@phdthesis{GraetzgebDittmann2022,
  author    = {Graetz [geb. Dittmann], Jonas},
  title     = {X-Ray Dark-Field Tensor Tomography : a Hitchhiker's Guide to Tomographic Reconstruction and Talbot Imaging},
  doi       = {10.25972/OPUS-28143},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-281437},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {X-ray dark-field imaging allows to resolve the conflict between the demand for centimeter scaled fields of view and the spatial resolution required for the characterization of fibrous materials structured on the micrometer scale. It draws on the ability of X-ray Talbot interferometers to provide full field images of a sample's ultra small angle scattering properties, bridging a gap of multiple orders of magnitude between the imaging resolution and the contrasted structure scale. The correspondence between shape anisotropy and oriented scattering thereby allows to infer orientations within a sample's microstructure below the imaging resolution. First demonstrations have shown the general feasibility of doing so in a tomographic fashion, based on various heuristic signal models and reconstruction approaches. Here, both a verified model of the signal anisotropy and a reconstruction technique practicable for general imaging geometries and large tensor valued volumes is developed based on in-depth reviews of dark-field imaging and tomographic reconstruction techniques. To this end, a wide interdisciplinary field of imaging and reconstruction methodologies is revisited. To begin with, a novel introduction to the mathematical description of perspective projections provides essential insights into the relations between the tangible real space properties of cone beam imaging geometries and their technically relevant description in terms of homogeneous coordinates and projection matrices. Based on these fundamentals, a novel auto-calibration approach is developed, facilitating the practical determination of perspective imaging geometries with minimal experimental constraints. A corresponding generalized formulation of the widely employed Feldkamp algorithm is given, allowing fast and flexible volume reconstructions from arbitrary tomographic imaging geometries. Iterative reconstruction techniques are likewise introduced for general projection geometries, with a particular focus on the efficient evaluation of the forward problem associated with tomographic imaging. A highly performant 3D generalization of Joseph's classic linearly interpolating ray casting algorithm is developed to this end and compared to typical alternatives. With regard to the anisotropic imaging modality required for tensor tomography, X-ray dark-field contrast is extensively reviewed. Previous literature is brought into a joint context and nomenclature and supplemented by original work completing a consistent picture of the theory of dark-field origination. Key results are explicitly validated by experimental data with a special focus on tomography as well as the properties of anisotropic fibrous scatterers. In order to address the pronounced susceptibility of interferometric images to subtle mechanical imprecisions, an efficient optimization based evaluation strategy for the raw data provided by Talbot interferometers is developed. Finally, the fitness of linear tensor models with respect to the derived anisotropy properties of dark-field contrast is evaluated, and an iterative scheme for the reconstruction of tensor valued volumes from projection images is proposed. The derived methods are efficiently implemented and applied to fiber reinforced plastic samples, imaged at the ID19 imaging beamline of the European Synchrotron Radiation Facility. The results represent unprecedented demonstrations of X-ray dark-field tensor tomography at a field of view of 3-4cm, revealing local fiber orientations of both complex shaped and low-contrast samples at a spatial resolution of 0.1mm in 3D. The results are confirmed by an independent micro CT based fiber analysis.},
  subject      = {Dreidimensionale Rekonstruktion},
  language  = {en}
}
@phdthesis{Ullherr2021,
  author    = {Ullherr, Maximilian},
  title     = {Optimization of Image Quality in High-Resolution X-Ray Imaging},
  doi       = {10.25972/OPUS-23117},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-231171},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2021},
  abstract  = {The SNR spectra model and measurement method developed in this work yield reliable application-specific optima for image quality. This optimization can either be used to understand image quality, find out how to build a good imaging device or to (automatically) optimize the parameters of an existing setup. SNR spectra are here defined as a fraction of power spectra instead of a product of device properties. In combination with the newly developed measurement method for this definition, a close correspondence be- tween theory and measurement is achieved. Prior approaches suffer from a focus on theoretical definitions without fully considering if the defined quantities can be measured correctly. Additionally, discrepancies between assumptions and reality are common. The new approach is more reliable and complete, but also more difficult to evaluate and interpret. The signal power spectrum in the numerator of this fraction allows to model the image quality of different contrast mechanisms that are used in high-resolution x-ray imaging. Superposition equations derived for signal and noise enable understanding how polychromaticity (or superposition in general) affects the image