Our network yields higher reconstruction peak signal-to-noise ratios in scenarios where the actual noise level is higher than that expected and used during the training phase.