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    Artifact-suppressing reconstruction for X-ray near-field holography
    (2026)
    Dora, Johannes
    ;
    Knopp, Tobias
    X-ray near-field holography is an excellent tool for imaging objects with a resolution in the nanometer range. Full-field measurements with a lens-free microscopic measurement setup allow holograms of objects to be recorded at multiple magnifications. Unfortunately, these holograms can only be measured as an integral over a certain period of time. The phase information of the hologram is lost and only the intensities are recorded, commonly known as the phase problem. To recover the phase information, a spatial support and/or the recording of several holograms at different detector distances are usually used as additional information in reconstruction approaches. However, both constraints are only feasible for a very closed set of experiments, require time consuming optimization of hyper-parameters and a lot of computation time. Avoiding both techniques is a necessity to enable in-situ/operando measurements but, in doing so, reconstruction artifacts appear. Another challenge is the Autofocus problem. For the reconstruction of holographic data, the numerical focus of the measurement setup is part of the forward model and must be known very precisely. If the numerical focus is inaccurately embedded in the forward model of the reconstruction algorithm, it will generate artifacts or result in blurred reconstructed images. In practice, it is often difficult to accurately determine the Fresnel number for a good reconstruction. In the following work, the origin of possible artifacts as described above is investigated in detail and countermeasures are developed by adjusting the raw data preprocessing, by adjusting the reconstruction and by an automatic optimization of the forward model. The first part of this thesis addresses reconstruction artifacts with respect to a reference method, a projected gradient decent approach, using a Nesterov accelerated gradient. The reconstruction artifacts have the following identified causes: Truncation artifacts are caused by the limited field of view of the detector. The real hologram is cut off and as a result, the acquired image contains inconsistent areas at the borders. Mirroring the hologram in all directions in combination with a modified window function is derived as a countermeasure to reduce the impact of truncation edges. Low frequency artifacts are identified to be mainly caused by a global normalization error in the empty beam normalization or flat-field-correction. The normalization is corrected by introducing a constant offset parameter into the reconstruction. Other artifacts are overestimation artifacts, mainly caused by the Nesterov acceleration. The forward model amplifies this effect, which is sensitive to high spatial frequencies. To reduce the overestimation of the phase and absorption values, a regularization of the absorption values is applied, which is sufficient for both parameters simultaneously. The insensitivity of the forward model to low spatial frequencies leads to weak reconstruction artifacts. These are therefore reconstructed slowly since they have a small impact on the reconstruction loss. A newly introduced frequency-dependent Nesterov momentum and iterations on multiple grids explicitly accelerate the reconstruction of low frequencies. Both techniques also reduce an overestimation of high spatial frequencies. The combination of all described methods is summarized in a new artifact-suppressing reconstruction method (ASRM). Compared to the reference algorithm, it suppresses all artifacts mentioned above and reduces the reconstruction time significantly. The second part of the thesis proposes a solution to the autofocus problem with an approach to automatically optimize the Fresnel number. The approach consists of three problems to be solved. An error metric is required that quantifies the distance between the estimated forward model and the correct one. A global optimization has to be defined, which has to be solved by an appropriate solver. As an error metric, a model fit error (MFE) metric is derived from data inconsistencies that are introduced when a non-negative electron density constraint is applied to a defocused wave-field. The novel error metric is evaluated by simulation and experimental results and finally compared with other error metrics from the literature. On simulated data, the MFE metric performs similarly to comparative error metrics and significantly better on experimental data. For an automated solution of the autofocus problem, a global optimization problem with respect to the numerical focus is formulated. The expected uncertainty of the setup geometry is added as a constraint to the target function. Since the gradients of the target function are not trivially accessible, a gradient-less downhill simplex method is chosen as a solver. As fine tuning, the parameters for the ASRM algorithm are adjusted so that the MFE behaves optimally.
