Universität zu Lübeck

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    850 nm Fourier domain mode-locked laser for ophthalmic optical coherence tomography imaging
    (2025) Klufts, Marie
    Non-invasive imaging techniques have become essential in medical diagnostics over the past few decades. Among these, Optical Coherence Tomography (OCT) offers micrometer resolution with millimeter-scale depth penetration, making it particularly valuable in ophthalmology. OCT captures backscattered light to generate 3D volumes. For eye imaging, wavelengths around 850 nm are ideal due to minimal absorption by the vitreous and high scattering in the upper retinal layers. Imaging speed is also critical, as faster speeds reduce motion artifacts. Swept-source OCT, using wavelength-tunable lasers, enables high-speed imaging. Fourier Domain Mode-Locked (FDML) lasers providing megahertz-level scan rates are ideal for this purpose. This thesis explores the development and application of FDML lasers for ophthalmic imaging. Unlike other tunable lasers, FDML lasers have a unique design that stores a full sweep in their fiber cavity for hundreds of round trips, avoiding rebuilding of lasing from spontaneous emission after tuning to new wavelengths offering high phase stability and long coherence length necessary for high quality OCT images. A new megahertz FDML laser at 850 nm would merge the unique advantages of this wavelength with the proven benefits of FDML lasers allowing for a low latency, dynamic view of the retina, opening new doors for real-time diagnostics. The first part delves into the challenges of developing an FDML laser around 850 nm, addressing issues like polarization mode dispersion, chromatic dispersion, and low gain/loss ratios. These factors contribute to the complexity of managing short wavelength OCT lasers, which explain their scarcity to date. The second part presents in-vivo ophthalmic OCT imaging results, with comparisons to other imaging techniques. The newly designed FDML laser demonstrates strong performance for OCT imaging, achieving an axial resolution below 10 µm, sensitivity above 84 dB, and a ranging depth of 1.4 cm. Also, its high phase stability, with a time jitter of 25 ps over 1,000 sweeps, makes it suitable for phaseresolved techniques. Retinal images were captured at 414,000 axial scans per second using a master-slave based calibration technique, at 828 kHz with bidirectional sweeping, and at 1.7 MHz using optical buffering with a single-k-calibration technique. While increased scattering at 850 nm limits choroidal imaging, most retinal layers of interest are clearly visible. This FDML laser highlights the advantages of short-wavelength, high-speed imaging and paves the way for new applications.
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    A Fourier-analytical approach for field-free-point magnetic particle imaging
    (2025) Maaß, Marco
    Magnetic particle imaging is a tracer-based medical imaging technique that measures the spatial distribution of superparamagnetic nanoparticles. Alternating magnetic fields with different excitation sequences are used to measure the nanoparticle distribution in a scanner. Usually, the simplified Langevin model of paramagnetism is used as a first approximation for the complicated nonlinear magnetization behavior of nanoparticles. Although the modified Langevin model of paramagnetism can provide suitable image reconstructions for one-dimensional excitation, the situation is more complicated for higher-dimensional excitation, as several aspects cannot be fully explained by the Langevin model. A well-known example is the spatial similarity of the frequency components of the system function with tensor products of Chebyshev polynomials. This was observed for a higher-dimensional excitation of the Lissajous trajectory type and was unproven for almost ten years. With the aim of explaining such observations mathematically, this thesis makes an important contribution to the mathematical foundations of magnetic particle imaging. To this end, the spatio-temporal system function based on the Langevin model is transformed into the frequency domain using various concepts of Fourier analysis. The scientific contribution of the newly developed mathematical framework is manifold. Firstly, the developed model is able to separate the scanner-dependent excitation from the particle magnetization model, allowing better utilization of the imaging operator so that faster reconstruction methods could be developed. Secondly, it is now easier to investigate both the effect of the magnetization model and that of the excitation sequence in the imaging model separately. Thus, an extended equilibrium magnetization model is introduced in this thesis and a series representation is developed for it. Furthermore, the exact relationship between the frequency components of the system function and the tensor products of Chebyshev polynomials is shown for excitations of the Lissajous trajectory type. Finally, using the developed mathematical framework, the frequency representations of various excitation sequences known from the literature are calculated, which further increases the applicability of the model for magnetic particle imaging.
