@article{GerdesWieserMuehlbergeretal.2010,
  author    = {Gerdes, Antje B. M. and Wieser, Matthias J. and M{\"u}hlberger, Andreas and Weyers, Peter and Alpers, Georg W. and Plichta, Michael M. and Breuer, Felix and Pauli, Paul},
  title     = {Brain activations to emotional pictures are differentially associated with valence and arousal ratings},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-68153},
  year      = {2010},
  abstract  = {Several studies have investigated the neural responses triggered by emotional pictures, but the specificity of the involved structures such as the amygdala or the ventral striatum is still under debate. Furthermore, only few studies examined the association of stimuli's valence and arousal and the underlying brain responses. Therefore, we investigated brain responses with functional magnetic resonance imaging of 17 healthy participants to pleasant and unpleasant affective pictures and afterwards assessed ratings of valence and arousal. As expected, unpleasant pictures strongly activated the right and left amygdala, the right hippocampus, and the medial occipital lobe, whereas pleasant pictures elicited significant activations in left occipital regions, and in parts of the medial temporal lobe. The direct comparison of unpleasant and pleasant pictures, which were comparable in arousal clearly indicated stronger amygdala activation in response to the unpleasant pictures. Most important, correlational analyses revealed on the one hand that the arousal of unpleasant pictures was significantly associated with activations in the right amygdala and the left caudate body. On the other hand, valence of pleasant pictures was significantly correlated with activations in the right caudate head, extending to the nucleus accumbens (NAcc) and the left dorsolateral prefrontal cortex. These findings support the notion that the amygdala is primarily involved in processing of unpleasant stimuli, particularly to more arousing unpleasant stimuli. Reward-related structures like the caudate and NAcc primarily respond to pleasant stimuli, the stronger the more positive the valence of these stimuli is.},
  subject      = {Psychologie},
  language  = {en}
}
@article{DawoodBreuerStebanietal.2023,
  author    = {Dawood, Peter and Breuer, Felix and Stebani, Jannik and Burd, Paul and Homolya, Istv{\´a}n and Oberberger, Johannes and Jakob, Peter M. and Blaimer, Martin},
  title     = {Iterative training of robust k-space interpolation networks for improved image reconstruction with limited scan specific training samples},
  series = {Magnetic Resonance in Medicine},
  volume    = {89},
  journal   = {Magnetic Resonance in Medicine},
  number    = {2},
  doi       = {10.1002/mrm.29482},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312306},
  pages     = {812 -- 827},
  year      = {2023},
  abstract  = {To evaluate an iterative learning approach for enhanced performance of robust artificial-neural-networks for k-space interpolation (RAKI), when only a limited amount of training data (auto-calibration signals [ACS]) are available for accelerated standard 2D imaging. Methods In a first step, the RAKI model was tailored for the case of limited training data amount. In the iterative learning approach (termed iterative RAKI [iRAKI]), the tailored RAKI model is initially trained using original and augmented ACS obtained from a linear parallel imaging reconstruction. Subsequently, the RAKI convolution filters are refined iteratively using original and augmented ACS extracted from the previous RAKI reconstruction. Evaluation was carried out on 200 retrospectively undersampled in vivo datasets from the fastMRI neuro database with different contrast settings. Results For limited training data (18 and 22 ACS lines for R = 4 and R = 5, respectively), iRAKI outperforms standard RAKI by reducing residual artifacts and yields better noise suppression when compared to standard parallel imaging, underlined by quantitative reconstruction quality metrics. Additionally, iRAKI shows better performance than both GRAPPA and standard RAKI in case of pre-scan calibration with varying contrast between training- and undersampled data. Conclusion RAKI benefits from the iterative learning approach, which preserves the noise suppression feature, but requires less original training data for the accurate reconstruction of standard 2D images thereby improving net acceleration.},
  language  = {en}
}