@article{RichterWechWengetal.2020,
  author    = {Richter, Julian A. J. and Wech, Tobias and Weng, Andreas M. and Stich, Manuel and Weick, Stefan and Breuer, Kathrin and Bley, Thorsten A. and K{\"o}stler, Herbert},
  title     = {Free-breathing self-gated 4D lung MRI using wave-CAIPI},
  series = {Magnetic Resonance in Medicine},
  volume    = {84},
  journal   = {Magnetic Resonance in Medicine},
  number    = {6},
  doi       = {10.1002/mrm.28383},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-218075},
  pages     = {3223 -- 3233},
  year      = {2020},
  abstract  = {Purpose The aim of this study was to compare the wave-CAIPI (controlled aliasing in parallel imaging) trajectory to the Cartesian sampling for accelerated free-breathing 4D lung MRI. Methods The wave-CAIPI k-space trajectory was implemented in a respiratory self-gated 3D spoiled gradient echo pulse sequence. Trajectory correction applying the gradient system transfer function was used, and images were reconstructed using an iterative conjugate gradient SENSE (CG SENSE) algorithm. Five healthy volunteers and one patient with squamous cell carcinoma in the lung were examined on a clinical 3T scanner, using both sampling schemes. For quantitative comparison of wave-CAIPI and standard Cartesian imaging, the normalized mutual information and the RMS error between retrospectively accelerated acquisitions and their respective references were calculated. The SNR ratios were investigated in a phantom study. Results The obtained normalized mutual information values indicate a lower information loss due to acceleration for the wave-CAIPI approach. Average normalized mutual information values of the wave-CAIPI acquisitions were 10\% higher, compared with Cartesian sampling. Furthermore, the RMS error of the wave-CAIPI technique was lower by 19\% and the SNR was higher by 14\%. Especially for short acquisition times (down to 1 minute), the undersampled Cartesian images showed an increased artifact level, compared with wave-CAIPI. Conclusion The application of the wave-CAIPI technique to 4D lung MRI reduces undersampling artifacts, in comparison to a Cartesian acquisition of the same scan time. The benefit of wave-CAIPI sampling can therefore be traded for shorter examinations, or enhancing image quality of undersampled 4D lung acquisitions, keeping the scan time constant.},
  language  = {en}
}
@article{WengHeidenreichMetzetal.2021,
  author    = {Weng, Andreas M. and Heidenreich, Julius F. and Metz, Corona and Veldhoen, Simon and Bley, Thorsten A. and Wech, Tobias},
  title     = {Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times},
  series = {BMC Medical Imaging},
  volume    = {21},
  journal   = {BMC Medical Imaging},
  doi       = {10.1186/s12880-021-00608-1},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-260520},
  year      = {2021},
  abstract  = {Background Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters. Methods In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the S{\o}rensen-Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson's correlation coefficient and Bland Altman analysis. To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer. Results The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson's correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L. Conclusions Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.},
  language  = {en}
}