quality. For the concept of detection energy weighting, a quantitative model for how it affects im- age quality was found. It was shown that—depending on sample properties—not detecting x-ray photons can increase image quality. For optimal computational energy weighting, more general formula for the optimal weight was found. In addition to the signal strength, it includes noise and modulation transfer. The novel method for measuring SNR spectra makes it possible to experimentally optimize image quality for different contrast mechanisms. This method uses one simple measurement to obtain a measure for im- age quality for a specific experimental setup. Comparable measurement methods typically require at least three more complex measurements, where the combination may then give a false result. SNR spectra measurements can be used to: • Test theoretical predictions about image quality optima. • Optimize image quality for a specific application. • Find new mechanisms to improve image quality. The last item reveals an important limitation of x- ray imaging in general: The achievable image quality is limited by the amount of x-ray photons interacting with the sample, not by the amount incident per detector area (see section 3.6). If the rest of the imaging geometry is fixed, moving the detector only changes the field of view, not the image quality. A practical consequence is that moving the sample closer to the x-ray source increases image quality quadratically. The results of a SNR spectra measurement represent the image quality only on a relative scale, but very reliable. This relative scale is sufficient for an optimization problem. Physical effects are often already clearly identifiable by the shape of the functional relationship between input parameter and measurement result. SNR spectra as a quantity are not well suited for standardization, but instead allow a reliable optimization. Not satisfying the requirements of standardization allows to use methods which have other advantages. In this case, the SNR spectra method describes the image quality for a specific application. Consequently, additional physical effects can be taken into account. Additionally, the measurement method can be used to automate the setting of optimal machine parameters. The newly proposed image quality measure detection effectiveness is better suited for standardization or setup comparison. This quantity is very similar to measures from other publications (e.g. CNR(u)), when interpreted monochromatically. Polychromatic effects can only be modeled fully by the DE(u). The measurement processes of both are different and the DE(u) is fundamentally more reliable. Information technology and digital data processing make it possible to determine SNR spectra from a mea- sured image series. This measurement process was designed from the ground up to use these technical capabilities. Often, information technology is only used to make processes easier and more exact. Here, the whole measurement method would be infeasible without it. As this example shows, using the capabilities of digital data processing much more extensively opens many new possibilities. Information technology can be used to extract information from measured data in ways that analog data processing simply cannot. The original purpose of the SNR spectra optimization theory and methods was to optimize high resolution x-ray imaging only. During the course of this work, it has become clear that some of the results of this work affect x-ray imaging in general. In the future, these results could be applied to MI and NDT x-ray imaging. Future work on the same topic will also need to consider the relationship between SNR spectra or DE(u) and sufficient image quality.This question is about the minimal image quality required for a specific measurement task.},
  subject      = {Bildqualit{\"a}t},
  language  = {en}
}
@phdthesis{PonceGarcia2018,
  author    = {Ponce Garcia, Irene Paola},
  title     = {Strategies for optimizing dynamic MRI},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-162622},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {In Magnetic Resonance Imaging (MRI), acquisition of dynamic data may be highly complex due to rapid changes occurred in the object to be imaged. For clinical diagnostic, dynamic MR images require both high spatial and temporal resolution. The speed in the acquisition is a crucial factor to capture optimally dynamics of the objects to obtain accurate diagnosis. In the 90's, partially parallel MRI (pMRI) has been introduced to shorten scan times reducing the amount of acquired data. These approaches use multi-receiver coil arrays to acquire independently and simultaneously the data. Reduction in the amount of acquired data results in images with aliasing artifacts. Dedicated methods as such Sensitivity Encoding (SENSE) and Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) were the basis of a series of algorithms in pMRI. Nevertheless, pMRI methods require extra spatial or temporal information in order to optimally reconstruct the data. This information is typically obtained by an extra scan or embedded in the accelerated acquisition applying a variable density acquisition scheme. In this work, we were able to reduce or totally eliminate the acquisition of the training data for kt-SENSE and kt-PCA algorithms obtaining accurate reconstructions with high temporal fidelity. For dynamic data acquired in an interleaved fashion, the temporal average of accelerated data can generate an artifact-free image used to estimate the coil sensitivity maps avoiding