Computationally-Optimized Bone Mechanical Modeling from High-Resolution Structural Images

Image-based mechanical modeling of the complex micro-structure of human bone has shown promise as a non-invasive method for characterizing bone strength and fracture risk in vivo. In particular, elastic moduli obtained from image-derived micro-finite element (μFE) simulations have been shown to correlate well with results obtained by mechanical testing of cadaveric bone. However, most existing large-scale finite-element simulation programs require significant computing resources, which hamper their use in common laboratory and clinical environments. In this work, we theoretically derive and computationally evaluate the resources needed to perform such simulations (in terms of computer memory and computation time), which are dependent on the number of finite elements in the image-derived bone model. A detailed description of our approach is provided, which is specifically optimized for μFE modeling of the complex three-dimensional architecture of trabecular bone. Our implementation includes domain decomposition for parallel computing, a novel stopping criterion, and a system for speeding up convergence by pre-iterating on coarser grids. The performance of the system is demonstrated on a dual quad-core Xeon 3.16 GHz CPUs equipped with 40 GB of RAM. Models of distal tibia derived from 3D in-vivo MR images in a patient comprising 200,000 elements required less than 30 seconds to converge (and 40 MB RAM). To illustrate the system's potential for large-scale μFE simulations, axial stiffness was estimated from high-resolution micro-CT images of a voxel array of 90 million elements comprising the human proximal femur in seven hours CPU time. In conclusion, the system described should enable image-based finite-element bone simulations in practical computation times on high-end desktop computers with applications to laboratory studies and clinical imaging

Mapping Trabecular Bone Fabric Tensor by in Vivo Magnetic Resonance Imaging

The mechanical competence of bone depends upon its quantity, structural arrangement, and chemical composition. Assessment of these factors is important for the evaluation of bone integrity, particularly as the skeleton remodels according to external (e.g. mechanical loading) and internal (e.g. hormonal changes) stimuli. Micro magnetic resonance imaging (µMRI) has emerged as a non-invasive and non-ionizing method well-suited for the repeated measurements necessary for monitoring changes in bone integrity. However, in vivo image-based directional dependence of trabecular bone (TB) has not been linked to mechanical competence or fracture risk despite the existence of convincing ex vivo evidence. The objective of this dissertation research was to develop a means of capturing the directional dependence of TB by assessing a fabric tensor on the basis of in vivo µMRI. To accomplish this objective, a novel approach for calculating the TB fabric tensor based on the spatial autocorrelation function was developed and evaluated in the presence of common limitations to in vivo µMRI. Comparisons were made to the standard technique of mean-intercept-length (MIL). Relative to MIL, ACF was identified as computationally faster by over an order of magnitude and more robust within the range of the resolutions and SNRs achievable in vivo. The potential for improved sensitivity afforded by isotropic resolution was also investigated in an improved µMR imaging protocol at 3T. Measures of reproducibility and reliability indicate the potential of images with isotropic resolution to provide enhanced sensitivity to orientation-dependent measures of TB, however overall reproducibility suffered from the sacrifice in SNR. Finally, the image-derived TB fabric tensor was validated through its relationship with TB mechanical competence in specimen and in vivo µMR images. The inclusion of trabecular bone fabric measures significantly improved the bone volume fraction-based prediction of elastic constants calculated by micro-finite element analysis. This research established a method for detecting TB fabric tensor in vivo and identified the directional dependence of TB as an important determinant of TB mechanical competence

High-quality computed tomography using advanced model-based iterative reconstruction

Computed Tomography (CT) is an essential technology for the treatment, diagnosis, and study of disease, providing detailed three-dimensional images of patient anatomy. While CT image quality and resolution has improved in recent years, many clinical tasks require visualization and study of structures beyond current system capabilities. Model-Based Iterative Reconstruction (MBIR) techniques offer improved image quality over traditional methods by incorporating more accurate models of the imaging physics. In this work, we seek to improve image quality by including high-fidelity models of CT physics in a MBIR framework. Specifically, we measure and model spectral effects, scintillator blur, focal-spot blur, and gantry motion blur, paying particular attention to shift-variant blur properties and noise correlations. We derive a novel MBIR framework that is capable of modeling a wide range of physical effects, and use this framework with the physical models to reconstruct data from various systems. Physical models of varying degrees of accuracy are compared with each other and more traditional techniques. Image quality is assessed with a variety of metrics, including bias, noise, and edge-response, as well as task specific metrics such as segmentation quality and material density accuracy. These results show that improving the model accuracy generally improves image quality, as the measured data is used more efficiently. For example, modeling focal-spot blur, scintillator blur, and noise iicorrelations enables more accurate trabecular bone visualization and trabecular thickness calculation as compared to methods that ignore blur or model blur but ignore noise correlations. Additionally, MBIR with advanced modeling typically outperforms traditional methods, either with more accurate reconstructions or by including physical effects that cannot otherwise be modeled, such as shift-variant focal-spot blur. This work provides a means to produce high-quality and high-resolution CT reconstructions for a wide variety of systems with different hardware and geometries, providing new tradeoffs in system design, enabling new applications in CT, and ultimately improving patient care

