Human brain consists of grey and white matter. Grey matter largely forms the outer layer of the brain, and is responsible for decision making and functioning of the human body. Complementary, the white matter (WM) contains the communication pathways between the grey matter areas. Diffusion-weighted magnetic resonance imaging (dMRI) is an imaging modality allowing to model the brain’s white matter structures by making the MRI acquisition sensitive to diffusion processes. When brain structure is altered due to pathology, it can be assessed and quantified by analysing dMRI data...@en

An important aspect in increasing our health and safety is the development of new sensors for screening drinking water samples for the presence of microbiological contaminants.The main problem associated with the detection and identification of these microbial contaminants is the long process time. The majority of the time is required fortheir purification and multiplication due to the low concentrations in which they arefound. These steps can be avoided by using spectroscopic identification rather than biochemical techniques. Raman spectroscopy provides a fingerprint based on the vibrational states of molecular bonds from which the bio-particles containing these can be identified. The Raman effect is weak, but rich in information and flexible with respect to its excitation wavelength.When working in an aqueous environment it can take advantage of the absorption minima of water to out perform for instance IR spectroscopy. Optical trapping allows the immobilization of particles without additional preparation steps and provides much added value to, and is highly compatible with Raman spectroscopy. Lab-on-a-chip techniques allow for integration and large-scale parallelization of processes, whichis unavoidable when performing large-scale identification of microbial contaminants on the level of single cells. To take advantage of established CMOS processing techniques for mass production in electronics, a CMOS compatible technology for integrated photonics providing waveguides transparent at a Raman suitable wavelengths, is needed. TripleX is such a waveguide technology. The research presented in this thesis shows the realization of an integrated dual-waveguide optical trap and the feasibility of its use for the identification of micro-organisms based on their Raman spectrum induced by the same on-chip optical beams used for trapping. It does so in fourmain steps. Firstly, laser tweezers Raman spectroscopy is used to classify the closely related yeast species Kluyveromyces lactis and Saccharomyces cerevisiae from measurements at the single cell level. Laser tweezers Raman spectroscopy combines optical trapping of a cell and generation of its characteristic Raman spectrum using one laser-beam focus. This enables fingerprinting of a cell’s molecular composition in a harmless fluidic environment within minutes. Laser tweezers Raman spectroscopy is considerably faster than well-known biological techniques based on streak plating or PCR, and requires far less biological material. For each yeast species a training set and a test set were measured. Visual inspection of the spectra showed intra-species variations obstructing division into two classes by eye. Application of a classification rule based on Fisher’s criterion nevertheless led to the successful blind classification of the test-set cells. Finally, a Kolmogorov-Smirnov test indicated that the difference between the distributions of the species was statistically significant, implying biological origin of the classification. This successful extension of laser tweezers Raman spectroscopy to classification of the aforementioned yeasts underlinesits applicability in microbiology and will hopefully contribute to the process of its adoption in this discipline. Laser tweezers Raman spectroscopy is not limited to rapid classification of single cells, but may also include e.g. study of the cell metabolism. Secondly, a new approach to the dual-beam geometry for on-chip optical trapping and Raman spectroscopy, using box shaped-waveguides microfabricated in TripleX technologyis demonstrated. These waveguides consist of SiO2 and Si3N4, so as to provide alow index contrast with respect to the SiO2 claddings and low signal loss, while retaining the advantages of Si3N4. The waveguides enable both the trapping and Raman functionality with the same dual beams. Polystyrene beads of 1µm diameter can be trapped with this device. In the axial direction discrete trapping positions occur, owing to the intensity pattern of the interfering beams. Interpretation of the trapping events on the basis of simulated optical fields and calculated optical forces indicate that a strong trap is formed by the beams emitted by the waveguides. Furthermore, the acquisition of Raman spectraof a single trapped bead is demonstrated. The spectra obtained in this manner show distinct polystyrene Raman peaks for integration times as short as 0.25 seconds. Thirdly, usual procedure of background subtraction is found to be less effective for Raman spectra obtained with the dual-waveguide trap, due to its specific geometry. The differences in the Raman generating properties between four dual-waveguide traps with varying distances between their waveguide facets are explored using a saturated ascorbic acid solution. Furthermore, the origin of a periodic background observed in the ascorbic acid spectra is investigated. Finally, an alternative method of signal acquisition and processing is presented to deal with the lack of fluidic control in the device and the periodic background and low signal-to-noise ratio observed in the spectra. The 10 µm and 5µm traps are used in trapping and Raman generation experiments with biological relevant particles in the form of Bacillus subtilis spores. These experiments result in noisy spectra for many and few spores in both traps. Using the presented processing the spectra are identified as Bacillussubtilis spore spectra. A comparison of the obtained signal-to-noise values to literature benchmarks shows the feasibility of micro-organisms identification with the dual-waveguide trap.@en