    Publicationtype: Doctoral Thesis
      60  56
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    On algorithmical methods facilitating clinical translation of magnetic particle imaging
    (2025)
    Scheffler, Konrad
    ;
    Knopp, Tobias
    Magnetic Particle Imaging (MPI) is an emerging tomographic imaging modality that has demonstrated significant potential in pre-clinical applications. As MPI moves towards clinical translation, the field faces a variety of interdisciplinary challenges that must be addressed through advancements in hardware, algorithmic methodologies, and application-driven refinements. This dissertation explores algorithmic methods that facilitate the transition of MPI from a research-oriented technology to a clinically viable imaging tool. The work presented in this dissertation highlights the crucial interplay between applicational demands, hardware design and limitations, and methodological development. Hardware innovations define the fundamental capabilities of MPI, such as sensitivity, resolution, and acquisition speed. However, these capabilities alone are insufficient to meet the stringent requirements of clinical imaging. Algorithmic research plays a pivotal role in bridging this gap by optimizing imaging performance, improving reconstruction accuracy, and addressing practical constraints in system usability. From accelerating system calibration to refining signal processing techniques and optimizing field encoding strategies, computational advancements serve as a critical link between hardware constraints and the needs of real-world medical applications. By focusing on the synergies between these three domains, this dissertation demonstrates how methodological improvements can drive the clinical adoption of MPI. Building on this foundation, the dissertation presents key methodological contributions that address specific challenges in MPI. The research contributions of the author are embedded within this broader framework, showcasing innovations that improve the efficiency and feasibility of MPI for clinical use. The core of the dissertation is composed of three peer-reviewed research articles, each addressing a distinct methodological challenge in MPI. The first contribution introduces a novel approach to system calibration by leveraging extrapolation techniques for system matrices. Traditional MPI calibration is time-intensive and requires extensive measurements, limiting the scalability of the method for clinical applications. By employing an extrapolation-based methodology, this work significantly reduces the calibration burden while maintaining reconstruction accuracy, thereby improving efficiency and reducing experimental effort. The second contribution focuses on regularization techniques in MPI reconstruction, presenting a method with automatically tuned parameters. Image reconstruction in MPI requires balancing noise suppression with the preservation of fine structural details, which is particularly crucial for medical imaging applications. This research introduces an adaptive regularization framework that dynamically adjusts parameters based on image characteristics, enhancing robustness, improving reconstruction quality, and reducing the need for manual parameter selection. The third contribution explores the use of ellipsoidal harmonic expansions to efficiently represent magnetic fields, addressing a fundamental challenge in MPI system modeling. Accurate field representations are essential for precise spatial encoding and the development of tailored field-cameras for MPI scanners. Compared to spherical harmonic representations, ellipsoidal harmonics provide greater geometric flexibility, enabling optimized field encoding strategies that are better suited for specific scanner designs. This study develops a mathematically rigorous yet efficient method for representing complex field distributions, offering improvements in encoding efficiency and adaptability to application-specific requirements. Additional research contributions contextualize these advancements within collaborative efforts involving multiple research institutions. These collaborations have furthered the development of MPI modeling techniques, investigated the MPI performance of the clinically approved tracer Resotran, and contributed to studies on the development of novel MPI tracers. The findings presented in this dissertation have been disseminated through various peer-reviewed journal articles and conference proceedings, reinforcing the significance of algorithmic research in driving the clinical translation of MPI. In conclusion, this dissertation demonstrates that algorithmic innovations are fundamental to bridging the gap between pre-clinical research and clinical implementation in MPI. By enhancing calibration processes, improving reconstruction methodologies, and optimizing field representations, these advancements facilitate the practical deployment of MPI in medical imaging. The final chapter provides a summary of the key findings and an outlook on future algorithmic research directions that will further support the integration of MPI into clinical workflows.
    Publicationtype: Doctoral Thesis
      70  70
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    Instrumentation, sequences and applications for magnetic particles in imaging and spectroscopy
    (2024)
    Mohn, Fabian
    ;
    Knopp, Tobias
    As an emerging medical imaging modality, magnetic particle imaging (MPI) has demonstrated considerable potential in imaging scenarios where established techniques are limited or have adverse effects. However, it still faces several challenges for future clinical integration and for achieving theoretical predictions of performance. MPI is a quantitative, tracer-based, and indirect imaging technique, that measures the non-linear response of magnetic nanoparticles (MNPs) at high temporal and spatial resolution with very high sensitivity. Major milestones such as the in vivo measurement of a beating mouse heart in 2009 or the first MPI-tailored MNPs in the same year, have been complemented by progress in various disciplines that constitute the research field of MPI. These include image reconstruction, signal encoding, MNP synthesis, applications and hardware instrumentation as main categories. Today, real-time reconstructions are feasible for perfusion imaging, as well as simultaneous measurement and signal separation of different MNP systems, and intentional interaction of MNPs with their immediate environment by mechanical activation, hyperthermia or targeted drug delivery. Initial progress toward the development of a full-scale, human-sized system by the Philips research division in Hamburg, Germany, in 2015 was accompanied by a number of challenges. The enormous engineering effort included the development of