the need of extra acquisitions. However, this temporal average contains errors from aliased components, which may lead to signal nulls along the spectra of reconstructions when methods like kt-SENSE are applied. The use of a GRAPPA filter applied to the temporal average reduces these errors and subsequently may reduce the null components in the reconstructed data. In this thesis the effect of using temporal averages from radial data was investigated. Non-periodic artifacts performed by undersampling radial data allow a more accurate estimation of the true temporal average and thereby avoiding undesirable temporal filtering in the reconstructed images. kt-SENSE exploits not only spatial coil sensitivity variations but also makes use of spatio-temporal correlations in order to separate the aliased signals. Spatio-temporal correlations in kt-SENSE are learnt using a training data set, which consists of several central k-space lines acquired in a separate scan. The scan of these extra lines results in longer acquisition times even for low resolution images. It was demonstrate that limited spatial resolution of training data set may lead to temporal filtering effects (or temporal blurring) in the reconstructed data. In this thesis, the auto-calibration for kt-SENSE was proposed and its feasibility was tested in order to completely eliminate the acquisition of training data. The application of a prior TSENSE reconstruction produces the training data set for the kt-SENSE algorithm. These training data have full spatial resolution. Furthermore, it was demonstrated that the proposed auto-calibrating method reduces significantly temporal filtering in the reconstructed images compared to conventional kt-SENSE reconstructions employing low resolution training images. However, the performance of auto-calibrating kt-SENSE is affected by the Signal-to-Noise Ratio (SNR) of the first pass reconstructions that propagates to the final reconstructions. Another dedicated method used in dynamic MRI applications is kt-PCA, that was first proposed for the reconstruction of MR cardiac data. In this thesis, kt-PCA was employed for the generation of spatially resolved M0, T1 and T2 maps from a single accelerated IRTrueFISP or IR-Snapshot FLASH measurement. In contrast to cardiac dynamic data, MR relaxometry experiments exhibit signal at all temporal frequencies, which makes their reconstruction more challenging. However, since relaxometry measurements can be represented by only few parameters, the use of few principal components (PC) in the kt-PCA algorithm can significantly simplify the reconstruction. Furthermore, it was found that due to high redundancy in relaxometry data, PCA can efficiently extract the required information from just a single line of training data. It has been demonstrated in this thesis that auto-calibrating kt-SENSE is able to obtain high temporal fidelity dynamic cardiac reconstructions from moderate accelerated data avoiding the extra acquisition of training data. Additionally, kt-PCA has been proved to be a suitable method for the reconstruction of highly accelerated MR relaxometry data. Furthermore, a single central training line is necessary to obtain accurate reconstructions. Both reconstruction methods are promising for the optimization of training data acquisition and seem to be feasible for several clinical applications.},
  subject      = {Kernspintomografie},
  language  = {en}
}
@phdthesis{TranGia2014,
  author    = {Tran-Gia, Johannes},
  title     = {Model-Based Reconstruction Methods for MR Relaxometry},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-109774},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {In this work, a model-based acceleration of parameter mapping (MAP) for the determination of the tissue parameter T1 using magnetic resonance imaging (MRI) is introduced. The iterative reconstruction uses prior knowledge about the relaxation behavior of the longitudinal magnetization after a suitable magnetization preparation to generate a series of fully sampled k-spaces from a strongly undersampled acquisition. A Fourier transform results in a spatially resolved time course of the longitudinal relaxation process, or equivalently, a spatially resolved map of the longitudinal relaxation time T1. In its fastest implementation, the MAP algorithm enables the reconstruction of a T1 map from a radial gradient echo dataset acquired within only a few seconds after magnetization preparation, while the acquisition time of conventional T1 mapping techniques typically lies in the range of a few minutes. After validation of the MAP algorithm for two different types of magnetization preparation (saturation recovery \& inversion recovery), the developed algorithm was applied in different areas of preclinical and clinical MRI and possible advantages and disadvantages were evaluated.},
  subject      = {Kernspintomographie},
  language  = {en}
}
@phdthesis{Oechsner2011,
  author    = {Oechsner, Markus},
  title     = {Morphologische und funktionelle <sup>1</sup>H-Magnetresonanztomographie der menschlichen Lunge bei 0.2 und 1.5 Tesla},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-66942},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2011},