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    Action regulation in energy-efficient driving
    (2025) Moll, Vivien Esther
    Battery electric vehicles (BEVs) offer substantial potential for reducing emissions but introduce cognitive and behavioural challenges for energy-efficient driving. In contrast to internal combustion engine vehicles (ICEVs), energy flow in BEVs is less tangible, and relevant consumption patterns are more complex to perceive, predict, and interpret. Current ecodriving research often lacks cognitive grounding, a focus on the specific challenges in BEVs, and a profound analysis beyond performance measures. This dissertation addresses the need for user-centred, cognitively aligned feedback by examining how different feedback approaches affect drivers’ perception, judgements, behaviour, knowledge, and perceived support of action regulation and the mental model of ecodriving. The theoretical foundation integrates adaptive control and action regulation models, cognitive information processing, and the role of mental models and perceived capability in goal-directed behaviour. It posits that energy-efficient driving with BEVs requires continuous situational adaptation and knowledge-based reasoning. Four empirical studies were conducted using experimental designs combined with qualitative and quantitative methods across diverse settings, including an online experiment, driving simulations, and real-world driving. Each study assessed both subjective and objective indicators of action regulation and knowledge. Study 1 (N = 55, online experiment) laid the conceptual foundation by exploring how drivers interpret typical consumption feedback derived from simplified acceleration dynamics. Rooted in bounded rationality, results revealed a systematic overestimation of energy use, particularly for high and brief maximum consumption values. There was no significant correlation between the correct energy efficiency ranking and the ranking derived from participants’ estimations. The study also identified interindividual differences in heuristic information processing, showing that both stimulus properties and cognitive predispositions shape perception. Study 2 (N = 63, driving simulator study) focused on knowledge gaps and their behavioural implications. It contrasted three feedback approaches: a baseline without support, a consumption trace display, and a recommendation system indicating optimal speed. Drivers frequently relied on incomplete or inaccurate conceptions of energy efficiency. While those using the recommendation system felt less uncertain, this confidence did not translate into better performance or more accurate knowledge. However, their tendency to verbalise more vehicle- and environment-related information suggests a more active reasoning process regarding energy-efficient driving. Study 3 (N = 50, field study) built on these findings and introduced a comprehension-based approach with pre-drive tip lists. When behavioural strategies were paired with technical reasoning, drivers reported higher perceived knowledge, stronger support for action regulation and the mental model, and better driving performance. This highlights the potential of explanation-based feedback to improve effectiveness, knowledge, and user experience. Study 4 (N = 112, driving simulator study) extended this approach into real-time driving by integrating elaborated auditory ecodriving tips into a recommendation system. This combined approach significantly improved driving performance and strengthened perceived mental model support, although cognitive load, information acquisition, and subjective information processing awareness were negatively influenced. The dissertation offers novel instruments and methods to evaluate ecodriving feedback. Key contributions include a new experimental paradigm for assessing dynamic magnitude perception, and two new constructs: perceived support of action regulation and perceived support of the mental model, enabling a finer-grained evaluation of action regulation quality beyond conventional usability or satisfaction metrics. Furthermore, existing items for measuring perceived ecodriving knowledge were revised based on theoretical considerations. Finally, an AI-assisted method was employed to systematically analyse verbalised driving strategies and their technical explanations, demonstrating scalable content analysis. Theoretically, the dissertation integrates psychological frameworks with an emphasis on mental models and information processing, provides a systematic literature review, and links various feedback approaches to cognitive processing and behavioural regulation. Moreover, it extends established cognitive biases by identifying a novel bias specific to dynamic data visualisation. Empirically, it demonstrates that comprehension-oriented feedback can improve energy-efficient behaviour, deepen understanding, and enhance perceived support, especially when it explains behavioural strategies and clarifies causal relationships. The practical implications are synthesised into design guidelines for future feedback systems in BEVs and beyond. The innovations in this dissertation extend beyond the context of BEVs. Action regulation in complex and dynamic systems—such as aviation, industrial control, or AI-assisted decision-making, especially in light of the growing role of generative, speech-based AI—can benefit from these findings. When users must form accurate mental models or interpret raw data in real-time, feedback should explain mechanisms and facilitate information analysis rather than merely presenting outcomes. This dissertation lays the groundwork for future research on cognitively aligned feedback systems that foster effective action regulation, adequate mental models, and user experience.
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