instrumentation needed to generate strong alternating and static magnetic fields in a cost-effective and maintainable manner, with a complexity similar to that of an Magnetic Resonance Imaging (MRI) system. Ultimately, the project struggled to maintain funding. At the Institute of Biomedical Imaging, Hamburg University of Technology, the challenge in instrumentation was addressed on a smaller scale, but still within the range of human proportions. A human-sized head scanner was introduced in 2019 that incorporated numerous straightforward solutions to previous difficulties, benefiting from smaller components and lower electrical specifications. A significant aspect of this thesis is the comprehensive redesign of this head-sized imaging system to align with clinical standards in terms of 3D acquisition speed, safety, and resolution. The design process incorporates both feasibility and clinical trial requirements, and achievable results are highlighted for an approved, but inferior, Ferucarbotran-based tracer. One part of this thesis is devoted to an in-depth examination of the transmit-receive signal chain of the device, accompanied by a comprehensive account of the development and design process of a high current linear toroidal transformer. The high-gain transformer can also be utilized in applications beyond medical imaging, operating in the kHz range, such as wireless power transmission. Another section in the present thesis investigates the suitability of a novel Ferucarbotran tracer, its MPI signal quality, composition, and performance in comparison to other MNPs. In the fields of neurology and interventional radiology, a head-sized imaging system can be employed for the prompt diagnosis of various types of stroke, including ischemic stroke, which represents a significant cause of mortality in the context of cardiovascular disease. Intracerebral hemorrhage represents the second most common type of stroke for which perfusion imaging serves as an invaluable diagnostic tool. Our head-sized imaging system may facilitate long-term monitoring of such patients. In this study, we propose a negative contrast perfusion imaging technique based on a long-circulating tracer and a saline bolus, which enables the repeated generation of trackable dynamic changes in MNP concentration without increasing the total administered iron dose. It leverages several significant advantages of MPI over other modalities, including its sensitivity, linearity, and high temporal resolution for 3D volume tracking, enabling not only concentration changes but also the rate of these processes to be monitored. Given the limited iron uptake by the liver and spleen, reducing the administered dose is a crucial aspect to consider for repeated monitoring of an individual patient. MNP spectrometry is a highly valuable tool during the preclinical stage of MPI, as it allows for the assessment and evaluation of the non-linear response and relaxation behavior with high signal quality and low noise in homogeneous fields. Concerning the instrumentation of MNP spectrometry, this work presents an arbitrary waveform magnetic particle spectrometer (MPS) for the purpose of measuring and emulating data, including the development and calibration of the device. This spectrometer is capable of measuring particles in a variety of excitation and offset fields. The data is then processed by a custom software framework, which assembles a realistic dataset that can be used for complex sequence emulation. In particular, the device can be utilized to assess different excitation types, MPI signal encoding, saturation behavior, and the magnetization behavior of nanoparticles on a small scale with short measuring times, typically below one minute.
    Publicationtype: Doctoral Thesis
      63  50
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    Compressed sensing and experiment design in magnetic field based imaging
    (2023)
    Grosser, Mirco
    ;
    Knopp, Tobias
    This work investigates the use of compressed sensing to reduce measurement times in magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). To this end, a flexible yet performant image reconstruction framework is developed. Based on this, an efficient reconstruction scheme for non Cartesian MRI is proposed and a low-rank-based method for the estimation of MPI system matrices is developed. Finally, an experiment design framework is developed to obtain optimized sampling patterns for both MRI and MPI.
    Publicationtype: Doctoral Thesis
      41  121
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    Perfusionsbildgebung anhand positiver und negativer Bolusanwendung in der Magnetpartikelbildgebung
    (2022-07-01)
    Exner, Miriam
    ;
    Knopp, Tobias
    Real-time perfusion imaging is a potential application of magnetic particle imaging. It is used to assess the vascular system and the perfusion of organs and tissues. The intravenous injection of a tracer bolus causes concentration-time curves in a volume of interest. Based on this, various perfusion parameters can be assessed and visualized in perfusion maps, e.g. time to peak, mean transit time, blood volume and blood flow. At certain diseases the consistent monitoring of the perfusion of certain organs is required. Until the reach of a steady state (when the concentration-time curves do not show a profile anymore), the injected particles spread in the patient’s blood. Thus, the injection of another bolus is necessary for every image acquisition. The maximum amount of tracer that may be injected to a patient in a certain time frame is limited. Additionally, the concentration of the tracer in the blood is increased by every bolus. Though, the change of concentration might not be detectable anymore. In this work a possible approach to this problem is considered. Therein, the form of the injection for the acquisition of a new perfusion image is inverted by the use of a water or NaCl bolus (negative bolus) instead of a tracer bolus (positive bolus). On the basis of a phantom of a rat’s heart that is connected to a pump, it is investigated if negative boluses might also be used for perfusion imaging as positive boluses to obtain the same results for the calculation of perfusion parameters. The concentration-time curves of the negative boluses necessarily need to be normalized to the positive ones. For this study, optical experiments were performed by use of ink and water as well as MPI measurements by use of tracer and water for the positive and negative boluses, respectively. Based on these experiments, time-curves were derived and assessed concerning their possibility of normalization. Besides, perfusion maps were calculated and compared quantitatively. As can be seen from the experiments, positive and negative boluses of identic size can be normalized to each other.
