@article{HerzStefanescuLohretal.2022,
  author    = {Herz, Stefan and Stefanescu, Maria R. and Lohr, David and Vogel, Patrick and Kosmala, Aleksander and Terekhov, Maxim and Weng, Andreas M. and Grunz, Jan-Peter and Bley, Thorsten A. and Schreiber, Laura M.},
  title     = {Effects of image homogeneity on stenosis visualization at 7 T in a coronary artery phantom study: With and without B1-shimming and parallel transmission},
  series = {PloS One},
  volume    = {17},
  journal   = {PloS One},
  number    = {6},
  doi       = {10.1371/journal.pone.0270689},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300129},
  year      = {2022},
  abstract  = {Background To investigate the effects of B\(_1\)-shimming and radiofrequency (RF) parallel transmission (pTX) on the visualization and quantification of the degree of stenosis in a coronary artery phantom using 7 Tesla (7 T) magnetic resonance imaging (MRI). Methods Stenosis phantoms with different grades of stenosis (0\%, 20\%, 40\%, 60\%, 80\%, and 100\%; 5 mm inner vessel diameter) were produced using 3D printing (clear resin). Phantoms were imaged with four different concentrations of diluted Gd-DOTA representing established arterial concentrations after intravenous injection in humans. Samples were centrally positioned in a thorax phantom of 30 cm diameter filled with a custom-made liquid featuring dielectric properties of muscle tissue. MRI was performed on a 7 T whole-body system. 2D-gradient-echo sequences were acquired with an 8-channel transmit 16-channel receive (8 Tx / 16 Rx) cardiac array prototype coil with and without pTX mode. Measurements were compared to those obtained with identical scan parameters using a commercially available 1 Tx / 16 Rx single transmit coil (sTX). To assess reproducibility, measurements (n = 15) were repeated at different horizontal angles with respect to the B0-field. Results B\(_1\)-shimming and pTX markedly improved flip angle homogeneity across the thorax phantom yielding a distinctly increased signal-to-noise ratio (SNR) averaged over a whole slice relative to non-manipulated RF fields. Images without B\(_1\)-shimming showed shading artifacts due to local B\(_1\)\(^+\)-field inhomogeneities, which hampered stenosis quantification in severe cases. In contrast, B\(_1\)-shimming and pTX provided superior image homogeneity. Compared with a conventional sTX coil higher grade stenoses (60\% and 80\%) were graded significantly (p<0.01) more precise. Mild to moderate grade stenoses did not show significant differences. Overall, SNR was distinctly higher with B\(_1\)-shimming and pTX than with the conventional sTX coil (inside the stenosis phantoms 14\%, outside the phantoms 32\%). Both full and half concentration (10.2 mM and 5.1 mM) of a conventional Gd-DOTA dose for humans were equally suitable for stenosis evaluation in this phantom study. Conclusions B\(_1\)-shimming and pTX at 7 T can distinctly improve image homogeneity and therefore provide considerably more accurate MR image analysis, which is beneficial for imaging of small vessel structures.},
  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}
}