@phdthesis{Reinhard2023,
  author    = {Reinhard, Sebastian},
  title     = {Improving Super-Resolution Microscopy Data Reconstruction and Evaluation by Developing Advanced Processing Algorithms and Artifcial Neuronal Networks},
  doi       = {10.25972/OPUS-31695},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-316959},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {The fusion of methods from several disciplines is a crucial component of scientific development. Artificial Neural Networks, based on the principle of biological neuronal networks, demonstrate how nature provides the best templates for technological advancement. These innovations can then be employed to solve the remaining mysteries of biology, including, in particular, processes that take place on microscopic scales and can only be studied with sophisticated techniques. For instance, direct Stochastic Optical Reconstruction Microscopy combines tools from chemistry, physics, and computer science to visualize biological processes at the molecular level. One of the key components is the computer-aided reconstruction of super-resolved images. Improving the corresponding algorithms increases the quality of the generated data, providing further insights into our biology. It is important, however, to ensure that the heavily processed images are still a reflection of reality and do not originate in random artefacts. Expansion microscopy is expanding the sample by embedding it in a swellable hydrogel. The method can be combined with other super-resolution techniques to gain additional resolution. We tested this approach on microtubules, a well-known filamentous reference structure, to evaluate the performance of different protocols and labelling techniques. We developed LineProfiler an objective tool for data collection. Instead of collecting perpendicular profiles in small areas, the software gathers line profiles from filamentous structures of the entire image. This improves data quantity, quality and prevents a biased choice of the evaluated regions. On the basis of the collected data, we deployed theoretical models of the expected intensity distribution across the filaments. This led to the conclusion that post-expansion labelling significantly reduces the labelling error and thus, improves the data quality. The software was further used to determine the expansion factor and arrangement of synaptonemal complex data. Automated Simple Elastix uses state-of-the-art image alignment to compare pre- and post-expansion images. It corrects linear distortions occurring under isotropic expansion, calculates a structural expansion factor and highlights structural mismatches in a distortion map. We used the software to evaluate expanded fungi and NK cells. We found that the expansion factor differs for the two structures and is lower than the overall expansion of the hydrogel. Assessing the fluorescence lifetime of emitters used for direct Stochastic Optical Reconstruction Microscopy can reveal additional information about the molecular environment or distinguish dyes emitting with a similar wavelength. The corresponding measurements require a confocal scanning of the sample in combination with the fluorescent switching of the underlying emitters. This leads to non-linear, interrupted Point Spread Functions. The software ReCSAI targets this problem by combining the classical algorithm of compressed sensing with modern methods of artificial intelligence. We evaluated several different approaches to combine these components and found, that unrolling compressed sensing into the network architecture yields the best performance in terms of reconstruction speed and accuracy. In addition to a deep insight into the functioning and learning of artificial intelligence in combination with classical algorithms, we were able to reconstruct the described non-linearities with significantly improved resolution, in comparison to other state-of-the-art architectures.},
  subject      = {Mikroskopie},
  language  = {en}
}
@phdthesis{Schindele2016,
  author    = {Schindele, Andreas},
  title     = {Proximal methods in medical image reconstruction and in nonsmooth optimal control of partial differential equations},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-136569},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {Proximal methods are iterative optimization techniques for functionals, J = J1 + J2, consisting of a differentiable part J2 and a possibly nondifferentiable part J1. In this thesis proximal methods for finite- and infinite-dimensional optimization problems are discussed. In finite dimensions, they solve l1- and TV-minimization problems that are effectively applied to image reconstruction in magnetic resonance imaging (MRI). Convergence of these methods in this setting is proved. The proposed proximal scheme is compared to a split proximal scheme and it achieves a better signal-to-noise ratio. In addition, an application that uses parallel imaging is presented. In infinite dimensions, these methods are discussed to solve nonsmooth linear and bilinear elliptic and parabolic optimal control problems. In particular, fast convergence of these methods is proved. Furthermore, for benchmarking purposes, truncated proximal schemes are compared to an inexact semismooth Newton method. Results of numerical experiments are presented to demonstrate the computational effectiveness of our proximal schemes that need less computation time than the semismooth Newton method in most cases. Results of numerical experiments are presented that successfully validate the theoretical estimates.},
