Cyril Flaig

Implant stability is affected by local bone microstructural quality

It is known that low bone quality, caused for instance by osteoporosis, not only increases the risk of fractures, but also decreases the performance of fracture implants; yet the specific mechanisms behind this phenomenon are still largely unknown. We hypothesized that especially peri-implant bone microstructure affects implant stability in trabecular bone, to a greater degree than more distant bone. To test this hypothesis we performed a computational study on implant stability in trabecular bone. Twelve humeral heads were measured using micro-computed tomography. Screws were inserted digitally into these heads at 25 positions. In addition, at each screw location, a virtual biopsy was taken. Bone structural quality was quantified by morphometric parameters. The stiffness of the 300 screw-bone constructs was quantified as a measure of implant stability. Global bone density correlated moderately with screw-bone stiffness (r2=0.52), whereas local bone density was a very good predictor (r2=0.91). The best correlation with screw-bone stiffness was found for local bone apparent Youngs modulus (r2=0.97), revealing that not only bone mass but also its arrangement in the trabecular microarchitecture are important for implant stability. In conclusion, we confirmed our hypothesis that implant stability is affected by the microstructural bone quality of the trabecular bone in the direct vicinity of the implant. Local bone density was the best single morphometric predictor of implant stability. The best predictability was provided by the mechanical competence of the peri-implant bone. A clinical implication of this work is that apparently good bone stock, such as assessed by DXA, does not guarantee good local bone quality, and hence does not guarantee good implant stability. New tools that could quantify the structural or mechanical quality of the peri-implant bone may help improve the surgical intervention in reaching better clinical outcomes for screw fixation.

A scalable memory efficient multigrid solver for micro-finite element analyses based on CT images

Micro-structural finite element (@mFE) analysis based on high-resolution computed tomography represents the current gold standard to predict bone stiffness and strength. Recent progress in solver technology makes possible simulations on large supercomputers that involve billions of degrees of freedom. In this paper we present an improved solver that has a significantly smaller memory footprint compared to the currently used solvers. This new approach fully exploits the information that is contained in the underlying CT image itself. It admits to execute all steps in the underlying multigrid-preconditioned conjugate gradient algorithm in matrix-free form. The reduced memory footprint allows to solve bigger bone models on a given hardware. It is an important step forward to the clinical usage of @mFE simulations. The new solver is fully parallel. We show almost perfect scalability up to 8000 cores of a Cray XT-5 supercomputer.

A hardware architecture for surface splatting

We present a novel architecture for hardware-accelerated rendering of point primitives. Our pipeline implements a refined version of EWA splatting, a high quality method for antialiased rendering of point sampled representations. A central feature of our design is the seamless integration of the architecture into conventional, OpenGL-like graphics pipelines so as to complement triangle-based rendering. The specific properties of the EWA algorithm required a variety of novel design concepts including a ternary depth test and using an on-chip pipelined heap data structure for making the memory accesses of splat primitives more coherent. In addition, we developed a computationally stable evaluation scheme for perspectively corrected splats. We implemented our architecture both on reconfigurable FPGA boards and as an ASIC prototype, and we integrated it into an OpenGL-like software implementation. Our evaluation comprises a detailed performance analysis using scenes of varying complexity.

On Smoothing Surfaces in Voxel Based Finite Element Analysis of Trabecular Bone

The (micro-)finite element analysis based on three-dimensional computed tomography (CT) data of human bone takes place on complicated domains composed of often hundreds of millions of voxel elements. The finite element analysis is used to determine stresses and strains at the trabecular level of bone. It is even used to predict fracture of osteoporotic bone. However, the computed stresses can deteriorate at the jagged surface of the voxel model. There are algorithms known to smooth surfaces of voxel models. Smoothing however can distort the element geometries. In this study we investigate the effects of smoothing on the accuracy of the finite element solution, on the condition of the resulting system matrix, and on the effectiveness of the smoothed aggregation multigrid preconditioned conjugate gradient method.

A highly scalable matrix-free multigrid solver for μ FE analysis based on a pointer-less octree

The state of the art method to predict bone stiffness is micro finite element (μFE) analysis based on high-resolution computed tomography (CT). Modern parallel solvers enable simulations with billions of degrees of freedom. In this paper we present a conjugate gradient solver that works directly on the CT image and exploits the geometric properties of the regular grid and the basic element shapes given by the 3D pixel. The data is stored in a pointer-less octree. The tree data structure provides different resolutions of the image that are used to construct a geometric multigrid preconditioner. It enables the use of matrix-free representation of all matrices on all levels. The new solver reduces the memory footprint by more than a factor of 10 compared to our previous solver ParFE. It allows to solve much bigger problems than before and scales excellently on a Cray XT-5 supercomputer.

Bone structure analysis on multiple GPGPUs

Osteoporosis is a disease that aects a growing number of people by increasing the fragility of their bones. To improve the understanding of the bone quality, large scale computer simulations are applied. A fast, scalable and memory ecient solver for such problems is ParOSol. It uses the preconditioned conjugate gradient algorithm with a multigrid preconditioner. A modication of ParOSol is presented that prots from the exorbitant compute capabilities of recent generalpurpose graphics processing units (GPGPUs). Adaptations of data structures for the GPGPU are discussed. The fastest implementation on a GPGPU achieves a speedup of more than ve compared with the CPU implementation and scales from 1 to at least 256 GPGPUs.