Quantitative evaluation of the effects of flow diverter treatment on aneurysm hemodynamics using time-resolved rotational angiography.

BACKGROUND: There have been immense advancements in the hardware and software of digital subtraction angiography systems over the last several years. These advancements continue to make progress toward the goals of offering better visualization and reducing radiation exposure. A newer advancement in this arena is presenting three-dimension data over time resulting in four-dimensional-digital subtracted angiography visualization. We have evaluated these protocols related to the evaluation of the treatment of intracranial aneurysms with pipeline flow diversion. METHODS: Four-dimensional-digital subtracted angiography imaging was acquired on an Artis Q Biplane angiographic system (Siemens Healthcare AG, Forchheim, Germany). A six second four-dimensional-digital subtracted angiography protocol was performed pre and post flow diverter placement. Pre and post reconstructed images were sent through a dedicated prototype research workstation (Syngo X-Workplace; Siemens Healthineers AG) for further flow evaluation. RESULTS: The treatment of an aneurysm with flow diversion led to a filling delay of 0.278 ± 0.422 s inside the aneurysms, whereas distal to the aneurysms the filling of the vessel segment occurred earlier post procedural (negative filling delay of -0.15 ± 0.31 s. The flow ratio inside the aneurysm decreased to 63.6 ± 23% of its pre-treatment value and distal to the aneurysm the flow remained substantially the same (flow ratio: 95.6 ± 0.29%). Data showed a relative filling delay of the aneurysm normalized to the distal vessel of 0.43 ± 0.36 s. The relative flow ratio of the aneurysm in comparison to the distal parent vessel was 72.2 ± 31%. CONCLUSIONS: Analysis of a four-dimensional-digital subtracted angiography acquisition allows assessment of the effects of flow diversion treatment on aneurysm hemodynamic parameters and shows a significant decrease in flow inside the aneurysm compared to the parent vessel distal to the aneurysm.

BOSS: Bones, organs and skin shape model.

A virtual anatomical model of a patient can be a valuable tool for enhancing clinical tasks such as workflow automation, patient-specific X-ray dose optimization, markerless tracking, positioning, and navigation assistance in image-guided interventions. For these tasks, it is highly desirable that the patient's surface and internal organs are of high quality for any pose and shape estimate. At present, the majority of statistical shape models (SSMs) are restricted to a small number of organs or bones or do not adequately represent the general population. To address this, we propose a deformable human shape and pose model that combines skin, internal organs, and bones, learned from CT images. By modeling the statistical variations in a pose-normalized space using probabilistic PCA while also preserving joint kinematics, our approach offers a holistic representation of the body that can be beneficial for automation in various medical applications. In an interventional setup, our model could, for example, facilitate automatic system/patient positioning, organ-specific iso-centering, automated collimation or collision prediction. We assessed our model's performance on a registered dataset, utilizing the unified shape space, and noted an average error of 3.6 mm for bones and 8.8 mm for organs. By utilizing solely skin surface data or patient metadata like height and weight, we find that the overall combined error for bone-organ measurement is 8.68 mm and 8.11 mm, respectively. To further verify our findings, we conducted additional tests on publicly available datasets with multi-part segmentations, which confirmed the effectiveness of our model. In the diverse TotalSegmentator dataset, the errors for bones and organs are observed to be 5.10mm and 8.72mm, respectively. Our work shows that anatomically parameterized statistical shape models can be created accurately and in a computationally efficient manner. The proposed approach enables the construction of shape models that can be directly integrated into to various medical applications.

Learning-based occupational x-ray scatter estimation.

Objective.During x-ray-guided interventional procedures, the medical staff is exposed to scattered ionizing radiation caused by the patient. To increase the staff's awareness of the invisible radiation and monitor dose online, computational scatter estimation methods are convenient. However, such methods are usually based on Monte Carlo (MC) simulations, which are inherently computationally expensive. Yet, in the interventional environment, immediate feedback to the personnel is desirable.Approach. In this work, we propose deep neural networks to mitigate the computational effort of MC simulations. Our learning-based models consider detailed models of the (outer) patient shape and (inner) anatomy, additional objects in the room, and the x-ray tube spectrum to cover imaging settings encountered in real interventional settings. We investigate two cases of scatter prediction. First, we employ network architectures to estimate the full three-dimensional (3D) scatter distribution. Second, we investigate the prediction of two-dimensional (2D) intensity projections that facilitate the intra-procedural visualization.Main results.Depending on the dimensionality of the estimated scatter distribution and the network architecture, the mean relative error of each network is in the range of 12% and 14% compared to MC simulations. However, 3D scatter distributions can be estimated within 60 ms and 2D distributions within 15 ms.Significance.Overall, our method is suitable to support the online assessment of scattered ionizing radiation in the interventional environment and can help to lower the occupational radiation risk.