Single molecule localization microscopy is a powerful tool that delivers high contrast and imaging specificity at a resolution beyond that of conventional microscopes. To obtain a super-resolved image, one needs to image at least hundred thousand camera frames and estimate the position of millions of molecules with nanometer precision. The tremendous amount of data that needs to be analyzed is one of the challenges scientists face when applying single molecule localization techniques. For this reason, a maximum likelihood estimation method is proposed in this thesis that attains the Cramer-Rao Lower Bound and estimates the position of single molecules in parallel on a GPU to achieve near real-time processing with high precision. A major drawback of current methods that detect single molecules is that the number of false positives is unknown. Therefore, a generalized likelihood ratio test is proposed here, which can control both the number of false positives and true positives withminimal user input. This target is stably achieved in the experiment over a large range of signal to background conditions. A key application of single molecule localization microscopy can be found in in vivo RNA imaging. In this thesis two RNA studies are presented: i) The nuclear pore complex structures and the kinetic interaction with mRNA are investigated, where a point mutation of mex67p triggered the nuclear export efficiency to drop significantly; and ii) A study on the mobility and occupation of mRNA within the nucleus is performed by instantaneously imaging the whole three-dimensional volume ofmRNAs in the nucleus of a living cell. Fromthis study, no evidence was obtained for exclusion or enrichment of the heterochromatin by mRNAs. For the general applicability of singlemolecule localization microscopy, the two-dimensionalmethods will need to be extended to three-dimensions. In threedimensional imaging small aberrations will become significant when imaging away from focus and therefore need to be compensated or calibrated. This thesis shows that one can extend single molecule localization into three-dimensions, or even a fourth-dimension such as color when the point spread function of a microscope is correctly distorted. Additionally, an adaptive optics strategy is presented that can correct for sample induced aberrations, and a method is proposed to encode emission color of the single molecule into the measurement.@en

The millions of filter weights in Convolutional Neural Networks (CNNs), all have a well-defined and analytical expression for the partial derivative to the loss function. Therefor these weights can be learned from data with a technique called gradient descent optimization. While the filter weights have a well-defined derivative, the filter size has not. There is currently no other way to optimize filter sizes, then with an exhaustive search over multiple CNNs trained with different filter sizes. In this report, we propose a new filter called Structured Receptive Field of which the filter size can be optimized during the training stage. This new filter constitutes a parameterization of a normal filter as a linear combination of all 2D Gaussian derivatives up to a certain order. Instead of learning the weights of the resulting filter directly, we learn the weights of the linear combination and the sigma of the Gaussian derivatives that implicitly define the filter weights. The advantage of parameterizing normal filters in this way, is that gradient descent optimization of the continuous sigma parameter which has a well-defined derivative, can be used as a proxy for optimizing the discrete filter size which has no well-defined derivative. The basic idea for this parameterization comes from scale-space theory in which the scale of image features is studied by parameterizing the image features with a continuous scale parameter. Thereby explicitly decoupling the spatial structure of image feature from the scale at which it occurs in the image. Structured receptive fields have several compelling advantages: they can learn bigger filters without increasing the number of learnable parameters and without increasing the complexity of the structure of the filter. In this report, we both provide theoretical and empirical evidence that structured receptive fields can approximate any filter learned by normal CNNs. I.e. we show that structured receptive fields of order 4 can approximate the filters in all layers of a pre-trained AlexNet. Furthermore, we demonstrate empirically that structured receptive fields can indeed learn their own filter size during training, and that this extra ability over normal filters seems to give them an advantage over normal filters when used for classification tasks. I.e. by replacing normal filters in a DenseNet architecture and keeping every- thing else the same, a ~1% higher test accuracy was obtained on the highly competitive CIFAR-10 benchmark dataset. It would not be wise to draw hard conclusions from a single training run on a single dataset, but this result definitely looks promising. In future research, we hope to demonstrate that, because of their extra ability to learn their filter size, replacing normal filters with structured receptive fields will always lead to strictly better or equal performance.

One of the most crucial estimates obtained from reflection seismology is the seismic image. It provides a map of the subsurface reflectivities. However, in order to construct an accurate map an accurate propagation velocity model is needed. For simple geologic environments an isotropic velocity model is sufficient, however, for complex geologic environments an anisotropic velocity model is more appropriate and more realistic in describing wave propagation. Ignoring the anisotropic kinematics in these geologic environments will most definitely lead to sub-optimal or even poor imaging results, especially with the tendency of today's acquisition geometries that include measurements at large source-receiver offsets...@en

Ultrasound sensors based on integrated photonics devices provide a new solution to meet the miniaturization demand for the detection functionality of transducers. An important example is the silicon ring-resonator (RR) ultrasound sensor developed in our department. This sensor combines a small footprint with high sensitivity and a low detection limit. Compared with piezoelectric transducers, it has additional advantages of low cost, mass producibility, and immunity to electromagnetic interference. It is also possible to build an array of these RR sensors on a single chip. These merits make the sensor appealing for ultrasound detection in general. However, there are three challenges that have arisen from this previous work on the RR ultrasound sensor. These challenges relate to specific properties of such silicon RR ultrasound sensors and to the high sensitivity of silicon integrated photonics devices to fabrication variations. More specifically, difficulties in interrogating RR ultrasound sensors need to be overcome. Further, inherent drawbacks of RR ultrasound sensors need to be addressed, suggesting to look for an alternative silicon integrated photonics ultrasound sensor. Finally, design methods need to be developed for making RRs and Mach-Zehnder interferometers (MZIs), which are widely used in silicon photonics, robust to fabrication variations. This thesis addresses the three challenges sequentially. We developed an integrated photonics interrogator to better read out the RR sensor, a silicon MZI based ultrasound sensor to overcome the RR sensor's drawbacks, and design methods for silicon RRs and MZIs with a free spectral range insensitive to fabrication variations. @en