THE SPATIO-TEMPORAL BEHAVIOR OF BASIC MULTICELLULAR UNITS IN A PTH-INDUCED CORTICAL BONE REMODELING RABBIT MODEL

The adult skeleton is continuously renewed by the bone remodeling process, which is carried out by coupled and balanced activities, localized in time and space, via cellular groupings known as basic multicellular units (BMUs). In cortical bone, a BMU is depicted as a cutting cone of osteoclasts in the front resorbing bone, followed by a reversal phase, and then a closing cone lined by osteoblasts behind forming new bone. Any imbalance in this sequence of events can lead to bone diseases such as osteoporosis. Although it is well known that many factors affect BMU activity and contribute to osteoporosis, little is known about BMU dynamic spatio-temporal regulation. The rate of BMU progression, their longitudinal erosion rate (LER) is a key example of where knowledge is lacking. LER has only been inferred by 2D (histological) double-labeling techniques based on remodeling in a steady state where the cutting cone advance is equal to that of the closing cone. If these spatio-temporal relationships are valid and constant, increasing the bone formation rate, as observed with recombinant parathyroid hormone (PTH), an anabolic treatment for osteoporosis, would concomitantly elevate LER. The present study utilizes a new methodology to explore whether the increased cortical remodeling activity induced by PTH, including accelerated bone formation, leads to an elevated LER. BMU progression was manipulated via different dosing regimens: PTH and PTH withdrawal (PTHW). It was hypothesized that LER would be higher during active dosing. After 14 days of PTH dosing, rabbit distal right tibiae were imaged in vivo by synchrotron-based phase-contrast micro-CT. For the following 14 days, the PTH group received the same treatment while the PTHW group was administered saline. At 28 days, the rabbits were then euthanized, and the tibiae were imaged ex vivo by micro-CT. The in vivo and ex vivo right limb data sets were then registered, and LER was measured as the distance traversed by BMU cutting cones divided by 14 days. A total of 638 BMUs were assessed. Counter to the hypothesis, LER was lower in the PTH (34.61 µm/day) compared with the PTHW (39.37 µm/day; p < 0.01) group. Slower BMU progression suggests that PTH has an important role in enhancing coupling both by increasing bone formation and slowing the advance of bone resorption within BMUs. This novel insight into BMU dynamics indicates that further investigation into LER modulation is warranted, with potential implications for combatting remodeling-related disease, improving treatment, and potentially reducing drug side effects

ESTIMATION OF THE BLURRING KERNEL IN EXPERIMENTAL HR-PQCT IMAGES BASED ON MUTUAL INFORMATION

International audienceThe analysis of trabecular bone micro structure from in-vivo CT images is still limited due to limited spatial resolution even with the new High Resolution peripheral Quantitative CT (HR-pQCT) scanners. In previous works, it has been proposed to exploit super resolution techniques to improve spatial resolution. However, the application of such methods requires to know the blurring kernel, which is challenging for experimental HR-pQCT images. The goal of this work is to determine the blurring kernel of these scanners in order to facilitate an increase of the resolution of the bone images and of the segmentation of the bone structures. To this aim, we propose a method based on mutual information and compare it with classical L 2-norm minimization methods

Muscle activation patterns in shoulder impingement patients

Introduction: Shoulder impingement is one of the most common presentations of shoulder joint problems 1. It appears to be caused by a reduction in the sub-acromial space as the humerus abducts between 60o -120o – the 'painful arc'. Structures between the humeral head and the acromion are thus pinched causing pain and further pathology 2. Shoulder muscle activity can influence this joint space but it is unclear whether this is a cause or effect in impingement patients. This study aimed to observe muscle activation patterns in normal and impingement shoulder patients and determine if there were any significant differences. Method: 19 adult subjects were asked to perform shoulder abduction in their symptomatic arm and non-symptomatic. 10 of these subjects (age 47.9 ± 11.2) were screened for shoulder impingement, and 9 subjects (age 38.9 ± 14.3) had no history of shoulder pathology. Surface EMG was used to collect data for 6 shoulder muscles (Upper, middle and lower trapezius, serratus anterior, infraspinatus, middle deltoids) which was then filtered and fully rectified. Subjects performed 3 smooth unilateral abduction movements at a cadence of 16 beats of a metronome set at 60bpm, and the mean of their results was recorded. T-tests were used to indicate any statistical significance in the data sets. Significance was set at P<0.05. Results: There was a significant difference in muscle activation with serratus anterior in particular showing a very low level of activation throughout the range when compared to normal shoulder activation patterns (<30%). Middle deltoid recruitment was significantly reduced between 60-90o in the impingement group (30:58%).Trends were noted in other muscles with upper trapezius and infraspinatus activating more rapidly and erratically (63:25%; 60:27% respectively), and lower trapezius with less recruitment (13:30%) in the patient group, although these did not quite reach significance. Conclusion: There appears to be some interesting alterations in muscle recruitment patterns in impingement shoulder patients when compared against their own unaffected shoulders and the control group. In particular changes in scapula control (serratus anterior and trapezius) and lateral rotation (infraspinatus), which have direct influence on the sub-acromial space, should be noted. It is still not clear whether these alterations are causative or reactionary, but this finding gives a clear indication to the importance of addressing muscle reeducation as part of a rehabilitation programme in shoulder impingement patients