"Virtual stent" - clinical evaluation and user experience of on-the-fly stent simulation in treatment of cerebral aneurysms.

BACKGROUND: Predicting final stent position can be challenging when treating cerebral aneurysms. Third-Party software proved helpful in selecting proper stents in treatment planning. Recent angiographic systems provide basic stent simulation capabilities integrated in the post-processing software to simulate stent position. Goal of this analysis was to evaluate the simulation process and correlation with definite stent position. MATERIALS AND METHODS: Thirty-three datasets with fusiform (n = 10) and saccular (n = 23) aneurysms, treated with stent or flow-diverter, were processed. A "virtual stent" of the same (nominal) size was simulated and its position was compared to the treatment result. Simulated length was rated in five grades (too short, shorter, equal, longer, too long), with regard to side-branches, anchoring zone etc. Simulation quality (centerline recognition/adherence to vessel margins) was rated in three grades (no, minor or major corrections required). RESULTS: Simulation was successful in 32/33 cases (97%), with one abortive attempt (3%). In 27/33 simulations (82%), there was no need for centerline refinement. Minor corrections were necessary in four and major corrections in two cases. Simulated nominal length was rated "equal" in 14/33 (42%) cases and "shorter" or "longer" - but within acceptable range - in each 9/33 (27%) cases. CONCLUSION: Basic stent simulation tools available with genuine angiographic workplace software can provide good simulation capabilities without need for third-party equipment. They can facilitate treatment planning and help to avoid shortage of devices. Yet, lack of calculation of foreshortening in large vessel diameters leaves the user to rely on their experience to account for device-specific properties.

X-Ray Scatter Estimation Using Deep Splines.

X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics' constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal's frequency characteristics.

Deep action learning enables robust 3D segmentation of body organs in various CT and MRI images.

In this study, we propose a novel point cloud based 3D registration and segmentation framework using reinforcement learning. An artificial agent, implemented as a distinct actor based on value networks, is trained to predict the optimal piece-wise linear transformation of a point cloud for the joint tasks of registration and segmentation. The actor network estimates a set of plausible actions and the value network aims to select the optimal action for the current observation. Point-wise features that comprise spatial positions (and surface normal vectors in the case of structured meshes), and their corresponding image features, are used to encode the observation and represent the underlying 3D volume. The actor and value networks are applied iteratively to estimate a sequence of transformations that enable accurate delineation of object boundaries. The proposed approach was extensively evaluated in both segmentation and registration tasks using a variety of challenging clinical datasets. Our method has fewer trainable parameters and lower computational complexity compared to the 3D U-Net, and it is independent of the volume resolution. We show that the proposed method is applicable to mono- and multi-modal segmentation tasks, achieving significant improvements over the state-of-the-art for the latter. The flexibility of the proposed framework is further demonstrated for a multi-modal registration application. As we learn to predict actions rather than a target, the proposed method is more robust compared to the 3D U-Net when dealing with previously unseen datasets, acquired using different protocols or modalities. As a result, the proposed method provides a promising multi-purpose segmentation and registration framework, particular in the context of image-guided interventions.

XDose: toward online cross-validation of experimental and computational X-ray dose estimation.