  subject      = {Optimale Kontrolle},
  language  = {en}
}
@phdthesis{Neumann2014,
  author    = {Neumann, Daniel},
  title     = {Advances in Fast MRI Experiments},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-108165},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging technique, that is rou- tinely used in clinical practice for detection and diagnosis of a wide range of different diseases. In MRI, no ionizing radiation is used, making even repeated application unproblematic. This is an important advantage over other common imaging methods such as X-rays and Computer To- mography. One major drawback of MRI, however, are long acquisition times and associated high costs of experiments. Since the introduction of MRI, several important technical developments have been made to successfully reduce acquisition times. In this work, novel approaches were developed to increase the efficiency of MRI acquisitions. In Chapter 4, an improved radial turbo spin-echo (TSE) combined acquisition and reconstruction strategy was introduced. Cartesian turbo spin-echo sequences [3] are widely used especially for the detection and diagnosis of neurological pathologies, as they provide high SNR images with both clinically important proton density and T2 contrasts. TSE acquisitions combined with radial sampling are very efficient, since it is possible to obtain a number of ETL images with different contrasts from a single radial TSE measurement [56-58]. Conventionally, images with a particular contrast are obtained from both radial and Cartesian TSE acquisitions by combining data from different echo times into a single image. In the radial case, this can be achieved by employing k-space weighted image contrast (KWIC) reconstruction. In KWIC, the center region of k-space is filled exclusively with data belonging to the desired contrast while outer regions also are assembled with data acquired at other echo times. However, this data sharing leads to mixed contrast contributions to both Cartesian and radial TSE images. This is true especially for proton density weighted images and therefore may reduce their diagnostic value. In the proposed method, an adapted golden angle reordering scheme is introduced for radial TSE acquisitions, that allows a free choice of the echo train length and provides high flexibility in image reconstruction. Unwanted contrast contaminations are greatly reduced by employing a narrow-band KWIC filter, that restricts data sharing to a small temporal window around the de- sired echo time. This corresponds to using fewer data than required for fully sampled images and consequently leads to images exhibiting aliasing artifacts. In a second step, aliasing-free images are obtained using parallel imaging. In the neurological examples presented, the CG-SENSE algorithm [42] was chosen due to its stable convergence properties and its ability to reconstruct arbitrarily sampled data. In simulations as well as in different in vivo neurological applications, no unwanted contrast contributions could be observed in radial TSE images reconstructed with the proposed method. Since this novel approach is easy to implement on today's scanners and requires low computational power, it might be valuable for the clinical breakthrough of radial TSE acquisitions. In Chapter 5, an auto-calibrating method was introduced to correct for stimulated echo contribu- tions to T2 estimates from a mono-exponential fit of multi spin-echo (MSE) data. Quantification of T2 is a useful tool in clinical routine for the detection and diagnosis of diseases as well as for tis- sue characterization. Due to technical imperfections, refocusing flip angles in a MSE acquisition deviate from the ideal value of 180○. This gives rise to significant stimulated echo contributions to the overall signal evolution. Therefore, T2 estimates obtained from MSE acquisitions typically are notably higher than the reference. To obtain accurate T2 estimates from MSE acquisitions, MSE signal amplitudes can be predicted using the extended phase graph (EPG, [23, 24]) algo- rithm. Subsequently, a correction factor can be obtained from the simulated EPG T2 value and applied to the MSE T2 estimates. However, EPG calculations require knowledge about refocus- ing pulse amplitudes, T2 and T1 values and the temporal spacing of subsequent echoes. While