An Approximate Electromagnetic Model for Optimizing Wireless Charging of Biomedical Implants.

OBJECTIVE: Computational modeling is increasingly used to design charging systems for implanted medical devices. The design of these systems must often satisfy conflicting requirements, such as charging speed, specific absorption rate (SAR) and coil size. Fast electromagnetic solvers are pivotal for enabling multi-criteria optimization. In this paper, we present an analytical model based on the quasi-static approximation as a fast, yet sufficiently accurate tool for optimizing inductive charging systems. METHODS: The approximate model was benchmarked against full-wave simulations to validate accuracy and improvement in computation time. The coupling factor of two test coils was measured for lateral and axial displacements and the SAR was measured experimentally in a PAA phantom. RESULTS: The approximate model takes only 11 seconds to compute a single iteration, while the full-wave model takes 5 hours to compute the same case. The maximum difference with full-wave simulations was less than 24% and the mean difference less than 2%. Adding a novel figure of merit into the multi-criterion optimization resulted in a 16% higher charging speed. The measured results of the SAR and coupling factor are within a 5 mm coil offset margin. CONCLUSION: The proposed approximate model succeeds as a rapid prototyping tool, enabling fast and sufficiently accurate optimization for wireless charging systems. SIGNIFICANCE: The approximate model is the first of its kind to compute both the coupling factor and the SAR near conducting structures fast enough to enable optimization of charging speed.

Efficient two-trait-locus linkage analysis through program optimization and parallelization: application to hypercholesterolemia.

We have optimized and parallelized the GENEHUNTER-TWOLOCUS program that allows to perform linkage analysis with two trait loci in the multimarker context. The optimization of the serial program, before parallelization, results in a speedup of a factor of more than 10. The parallelization affects the two-locus-score calculation, which is predominant in terms of computation time. We obtain perfect speedup, that is, the computation time decreases exactly by a factor of the number of processors. In addition, two-locus LOD and NPL scores are now calculated for varying genetic positions of both disease loci, not just one locus varied and the position of the other disease locus fixed, as before. This results in easily interpretable 3-D plots. We have reanalyzed a pedigree with hypercholesterolemia using our new version of GENEHUNTER-TWOLOCUS. Whereas originally, two individuals had to be discarded due to excessive computation-time demands, the entire 17-bit pedigree could now be analyzed as a whole. We obtain a two-trait-locus LOD score of 5.49 under a multiplicative model, compared to LOD scores of 3.08 and 2.87 under a heterogeneity and additive model, respectively. This further increases evidence for linkage to both 1p36.1-p35 and 13q22-q32 regions, and corroborates the hypothesis that the two genes act in a multiplicative way on LDL cholesterol level. Furthermore, we compare the computation times for two-trait-locus analysis needed by the programs GENEHUNTER-TWOLOCUS, TLINKAGE, and SUPERLINK. Altogether, our algorithmic improvements of GENEHUNTER-TWOLOCUS allow researchers to analyze complex diseases under realistic two-trait-locus models with pedigrees of reasonable size and using many markers.