@unpublished{HeidenreichGassenmaierAnkenbrandetal.2021,
  author    = {Heidenreich, Julius F. and Gassenmaier, Tobias and Ankenbrand, Markus J. and Bley, Thorsten A. and Wech, Tobias},
  title     = {Self-configuring nnU-net pipeline enables fully automatic infarct segmentation in late enhancement MRI after myocardial infarction},
  edition   = {accepted version},
  doi       = {10.1016/j.ejrad.2021.109817},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-323418},
  year      = {2021},
  abstract  = {Purpose To fully automatically derive quantitative parameters from late gadolinium enhancement (LGE) cardiac MR (CMR) in patients with myocardial infarction and to investigate if phase sensitive or magnitude reconstructions or a combination of both results in best segmentation accuracy. Methods In this retrospective single center study, a convolutional neural network with a U-Net architecture with a self-configuring framework ("nnU-net") was trained for segmentation of left ventricular myocardium and infarct zone in LGE-CMR. A database of 170 examinations from 78 patients with history of myocardial infarction was assembled. Separate fitting of the model was performed, using phase sensitive inversion recovery, the magnitude reconstruction or both contrasts as input channels. Manual labelling served as ground truth. In a subset of 10 patients, the performance of the trained models was evaluated and quantitatively compared by determination of the S{\o}rensen-Dice similarity coefficient (DSC) and volumes of the infarct zone compared with the manual ground truth using Pearson's r correlation and Bland-Altman analysis. Results The model achieved high similarity coefficients for myocardium and scar tissue. No significant difference was observed between using PSIR, magnitude reconstruction or both contrasts as input (PSIR and MAG; mean DSC: 0.83 ± 0.03 for myocardium and 0.72 ± 0.08 for scars). A strong correlation for volumes of infarct zone was observed between manual and model-based approach (r = 0.96), with a significant underestimation of the volumes obtained from the neural network. Conclusion The self-configuring nnU-net achieves predictions with strong agreement compared to manual segmentation, proving the potential as a promising tool to provide fully automatic quantitative evaluation of LGE-CMR.},
  language  = {en}
}
@article{WinterAndelovicKampfetal.2021,
  author    = {Winter, Patrick M. and Andelovic, Kristina and Kampf, Thomas and Hansmann, Jan and Jakob, Peter Michael and Bauer, Wolfgang Rudolf and Zernecke, Alma and Herold, Volker},
  title     = {Simultaneous measurements of 3D wall shear stress and pulse wave velocity in the murine aortic arch},
  series = {Journal of Cardiovascular Magnetic Resonance},
  volume    = {23},
  journal   = {Journal of Cardiovascular Magnetic Resonance},
  number    = {1},
  doi       = {10.1186/s12968-021-00725-4},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-259152},
  pages     = {34},
  year      = {2021},
  abstract  = {Purpose Wall shear stress (WSS) and pulse wave velocity (PWV) are important parameters to characterize blood flow in the vessel wall. Their quantification with flow-sensitive phase-contrast (PC) cardiovascular magnetic resonance (CMR), however, is time-consuming. Furthermore, the measurement of WSS requires high spatial resolution, whereas high temporal resolution is necessary for PWV measurements. For these reasons, PWV and WSS are challenging to measure in one CMR session, making it difficult to directly compare these parameters. By using a retrospective approach with a flexible reconstruction framework, we here aimed to simultaneously assess both PWV and WSS in the murine aortic arch from the same 4D flow measurement. Methods Flow was measured in the aortic arch of 18-week-old wildtype (n = 5) and ApoE\(^{-/-}\) mice (n = 5) with a self-navigated radial 4D-PC-CMR sequence. Retrospective data analysis was used to reconstruct the same dataset either at low spatial and high temporal resolution (PWV analysis) or high spatial and low temporal resolution (WSS analysis). To assess WSS, the aortic lumen was labeled by semi-automatically segmenting the reconstruction with high spatial resolution. WSS was determined from the spatial velocity gradients at the lumen surface. For calculation of the PWV, segmentation data was interpolated along the temporal dimension. Subsequently, PWV was quantified from the through-plane flow data using the multiple-points transit-time method. Reconstructions with varying frame rates and spatial resolutions were performed to investigate the influence of spatiotemporal resolution on the PWV and WSS quantification. Results 4D flow measurements were conducted in an acquisition time of only 35 min. Increased peak flow and peak WSS values and lower errors in PWV estimation were observed in the reconstructions with high temporal resolution. Aortic PWV was significantly increased in ApoE\(^{-/-}\) mice compared to the control group (1.7 ± 0.2 versus 2.6 ± 0.2 m/s, p < 0.001). Mean WSS magnitude values averaged over the aortic arch were (1.17 ± 0.07) N/m\(^2\) in wildtype mice and (1.27 ± 0.10) N/m\(^2\) in ApoE\(^{-/-}\) mice. Conclusion The post processing algorithm using the flexible reconstruction framework developed in this study permitted quantification of global PWV and 3D-WSS in a single acquisition. The possibility to assess both parameters in only 35 min will markedly improve the analyses and information content of in vivo measurements.},
  language  = {en}
}