the echo spacing is known and, as shown in simulations, an approximate T1 value can be assumed for high ratios of T1/T2 without compromising accuracy of the results, the remaining two parameters are estimated from the data themselves. An estimate for the refocusing flip angle can be obtained from the signal intensity ratio of the second to the first echo using EPG. A conventional mono- exponential fit of the MSE data yields a first estimate for T2. The T2 correction is then obtained iteratively by updating the T2 value used for EPG calculations in each step. For all examples pre- sented, two iterations proved to be sufficient for convergence. In the proposed method, a mean flip angle is extracted across the slice. As shown in simulations, this assumption leads to greatly reduced deviations even for more inhomogeneous slice profiles. The accuracy of corrected T2 values was shown in experiments using a phantom consisting of bottles filled with liquids with a wide range of different T2 values. While T2 MSE estimates were shown to deviate significantly from the spin-echo reference values, this is not the case for corrected T2 values. Furthermore, applicability was demonstrated for in vivo neurological experiments. In Chapter 6, a new auto-calibrating parallel imaging method called iterative GROG was pre- sented for the reconstruction of non-Cartesian data. A wide range of different non-Cartesian schemes have been proposed for data acquisition in MRI, that present various advantages over conventional Cartesian sampling such as faster acquisitions, improved dynamic imaging and in- trinsic motion correction. However, one drawback of non-Cartesian data is the more complicated reconstruction, which is ever more problematic for non-Cartesian parallel imaging techniques. Iterative GROG uses Calibrationless Parallel Imaging by Structured Low-Rank Matrix Completion (CPI) for data reconstruction. Since CPI requires points on a Cartesian grid, it cannot be used to directly reconstruct non-Cartesian data. Instead, Grappa Operator Gridding (GROG) is employed in a first step to move the non-Cartesian points to the nearest Cartesian grid locations. However, GROG requires a fully sampled center region of k-space for calibration. Combining both methods in an iterative scheme, accurate GROG weights can be obtained even from highly undersampled non-Cartesian data. Subsequently, CPI can be used to reconstruct either full k- space or a calibration area of arbitrary size, which can then be employed for data reconstruction with conventional parallel imaging methods. In Chapter 7, a new 2D sampling scheme was introduced consisting of multiple oscillating effi- cient trajectories (MOET), that is optimized for Compressed Sensing (CS) reconstructions. For successful CS reconstruction of a particular data set, some requirements have to be met. First, ev- ery data sample has to carry information about the whole object, which is automatically fulfilled for the Fourier sampling employed in MRI. Additionally, the image to be reconstructed has to be sparse in an arbitrary domain, which is true for a number of different applications. Last, data sam- pling has to be performed in an incoherent fashion. For 2D imaging, this important requirement of CS is difficult to achieve with conventional Cartesian and non-Cartesian sampling schemes. Ra- dial sampling is often used for CS reconstructions of dynamic data despite the streaking present in undersampled images. To obtain incoherent aliasing artifacts in undersampled images while at the same time preserving the advantages of radial sampling for dynamic imaging, MOET com- bines radial spokes with oscillating gradients of varying amplitude and alternating orientation orthogonal to the readout direction. The advantage of MOET over radial sampling in CS re- constructions was demonstrated in simulations and in in vivo cardiac imaging. MOET provides superior results especially when used in CS reconstructions with a sparsity constraint directly in image space. Here, accurate results could be obtained even from few MOET projections, while the coherent streaking artifacts present in the case of radial sampling prevent image recovery even for smaller acceleration factors. For CS reconstructions of dynamic data with sparsity constraint in xf-space, the advantage of MOET is smaller since the temporal reordering is responsible for an important part of incoherency. However, as was shown in simulations of a moving phantom and in the reconstruction of ungated cardiac data, the additional spatial incoherency provided by MOET still leads to improved results with higher accuracy and may allow reconstructions with higher acceleration factors.},
  subject      = {Kernspintomografie},
  language  = {en}
}
@phdthesis{Fischer2011,
  author    = {Fischer, Andr{\´e}},
  title     = {On the Application of Compressed Sensing to Magnetic Resonance Imaging},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-72496},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2011},