PURPOSE: As the spectrum of X-ray procedures has increased both for diagnostic and for interventional cases, more attention is paid to X-ray dose management. While the medical benefit to the patient outweighs the risk of radiation injuries in almost all cases, reproducible studies on organ dose values help to plan preventive measures helping both patient as well as staff. Dose studies are either carried out retrospectively, experimentally using anthropomorphic phantoms, or computationally. When performed experimentally, it is helpful to combine them with simulations validating the measurements. In this paper, we show how such a dose simulation method, carried out together with actual X-ray experiments, can be realized to obtain reliable organ dose values efficiently. METHODS: A Monte Carlo simulation technique was developed combining down-sampling and super-resolution techniques for accelerated processing accompanying X-ray dose measurements. The target volume is down-sampled using the statistical mode first. The estimated dose distribution is then up-sampled using guided filtering and the high-resolution target volume as guidance image. Second, we present a comparison of dose estimates calculated with our Monte Carlo code experimentally obtained values for an anthropomorphic phantom using metal oxide semiconductor field effect transistor dosimeters. RESULTS: We reconstructed high-resolution dose distributions from coarse ones (down-sampling factor 2 to 16) with error rates ranging from 1.62 % to 4.91 %. Using down-sampled target volumes further reduced the computation time by 30 % to 60 %. Comparison of measured results to simulated dose values demonstrated high agreement with an average percentage error of under [Formula: see text] for all measurement points. CONCLUSIONS: Our results indicate that Monte Carlo methods can be accelerated hardware-independently and still yield reliable results. This facilitates empirical dose studies that make use of online Monte Carlo simulations to easily cross-validate dose estimates on-site.

A NanoFE simulation-based surrogate machine learning model to predict mechanical functionality of protein networks from live confocal imaging.

Sub-cellular mechanics plays a crucial role in a variety of biological functions and dysfunctions. Due to the strong structure-function relationship in cytoskeletal protein networks, light can be shed on their mechanical functionality by investigating their structures. Here, we present a data-driven approach employing a combination of confocal live imaging of fluorescent tagged protein networks, in silico mechanical experiments and machine learning to investigate this relationship. Our designed image processing and nanoFE mechanical simulation framework resolves the structure and mechanical behaviour of cytoskeletal networks and the developed gradient boosting surrogate models linking network structure to its functionality. In this study, for the first time, we perform mechanical simulations of Filamentous Temperature Sensitive Z (FtsZ) complex protein networks with realistic network geometry depicting its skeletal functionality inside organelles (here, chloroplasts) of the moss Physcomitrella patens. Training on synthetically produced simulation data enables predicting the mechanical characteristics of FtsZ network purely based on its structural features ( R 2 ⩾ 0.93 ), therefore allowing to extract structural principles enabling specific mechanical traits of FtsZ, such as load bearing and resistance to buckling failure in case of large network deformation.

Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo µCT imaging and deep learning.

Throughout the process of aging, dynamic changes of bone material, micro- and macro-architecture result in a loss of strength and therefore in an increased likelihood of fragility fractures. To date, precise contributions of age-related changes in bone (re)modeling and (de)mineralization dynamics to this fragility increase are not completely understood. Here, we present an image-based deep learning approach to quantitatively describe the effects of short-term aging and adaptive response to cyclic loading applied to proximal mouse tibiae and fibulae. Our approach allowed us to perform an end-to-end age prediction based on µCT imaging to determine the dynamic biological process of aging during a two week period, therefore permitting short-term bone aging analysis with 95% accuracy in predicting time points. In a second application, our deep learning analysis reveals that two weeks of in vivo mechanical loading are associated with an underlying rejuvenating effect of 5 days. Additionally, by quantitatively analyzing the learning process, we could, for the first time, identify the localization of the age-relevant encoded information and demonstrate 89% load-induced similarity of these locations in the loaded tibia with younger control bones. These data therefore suggest that our method enables identifying a general prognostic phenotype of a certain skeletal age as well as a temporal and localized loading-treatment effect on this apparent skeletal age for the studied mouse tibia and fibula. Future translational applications of this method may provide an improved decision-support method for osteoporosis treatment at relatively low cost. STATEMENT OF SIGNIFICANCE: Bone is a highly complex and dynamic structure that undergoes changes during the course of aging as well as in response to external stimuli, such as loading. Automatic assessment of "age" and "state" of the bone may lead to early prognosis of deceases such as osteoporosis and enables evaluating the effects of certain treatments. Here, we present an artificial intelligence-based method capable of automatically predicting the skeletal age from µCT images with 95% accuracy. Additionally, we utilize it to demonstrate the rejuvenation effects of in-vivo loading treatment on bones. We further, for the first time, break down aging-related local changes in bone by quantitatively analyzing "what the age assessment model has learned" and use this information to investigate the structural details of rejuvenation process.

Effects of Long-Term Sclerostin Deficiency on Trabecular Bone Mass and Adaption to Limb Loading Differ in Male and Female Mice.

