Smart grid initialization reduces the computational complexity of multi-objective image registration based on a dual-dynamic transformation model to account for large anatomical differences

We recently demonstrated the strong potential of using dual-dynamic transformation models when tackling deformable image registration problems involving large anatomical differences. Dual-dynamic transformation models employ two moving grids instead of the common single moving grid for the target image (and single fixed grid for the source image). We previously employed powerful optimization algorithms to make use of the additional flexibility offered by a dual-dynamic transformation model with good results, directly obtaining insight into the trade-off between important registration objectives as a result of taking a multi-objective approach to optimization. However, optimization has so far been initialized using two regular grids, which still leaves a great potential of dual-dynamic transformation models untapped: a-priori grid alignment with image structures/areas that are expected to deform more. This allows (far) less grid points to be used, compared to using a sufficiently refined regular grid, leading to (far) more efficient optimization, or, equivalently, more accurate results using the same number of grid points. We study the implications of exploiting this potential by experimenting with two new smart grid initialization procedures: one manual expert-based and one automated image-feature-based. We consider a CT test case with large differences in bladder volume with and without a multi-resolution scheme and find a substantial benefit of using smart grid initialization

Characterising resistance to fatigue crack growth in adhesive bonds by measuring release of strain energy

Measurement of the energy dissipation during fatigue crack growth is used as a technique to gain more insight into the physics of the crack growth process. It is shown that the amount of energy dissipation required per unit of crack growth is determined by Gmax, whereas the total amount of energy available for crack growth in a single cycle is determined by (Δ √g)2

On the usefulness of gradient information in multi-objective deformable image registration using a B-spline-based dual-dynamic transformation model: comparison of three optimization algorithms

The use of gradient information is well-known to be highly useful in single-objective optimization-based image registration methods. However, its usefulness has not yet been investigated for deformable image registration from a multi-objective optimization perspective. To this end, within a previously introduced multi-objective optimization framework, we use a smooth B-spline-based dual-dynamic transformation model that allows us to derive gradient information analytically, while still being able to account for large deformations. Within the multi-objective framework, we previously employed a powerful evolutionary algorithm (EA) that computes and advances multiple outcomes at once, resulting in a set of solutions (a so-called Pareto front) that represents efficient trade-offs between the objectives. With the addition of the B-spline-based transformation model, we studied the usefulness of gradient information in multiobjective deformable image registration using three different optimization algorithms: the (gradient-less) EA, a gradientonly algorithm, and a hybridization of these two. We evaluated the algorithms to register highly deformed images: 2D MRI slices of the breast in prone and supine positions. Results demonstrate that gradient-based multi-objective optimization significantly speeds up optimization in the initial stages of optimization. However, allowing sufficient computational resources, better results could still be obtained with the EA. Ultimately, the hybrid EA found the best overall approximation of the optimal Pareto front, further indicating that adding gradient-based optimization for multiobjective optimization-based deformable image registration can indeed be beneficial

Dietrich Bonhoeffer en de bio-ethiek van het levensbegin

Masterthesis Ethie

Focal adhesion signaling in acute renal failure

The elucidation of the molecular and cellular mechanisms of ischemic ARF very important in finding new strategies to reduce or prevent renal injury. FAK is an important FA protein with tyrosine kinase and scaffolding function. The general goal of this thesis was to investigate the role of FAK during I/R. Using a unilateral renal I/R rat model, we show the presence of tyrosine phosphorylated FAs in vivo and disruption of FAs and the F-actin network after ischemia and rebuild during reperfusion. FAK phosphorylation occured on different tyrosine residues during the reperfusion implicating a role of FAK. ERK is known to be involved in FA signaling. We studied the role of ERK signaling pathway during I/R in vivo using the inhibitor U0126. Inhibition prevented the changes in FA protein phosphorylation after ischemia and diminished injury. We used an inducible proximal tubule cell specific FAK knockout model to investigate the role of FAK in I/R. We show that FAK knockout mice are less susceptible to I/R injury compared to their wildtype littermates. Furthermore we studied FAK signaling under normal and ATP depletion in vitro. FAK deleted renal cells show no differences in morphology. However FAK knockout cells have increased FAs, aberrant stress fibers and impaired spreading. During recovery from ATP depletion, FAK deleted cells show impaired recovery of FAs and stress fibers.UBL - phd migration 201

Fitness-based Linkage Learning and Maximum-Clique Conditional Linkage Modelling for Gray-box Optimization with RV-GOMEA

For many real-world optimization problems it is possible to perform partial evaluations, meaning that the impact of changing a few variables on a solution's fitness can be computed very efficiently. It has been shown that such partial evaluations can be excellently leveraged by the Real-Valued GOMEA (RV-GOMEA) that uses a linkage model to capture dependencies between problem variables. Recently, conditional linkage models were introduced for RV-GOMEA, expanding its state-of-the-art performance even to problems with overlapping dependencies. However, that work assumed that the dependency structure is known a priori. Fitness-based linkage learning techniques have previously been used to detect dependencies during optimization, but only for non-conditional linkage models. In this work, we combine fitness-based linkage learning and conditional linkage modelling in RV-GOMEA. In addition, we propose a new way to model overlapping dependencies in conditional linkage models to maximize the joint sampling of fully interdependent groups of variables. We compare the resulting novel variant of RV-GOMEA to other variants of RV-GOMEA and VkD-CMA on 12 problems with varying degree of overlapping dependencies. We find that the new RV-GOMEA not only performs best on most problems, also the overhead of learning the conditional linkage models during optimization is often negligible