  abstract  = {This thesis investigated the potential of Compressed Sensing (CS) applied to Magnetic Resonance Imaging (MRI). CS is a novel image reconstruction method that emerged from the field of information theory. The framework of CS was first published in technical reports in 2004 by Cand{\`e}s and Donoho. Two years later, the theory of CS was published in a conference abstract and two papers. Cand{\`e}s and Donoho proved that it is possible, with overwhelming probability, to reconstruct a noise-free sparse signal from incomplete frequency samples (e.g., Fourier coefficients). Hereby, it is assumed a priori that the desired signal for reconstruction is sparse. A signal is considered "sparse" when the number of non-zero elements is significantly smaller than the number of all elements. Sparsity is the most important foundation of CS. When an ideal noise-free signal with few non-zero elements is given, it should be understandably possible to obtain the relevant information from fewer Fourier coefficients than dictated by the Nyquist-Shannon criterion. The theory of CS is based on noise-free sparse signals. As soon as noise is introduced, no exact sparsity can be specified since all elements have signal intensities that are non-zero. However, with the addition of little or moderate noise, an approximate sparsity that can be exploited using the CS framework will still be given. The ability to reconstruct noisy undersampled sparse MRI data using CS has been extensively demonstrated. Although most MR datasets are not sparse in image space, they can be efficiently sparsified by a sparsifying transform. In this thesis, the data are either sparse in the image domain, after Discrete Gradient transformation, or after subtraction of a temporally averaged dataset from the data to be reconstructed (dynamic imaging). The aim of this thesis was to identify possible applications of CS to MRI. Two different algorithms were considered for reconstructing the undersampled sparse data with the CS concept. The Nonlinear Conjugate Gradient based technique with a relaxed data consistency constraint as suggested by Lustig et al. is termed Relaxed DC method. An alternative represents the Gradient or Steepest Descent algorithm with strict data consistency and is, therefore, termed the Strict DC method. Chapter 3 presents simulations illustrating which of these two reconstruction algorithms is best suited to recover undersampled sparse MR datasets. The results lead to the decision for the Strict DC method as reconstruction technique in this thesis. After these simulations, different applications and extensions of CS are demonstrated. Chapter 4 shows how CS benefits spectroscopic 19F imaging at 7 T, allowing a significant reduction of measurement times during in vivo experiments. Furthermore, it allows highly resolved spectroscopic 3D imaging in acceptable measurement times for in vivo applications. Chapter 5 introduces an extension of the Strict DC method called CS-CC (CS on Combined Coils), which allows efficient processing of sparse undersampled multi-coil data. It takes advantage of a concept named "Joint Sparsity", which exploits the fact that all channels of a coil array detect the same sparse object weighted with the coil sensitivity profiles. The practical use of this new algorithm is demonstrated in dynamic radial cardiac imaging. Accurate reconstructions of cardiac motion in free breathing without ECG triggering were obtained for high undersampling factors. An Iterative GRAPPA algorithm is introduced in Chapter 6 that can recover undersampled data from arbitrary (Non-Cartesian) trajectories and works solely in the Cartesian plane. This characteristic makes the proposed Iterative GRAPPA computationally more efficient than SPIRiT. Iterative GRAPPA was developed in a preceding step to combine parallel imaging with CS. Optimal parameters for Iterative GRAPPA (e.g. number of iterations, GRAPPA kernel size) were determined in phantom experiments and verified by retrospectively undersampling and reconstructing a radial cardiac cine dataset. The synergistic combination of the coil-by-coil Strict DC CS method and Iterative GRAPPA called CS-GRAPPA is presented in Chapter 7. CS-GRAPPA allows accurate reconstruction of undersampled data from even higher acceleration factors than each individual method. It is a formulation equivalent to L1-SPIRiT but computationally more efficient. Additionally, a comparison with CS-CC is given. Interestingly, exploiting joint sparsity in CS-CC is slightly more efficient than the proposed CS-GRAPPA, a hybrid of parallel imaging and CS. The last chapter of this thesis concludes the findings presented in this dissertation. Future applications expected to benefit from CS are discussed and possible synergistic combinations with other existing MR methodologies for accelerated imaging are also contemplated.},
  subject      = {NMR-Tomographie},
  language  = {en}
}