A new therapeutic option to treat osteoporosis is focused on Wnt signaling and its inhibitor sclerostin, a product of the Sost gene. In this work, we study the effect of sclerostin deficiency on trabecular bone formation and resorption in male and female mice and whether it affects mechano-responsiveness. Male and female 10- and 26-week-old Sost knockout (KO) and littermate controls (LCs) were subjected to in vivo mechanical loading of the left tibia for 2 weeks. The right tibia served as internal control. The mice were imaged using in vivo micro-computed tomography at days 0, 5, 10, and 15 and tibiae were collected for histomorphometric analyses after euthanasia. Histomorphometry and micro-CT-based 3D time-lapse morphometry revealed an anabolic and anti-catabolic effect of Sost deficiency although increased trabecular bone resorption accompanied by diminished trabecular bone formation occurred with age. Loading led to diminished resorption in adult female but not in male mice. A net gain in bone volume could be achieved with mechanical loading in Sost KO adult female mice, which occurred through a further reduction in resorbed bone volume. Our data show that sclerostin deficiency has a particularly positive effect in adult female mice. Sclerostin antibodies are approved to treat postmenopausal women with high risk of osteoporotic fractures. Further studies are required to clarify whether both sexes benefit equally from sclerostin inhibition.

Physics-driven learning of x-ray skin dose distribution in interventional procedures.

PURPOSE: Radiation doses accumulated during very complicated image-guided x-ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation-related risks to patients' skin, x-ray imaging devices are equipped with online air kerma monitoring components. Traditionally, such measurements have been used to estimate skin entrance dose by (a) estimating air kerma at the interventional reference point (IRP), (b) forward projecting the dose distribution, and (c) considering a backscatter factor among other correction factors. Unfortunately, the complicated interaction between incident x-ray photons, secondary electrons, and skin tissue cannot be properly accounted for by assuming a linear relationship between forward projected air kerma and a backscatter factor. Gold standard skin dose models are therefore determined using Monte Carlo (MC) techniques. However, MC simulations are computationally complex in general and possible acceleration mainly depends on the employed hardware and variance reduction techniques. To obtain reliable and fast dose estimates, we propose to combine MC-based simulations with learning-based methods. METHODS: The basic idea of our method is to approximate the radiation physics to calculate a first-order exposure estimate quickly. This initial estimate is then refined using prior knowledge derived from MC simulations. To this end, the primary photon propagation inside a voxelized patient model is estimated using a less accurate but fast photon ray casting (RC) simulation based on the Beer-Lambert law. The results of the RC simulation are then fed into a convolutional neural network (CNN), which maps the propagation of primary photons to the dose deposition inside the patient model. Additionally, the patient model itself including anatomy and material properties, such as mass density and mass energy-absorption coefficients, are fed into the CNN as well. The CNN is trained using smoothed results of MC simulations as output and RC simulations of identical imaging settings and patient models as input. RESULTS: In total, 163 MC and associated RC simulations are carried out for the head, thorax, abdomen, and pelvis in three different voxel phantoms. We used 10 8 or 10 9 primarily emitted photons sampled from a 125 kV peak voltage spectrum, respectively. Edge-preserving smoothing (EPS) is applied to reduce (a) general stochastic uncertainties and (b) stochastic uncertainty concerning MC simulations of less primary photons. The CNN is trained using seven imaging settings of the abdomen in a single phantom. Testing its performance on the remaining datasets, the CNN is capable of estimating skin dose with an error of below 10% for the majority of test cases. CONCLUSION: The combination of deep neural networks and MC simulation of particle physics has the potential to decrease the computational complexity of accurate skin dose estimation. The proposed approach can provide dose distributions in under one second when running on high-end hardware. On lower cost hardware, it took up to 2 min to arrive at the same result. This makes our approach applicable in high-end environments as well as in budget solutions. Furthermore, the number of primary photons only affects the training time, while the execution time is independent of the number of primary photons.

Pitfalls in interventional X-ray organ dose assessment-combined experimental and computational phantom study: application to prostatic artery embolization.

