@article{OPUS4-12785,
  title     = {Search for anomaly-mediated supersymmetry breaking with the ATLAS detector based on a disappearing-track signature in pp collisions at √s=7 TeV},
  series = {The European Physical Journal C},
  volume    = {72},
  journal   = {The European Physical Journal C},
  number    = {1993},
  organization    = {ATLAS Collaboration},
  doi       = {10.1140/epjc/s10052-012-1993-2},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-127850},
  year      = {2012},
  abstract  = {In models of anomaly-mediated supersymmetry breaking (AMSB), the lightest chargino is predicted to have a lifetime long enough to be detected in collider experiments. This letter explores AMSB scenarios in pp collisions at √s=7 TeV by attempting to identify decaying charginos which result in tracks that appear to have few associated hits in the outer region of the tracking system. The search was based on data corresponding to an integrated luminosity of 1.02 fb\(^{-1}\) collected with the ATLAS detector in 2011. The p\(_T\) spectrum of candidate tracks is found to be consistent with the expectation from Standard Model background processes and constraints on the lifetime and the production cross section were obtained. In the minimal AMSB framework with m\(_{3/2}\)<32 TeV, m\(_0\)<1.5 TeV, tanβ=5 and μ>0, a chargino having mass below 92 GeV and a lifetime between 0.5 ns and 2 ns is excluded at 95 \% confidence level.},
  language  = {en}
}
@article{LeubeGustafssonLassmannetal.2022,
  author    = {Leube, Julian and Gustafsson, Johan and Lassmann, Michael and Salas-Ramirez, Maikol and Tran-Gia, Johannes},
  title     = {Analysis of a deep learning-based method for generation of SPECT projections based on a large Monte Carlo simulated dataset},
  series = {EJNMMI Physics},
  volume    = {9},
  journal   = {EJNMMI Physics},
  issn      = {2197-7364},
  doi       = {10.1186/s40658-022-00476-w},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-300697},
  year      = {2022},
  abstract  = {Background In recent years, a lot of effort has been put in the enhancement of medical imaging using artificial intelligence. However, limited patient data in combination with the unavailability of a ground truth often pose a challenge to a systematic validation of such methodologies. The goal of this work was to investigate a recently proposed method for an artificial intelligence-based generation of synthetic SPECT projections, for acceleration of the image acquisition process based on a large dataset of realistic SPECT simulations. Methods A database of 10,000 SPECT projection datasets of heterogeneous activity distributions of randomly placed random shapes was simulated for a clinical SPECT/CT system using the SIMIND Monte Carlo program. Synthetic projections at fixed angular increments from a set of input projections at evenly distributed angles were generated by different u-shaped convolutional neural networks (u-nets). These u-nets differed in noise realization used for the training data, number of input projections, projection angle increment, and number of training/validation datasets. Synthetic projections were generated for 500 test projection datasets for each u-net, and a quantitative analysis was performed using statistical hypothesis tests based on structural similarity index measure and normalized root-mean-squared error. Additional simulations with varying detector orbits were performed on a subset of the dataset to study the effect of the detector orbit on the performance of the methodology. For verification of the results, the u-nets were applied to Jaszczak and NEMA physical phantom data obtained on a clinical SPECT/CT system. Results No statistically significant differences were observed between u-nets trained with different noise realizations. In contrast, a statistically significant deterioration was found for training with a small subset (400 datasets) of the 10,000 simulated projection datasets in comparison with using a large subset (9500 datasets) for training. A good agreement between synthetic (i.e., u-net generated) and simulated projections before adding noise demonstrates a denoising effect. Finally, the physical phantom measurements show that our findings also apply for projections measured on a clinical SPECT/CT system. Conclusion Our study shows the large potential of u-nets for accelerating SPECT/CT imaging. In addition, our analysis numerically reveals a denoising effect when generating synthetic projections with a u-net. Clinically interesting, the methodology has proven robust against camera orbit deviations in a clinically realistic range. Lastly, we found that a small number of training samples (e.g., ~ 400 datasets) may not be sufficient for reliable generalization of the u-net.},
  language  = {en}
}