  abstract  = {Das Ziel dieser Arbeit war es, Methoden und Techniken f{\"u}r die morphologische und funktionelle Bildgebung der menschlichen Lunge mittels Kernspintomographie bei Feldst{\"a}rken von 0,2 Tesla und 1,5 Tesla zu entwickeln und zu optimieren. Bei 0,2 Tesla wurde mittels der gemessenen Relaxationszeiten T1 und T2* eine 2D und eine 3D FLASH Sequenz zur Untersuchung der Lungenmorphologie optimiert. Sauerstoffgest{\"u}tzte Messungen der Relaxationszeiten T1 und T2* sowie eine SpinLabeling Sequenz liefern funktionelle Informationen {\"u}ber den Sauerstofftransfer und die Perfusion der Lungen. Bei 1,5 Tesla wurde die Lungenperfusion mittels MR-Kontrastmittel mit einer 2D und einer 3D Sequenz unter Verwendung der Pr{\"a}bolus Technik quantifiziert. Zudem wurden zwei MR-Navigationstechniken entwickelt, die es erm{\"o}glichen Lungenuntersuchungen unter freier Atmung durchzuf{\"u}hren und aus den Daten artefaktfreie Bilder zu rekonstruieren. Diese Techniken k{\"o}nnen in verschiedenste Sequenzen f{\"u}r die Lungenbildgebung implementiert werden, ohne dass die Messzeit dadurch signifikant verl{\"a}ngert wird.},
  subject      = {NMR-Bildgebung},
  language  = {de}
}
@phdthesis{Seiberlich2008,
  author    = {Seiberlich, Nicole},
  title     = {Advances in Non-Cartesian Parallel Magnetic Resonance Imaging using the GRAPPA Operator},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-28321},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2008},
  abstract  = {Magnetic Resonance Imaging (MRI) is an imaging modality which provides anatomical or functional images of the human body with variable contrasts in an arbitrarily positioned slice without the need for ionizing radiation. In MRI, data are not acquired directly, but in the reciprocal image space (otherwise known as k-space) through the application of spatially variable magnetic field gradients. The k-space is made up of a grid of data points which are generally acquired in a line-by-line fashion (Cartesian imaging). After the acquisition, the k-space data are transformed into the image domain using the Fast Fourier Transformation (FFT). However, the acquisition of data is not limited to the rectilinear Cartesian sampling scheme described above. Non-Cartesian acquisitions, where the data are collected along exotic trajectories, such as radial and spiral, have been shown to be beneficial in a number of applications. However, despite their additional properties and potential advantages, working with non-Cartesian data can be complicated. The primary difficulty is that non-Cartesian trajectories are made up of points which do not fall on a Cartesian grid, and a simple and fast FFT algorithm cannot be employed to reconstruct images from non-Cartesian data. In order to create an image, the non-Cartesian data are generally resampled on a Cartesian grid, an operation known as gridding, before the FFT is performed. Another challenge for non-Cartesian imaging is the combination of unusual trajectories with parallel imaging. This thesis has presented several new non-Cartesian parallel imaging methods which simplify both gridding and the reconstruction of images from undersampled data. In Chapter 4, a novel approach which uses the concepts of parallel imaging to grid data sampled along a non-Cartesian trajectory called GRAPPA Operator Gridding (GROG) is described. GROG shifts any acquired k-space data point to its nearest Cartesian location, thereby converting non-Cartesian to Cartesian data. The only requirements for GROG are a multi-channel acquisition and a calibration dataset for the determination of the GROG weights. Chapter 5 discusses an extension of GRAPPA Operator Gridding, namely Self-Calibrating GRAPPA Operator Gridding (SC-GROG). SC-GROG is a method by which non-Cartesian data can be gridded using spatial information from a multi-channel coil array without the need for an additional calibration dataset, as required in standard GROG. Although GROG can be used to grid undersampled datasets, it is important to note that this method uses parallel imaging only for gridding, and not to reconstruct artifact-free images from undersampled data. Chapter 6 introduces a simple, novel method for performing modified Cartesian GRAPPA reconstructions on undersampled non-Cartesian k-space data gridded using GROG to arrive at a non-aliased image. Because the undersampled non-Cartesian data cannot be reconstructed using a single GRAPPA kernel, several Cartesian patterns are selected for the reconstruction. Finally, Chapter 7 discusses a novel method of using GROG to mimic the bunched phase encoding acquisition (BPE) scheme. In MRI, it is generally assumed that an artifact-free image can be reconstructed only from sampled points which fulfill the Nyquist criterion. However, the BPE reconstruction is based on the Generalized Sampling Theorem of Papoulis, which states that a continuous signal can be reconstructed from sampled points as long as the points are on average sampled at the Nyquist frequency. A novel method of generating the "bunched" data using GRAPPA Operator Gridding (GROG), which shifts datapoints by small distances in k-space using the GRAPPA Operator instead of employing zig-zag shaped gradients, is presented in this chapter. With the conjugate gradient reconstruction method, these additional "bunched" points can then be used to reconstruct an artifact-free image from undersampled data. This method is referred to as GROG-facilitated Bunched Phase Encoding, or GROG-BPE.},
  subject      = {NMR-Tomographie},
  language  = {en}
}
@phdthesis{Pracht2007,
  author    = {Pracht, Eberhard},
  title     = {Entwicklung und Optimierung von Bildgebungssequenzen f{\"u}r die 1H-Magnetresonanztomographie der Lunge},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-26398},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2007},
  abstract  = {No abstract available},
  subject      = {NMR-Tomographie},
  language  = {de}
}