PURPOSE: With X-ray radiation protection and dose management constantly gaining interest in interventional radiology, novel procedures often undergo prospective dose studies using anthropomorphic phantoms to determine expected reference organ-equivalent dose values. Due to inherent uncertainties, such as impact of exact patient positioning, generalized geometry of the phantoms, limited dosimeter positioning options, and composition of tissue-equivalent materials, these dose values might not allow for patient-specific risk assessment. Therefore, first the aim of this study is to quantify the influence of these parameters on local X-ray dose to evaluate their relevance in the assessment of patient-specific organ doses. Second, this knowledge further enables validating a simulation approach, which allows employing physiological material models and patient-specific geometries. METHODS: Phantom dosimetry experiments using MOSFET dosimeters were conducted reproducing imaging scenarios in prostatic arterial embolization (PAE). Associated organ-equivalent dose of prostate, bladder, colon, and skin was determined. Dose deviation induced by possible small displacements of the patient was reproduced by moving the X-ray source. Dose deviation induced by geometric and material differences was investigated by analyzing two different commonly used phantoms. We reconstructed the experiments using Monte Carlo (MC) simulations, a reference male geometry, and different material properties to validate simulations and experiments against each other. RESULTS: Overall, MC-simulated organ dose values are in accordance with the measured ones for the majority of cases. Marginal displacements of X-ray source relative to the phantoms lead to deviations of 6-135% in organ dose values, while skin dose remains relatively constant. Regarding the impact of phantom material composition, underestimation of internal organ dose values by 12-20% is prevalent in all simulated phantoms. Skin dose, however, can be estimated with low deviation of 1-8% at least for two materials. CONCLUSIONS: Prospective reference dose studies might not extend to precise patient-specific dose assessment. Therefore, online organ dose assessment tools, based on advanced patient modeling and MC methods, are desirable.

A machine learning pipeline for internal anatomical landmark embedding based on a patient surface model.

PURPOSE: With the recent introduction of fully assisting scanner technologies by Siemens Healthineers (Erlangen, Germany), a patient surface model was introduced to the diagnostic imaging device market. Such a patient representation can be used to automate and accelerate the clinical imaging workflow, manage patient dose, and provide navigation assistance for computed tomography diagnostic imaging. In addition to diagnostic imaging, a patient surface model has also tremendous potential to simplify interventional imaging. For example, if the anatomy of a patient was known, a robotic angiography system could be automatically positioned such that the organ of interest is positioned in the system's iso-center offering a good and flexible view on the underlying patient anatomy quickly and without any additional X-ray dose. METHOD: To enable such functionality in a clinical context with sufficiently high accuracy, we present an extension of our previous patient surface model by adding internal anatomical landmarks associated with certain (main) bones of the human skeleton, in particular the spine. We also investigate different approaches to positioning of these landmarks employing CT datasets with annotated internal landmarks as training data. The general pipeline of our proposed method comprises the following steps: First, we train an active shape model using an existing avatar database and segmented CT surfaces. This stage also includes a gravity correction procedure, which accounts for shape changes due to the fact that the avatar models were obtained in standing position, while the CT data were acquired with patients in supine position. Second, we match the gravity-corrected avatar patient surface models to surfaces segmented from the CT datasets. In the last step, we derive the spatial relationships between the patient surface model and internal anatomical landmarks. RESULT: We trained and evaluated our method using cross-validation using 20 datasets, each containing 50 internal landmarks. We further compared the performance of four different generalized linear models' setups to describe the positioning of the internal landmarks relative to the patient surface. The best mean estimation error over all the landmarks was achieved using lasso regression with a mean error of [Formula: see text]. CONCLUSION: Considering that interventional X-ray imaging systems can have detectors covering an area of about [Formula: see text] ([Formula: see text]) at iso-center, this accuracy is sufficient to facilitate automatic positioning of the X-ray system.

Cytological analysis and structural quantification of FtsZ1-2 and FtsZ2-1 network characteristics in Physcomitrella patens.

Although the concept of the cytoskeleton as a cell-shape-determining scaffold is well established, it remains enigmatic how eukaryotic organelles adopt and maintain a specific morphology. The Filamentous Temperature Sensitive Z (FtsZ) protein family, an ancient tubulin, generates complex polymer networks, with striking similarity to the cytoskeleton, in the chloroplasts of the moss Physcomitrella patens. Certain members of this protein family are essential for structural integrity and shaping of chloroplasts, while others are not, illustrating the functional diversity within the FtsZ protein family. Here, we apply a combination of confocal laser scanning microscopy and a self-developed semi-automatic computational image analysis method for the quantitative characterisation and comparison of network morphologies and connectivity features for two selected, functionally dissimilar FtsZ isoforms, FtsZ1-2 and FtsZ2-1. We show that FtsZ1-2 and FtsZ2-1 networks are significantly different for 8 out of 25 structural descriptors. Therefore, our results demonstrate that different FtsZ isoforms are capable of generating polymer networks with distinctive morphological and connectivity features which might be linked to the functional differences between the two isoforms. To our knowledge, this is the first study to employ computational algorithms in the quantitative comparison of different classes of protein networks in living cells.