    Publicationtype: Master Thesis
      31  44
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    Deep learning for automatic lung disease analysis in chest x-rays
    (2021)
    Baltruschat, Ivo-Matteo
    ;
    Knopp, Tobias
    This work addresses the automatic analysis of chest radiographs using Deep Learning, a rapidly evolving machine learning method. After a detailed evaluation of state-of-the-art CNN architectures on the ChestX-ray14 dataset, it is shown how models can be adapted to incorporate non-image features and improve their performance. Further, to simplify the appearance of CXRs and help CNNs deal with high-dimensional and limited data, as well as to improve their performance, a number of advanced pre-processing methods have been employed. Lastly, through simulations based on queuing theory and Markov processes, the significant clinical impact of smart worklist ordering is shown.
    Publicationtype: Doctoral Thesis
      492  3871
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    IVOCT catheter tracking and targeted drug delivery using magnetic particle imaging and magnetic particle navigation
    (2020)
    Griese, Florian
    ;
    Knopp, Tobias
    The modality Magnetic Particle Imaging (MPI) visualizes the quantitative distribution of magnetic nanoparticles by using static and oscillating magnetic fields. In this work, a submillimeter-accurate localization method is developed to identify fiducial markers more precisely for the registration of MRI and MPI volumes. With this method a IVOCT catheter is also tracked in 4D for bimodal imaging of IVOCT and MPI. Further, the method of Magnetic Particle Imaging and Magnetic Particle Navigation is used to navigate magnetic particles through a bifurcation within a vascular flow. Finally, the novel easy-axis aligned MPI contrast is used to determine the easy-axis orientation of samples.
    Publicationtype: Doctoral Thesis
      353  253
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    Eignung und Unsicherheiten geometrischer und fluiddynamischer Parameter zur Rupturrisikoabschätzung zerebraler Aneurysmen
    (2020)
    Schetelig, Daniel
    ;
    Knopp, Tobias
    Cerebral aneurysms are abnormal bulges of blood vessels in the brain that, if ruptured, can cause severe damage - from motor and cognitive failure to death. The choice of a suitable therapy for a diagnosed aneurysm is currently largely dependent on the size and location of the aneurysm. In contrast, hemodynamic and mechanical properties of the vascular system are not currently used in clinical practice for the risk assessment of aneurysms, but could be of great diagnostic value, especially for the assessment of small aneurysms with regard to the assessment of formation, growth and ultimately rupture. Furthermore, a major problem in understanding aneurysms with regard to the exact mechanisms leading to rupture is that only a few image data sets of individual aneurysm structures exist at different points in time. The different developmental stages of a single aneurysm are therefore difficult to analyze, making it difficult to formulate and verify propositions regarding the factors that determine development and rupture. This problem was the motivation for this work, which is why we worked on the exact calculation of geometric deformation of aneurysms, the reliable calculation of fluid-mechanical parameters and a extrapolated temporal development of aneurysm structures. Typically, only limited ground-truth data sets are available in this field of research. Therefore, the goal for this work was to base the analysis on verifiable datasets as much as possible, which motivated the development of a hardware phantom. The phantom simulates the fluid-mechanical characteristics of an aneurysm and material properties of the vessel wall and serves as a ground truth data set for all studies presented. The flow phantom was measured with different imaging modalities (MRI, CT, DSA), so that medical image data (which are common in this research field) are available, which represent the geometry and temporal geometric deformation. First, these data were used to develop a reliable method for the calculation of volumes and deformation of aneurysm geometries. Although previous studies have tried to quantify aneurysm pulsation based on temporally resolved image data, artifacts of the used imaging modalities have not been addressed, so it is not clear to what extent the measured values correspond to a true deformation. In addition, in previous studies no ground-truth data sets were used to verify the generated results, which leaves the reliability regarding the quantified values open. In order to approach the reliable calculation of fluid mechanical parameters, a fluid mechanical calculation setup was set up using the Ground-Truth data set, which enables a reliable simulation and calculation of fluid mechanical parameters. The previously acquired knowledge of exact phantom geometry, deformation and fluid mechanical boundary conditions (inflow velocity, etc.) was used to verify the calculated results. Based on this calculation setup, patient-specific geometries, which were collected at the University Medical Center Hamburg-Eppendorf (Clinic and Polyclinic for Neuroradiological Diagnostics and Intervention), could be simulated and parameters, which correlated with an increased risk of rupture in previous studies, could be evaluated. This was carried out in the sense of a parameter study with the aim of identifying boundary conditions that could have a falsifying influence on the produced results, which could explain the partial contradiction of currently published studies. In addition, different developmental stages of aneurysms were constructed and the geometric shape was simulated so that the fluid mechanical properties that change over the development process could be analysed.
    Publicationtype: Doctoral Thesis
      307  964