Computational 3D imaging to quantify structural components and assembly of protein networks.

Traditionally, protein structures have been described by the secondary structure architecture and fold arrangement. However, the relatively novel method of 3D confocal microscopy of fluorescent-protein-tagged networks in living cells allows resolving the detailed spatial organization of these networks. This provides new possibilities to predict network functionality, as structure and function seem to be linked at various scales. Here, we propose a quantitative approach using 3D confocal microscopy image data to describe protein networks based on their nano-structural characteristics. This analysis is constructed in four steps: (i) Segmentation of the microscopic raw data into a volume model and extraction of a spatial graph representing the protein network. (ii) Quantifying protein network gross morphology using the volume model. (iii) Quantifying protein network components using the spatial graph. (iv) Linking these two scales to obtain insights into network assembly. Here, we quantitatively describe the filamentous temperature sensitive Z protein network of the moss Physcomitrella patens and elucidate relations between network size and assembly details. Future applications will link network structure and functionality by tracking dynamic structural changes over time and comparing different states or types of networks, possibly allowing more precise identification of (mal) functions or the design of protein-engineered biomaterials for applications in regenerative medicine. STATEMENT OF SIGNIFICANCE: Protein networks are highly complex and dynamic structures that play various roles in biological environments. Analyzing the detailed spatial structure of these networks may lead to new insight into biological functions and malfunctions. Here, we propose a tool set that extracts structural information at two scales of the protein network and allows therefore to address questions such as "how is the network built?" or "how networks grow?".

Sost deficiency led to a greater cortical bone formation response to mechanical loading and altered gene expression.

Bone adaptation optimizes mass and structure, but the mechano-response is already reduced at maturation. Downregulation of sclerostin was believed to be a mandatory step in mechano-adaptation, but in young mice it was shown that load-induced formation can occur independent of sclerostin, a product of the Sost gene. We hypothesized that the bone formation and resorption response to loading is not affected by Sost deficiency, but is age-specific. Our findings indicate that the anabolic response to in vivo tibial loading was reduced at maturation in Sost Knockout (KO) and littermate control (LC) mice. Age affected all anabolic and catabolic parameters and altered Sost and Wnt target gene expression. While load-induced cortical resorption was similar between genotypes, loading-induced gains in mineralizing surface was enhanced in Sost KO compared to LC mice. Loading led to a downregulation in expression of the Wnt inhibitor Dkk1. Expression of Dkk1 was greater in both control and loaded limbs of Sost KO compared to LC mice suggesting a compensatory role in the absence of Sost. These data suggest physical activity could enhance bone mass concurrently with sclerostin-neutralizing antibodies, but treatment strategies should consider the influence of age on ultimate load-induced bone mass gains.

Tomography-Based Quantification of Regional Differences in Cortical Bone Surface Remodeling and Mechano-Response.

Bone has an adaptive capacity to maintain structural integrity. However, there seems to be a heterogeneous cortical (re)modeling response to loading at different regions within the same bone, which may lead to inconsistent findings since most studies analyze only one region. It remains unclear if the local mechanical environment is responsible for this heterogeneous response and whether both formation and resorption are affected. Thus, we compared the formation and resorptive response to in vivo loading and the strain environment at two commonly analyzed regions in the mouse tibia, the mid-diaphysis and proximal metaphysis. We quantified cortical surface (re)modeling by tracking changes between geometrically aligned consecutive in vivo micro-tomography images (time lapse 15 days). We investigated the local mechanical strain environment using finite element analyses. The relationship between mechanical stimuli and surface (re)modeling was examined by sub-dividing the mid-diaphysis and proximal metaphysis into 32 sub-regions. In response to loading, metaphyseal cortical bone (re)modeled predominantly at the periosteal surface, whereas diaphyseal (re)modeling was more pronounced at the endocortical surface. Furthermore, different set points and slopes of the relationship between engendered strains and remodeling response were found for the endosteal and periosteal surfaces at the metaphyseal and diaphyseal regions. Resorption was correlated with strain at the endocortical, but not the periosteal surfaces, whereas, formation correlated with strain at all surfaces, except at the metaphyseal periosteal surface. Therefore, besides mechanical stimuli, other non-mechanical factors are likely driving regional differences in adaptation. Studies investigating adaptation to loading or other treatments should consider region-specific (re)modeling differences.

The Periosteal Bone Surface is Less Mechano-Responsive than the Endocortical.

Dynamic processes modify bone micro-structure to adapt to external loading and avoid mechanical failure. Age-related cortical bone loss is thought to occur because of increased endocortical resorption and reduced periosteal formation. Differences in the (re)modeling response to loading on both surfaces, however, are poorly understood. Combining in-vivo tibial loading, in-vivo micro-tomography and finite element analysis, remodeling in C57Bl/6J mice of three ages (10, 26, 78 week old) was analyzed to identify differences in mechano-responsiveness and its age-related change on the two cortical surfaces. Mechanical stimulation enhanced endocortical and periosteal formation and reduced endocortical resorption; a reduction in periosteal resorption was hardly possible since it was low, even without additional loading. Endocortically a greater mechano-responsiveness was identified, evident by a larger bone-forming surface and enhanced thickness of formed bone packets, which was not detected periosteally. Endocortical mechano-responsiveness was better conserved with age, since here adaptive response declined continuously with aging, whereas periosteally the main decay in formation response occurred already before adulthood. Higher endocortical mechano-responsiveness is not due to higher endocortical strains. Although it is clear structural adaptation varies between different bones in the skeleton, this study demonstrates that adaptation varies even at different sites within the same bone.

Aging Leads to a Dysregulation in Mechanically Driven Bone Formation and Resorption.

Physical activity is essential to maintain skeletal mass and structure, but its effect seems to diminish with age. To test the hypothesis that bone becomes less sensitive to mechanical strain with age, we used a combined in vivo/in silico approach. We investigated how maturation and aging influence the mechanical regulation of bone formation and resorption to 2 weeks of noninvasive in vivo controlled loading in mice. Using 3D in vivo morphometrical assessment of longitudinal microcomputed tomography images, we quantified sites in the mouse tibia where bone was deposited or resorbed in response to controlled in vivo loading. We compared the (re)modeling events (formation/resorption/quiescent) to the mechanical strains induced at these sites (predicted using finite element analysis). Mice of all age groups (young, adult, and elderly) responded to loading with increased formation and decreased resorption, preferentially at high strains. Low strains were associated with no anabolic response in adult and elderly mice, whereas young animals showed a strong response. Adult animals showed a clear separation between strain ranges where formation and resorption occurred but without an intermediate quiescent "lazy zone". This strain threshold disappeared in elderly mice, as mechanically induced (re)modeling became dysregulated, apparent in an inability to inhibit resorption or initiate formation. Contrary to what is generally believed until now, aging does not shift the mechanical threshold required to initiate formation or resorption, but rather blurs its specificity. These data suggest that pharmaceutical strategies augmenting physical exercise should consider this dysfunction in the mechanical regulation of bone (re)modeling to more effectively combat age-related bone loss.

Monitoring in vivo (re)modeling: a computational approach using 4D microCT data to quantify bone surface movements.

Bone undergoes continual damage repair and structural adaptation to changing external loads with the aim of maintaining skeletal integrity throughout life. The ability to monitor bone (re)modeling would allow for a better understanding in how various pathologies and interventions affect bone turnover and subsequent bone strength. To date, however, current methods to monitor bone (re)modeling over time and in space are limited. We propose a novel method to visualize and quantify bone turnover, based on in vivo microCT imaging and a 4D computational approach. By in vivo tracking of spatially correlated formation and resorption sites over time it classifies bone restructuring into (re)modeling sequences, the spatially and temporally linked sequences of formation, resorption and quiescent periods on the bone surface. The microCT based method was validated using experimental data from an in vivo mouse tibial loading model and ex vivo data of the mouse tibia. In this application, the method allows the visualization of time-resolved cortical (re)modeling and the quantification of short-term and long-term modeling on the endocortical and periosteal surface at the mid-diaphysis of loaded and control mice tibiae. Both short-term and long-term modeling processes, independent formation and resorption events, could be monitored and modeling (spatially not correlated formation and resorption) and remodeling (resorption followed by new formation at the same site) could be distinguished on the bone surface. This novel method that combines in vivo microCT with a computational approach is a powerful tool to monitor bone turnover in animal models now and is waiting to be applied to human patients in the near future.