@article{AnkenbrandLohrSchloetelburgetal.2021,
  author    = {Ankenbrand, Markus Johannes and Lohr, David and Schl{\"o}telburg, Wiebke and Reiter, Theresa and Wech, Tobias and Schreiber, Laura Maria},
  title     = {Deep learning-based cardiac cine segmentation: Transfer learning application to 7T ultrahigh-field MRI},
  series = {Magnetic Resonance in Medicine},
  volume    = {86},
  journal   = {Magnetic Resonance in Medicine},
  number    = {4},
  doi       = {10.1002/mrm.28822},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-257604},
  pages     = {2179-2191},
  year      = {2021},
  abstract  = {Purpose Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pathologies is limited. Transfer learning addresses this challenge, but specific recommendations regarding type and amount of data required is lacking. In this study, we assess data requirements for transfer learning to experimental cardiac MRI at 7T where the segmentation task can be challenging. In addition, we provide guidelines, tools, and annotated data to enable transfer learning approaches by other researchers and clinicians. Methods A publicly available segmentation model was used to annotate a publicly available data set. This labeled data set was subsequently used to train a neural network for segmentation of left ventricle and myocardium in cardiac cine MRI. The network is used as starting point for transfer learning to 7T cine data of healthy volunteers (n = 22; 7873 images) by updating the pre-trained weights. Structured and random data subsets of different sizes were used to systematically assess data requirements for successful transfer learning. Results Inconsistencies in the publically available data set were corrected, labels created, and a neural network trained. On 7T cardiac cine images the model pre-trained on public imaging data, acquired at 1.5T and 3T, achieved DICE\(_{LV}\) = 0.835 and DICE\(_{MY}\) = 0.670. Transfer learning using 7T cine data and ImageNet weight initialization improved model performance to DICE\(_{LV}\) = 0.900 and DICE\(_{MY}\) = 0.791. Using only end-systolic and end-diastolic images reduced training data by 90\%, with no negative impact on segmentation performance (DICE\(_{LV}\) = 0.908, DICE\(_{MY}\) = 0.805). Conclusions This work demonstrates and quantifies the benefits of transfer learning for cardiac cine image segmentation. We provide practical guidelines for researchers planning transfer learning projects in cardiac MRI and make data, models, and code publicly available.},
  language  = {en}
}
@article{HockTerekhovStefanescuetal.2021,
  author    = {Hock, Michael and Terekhov, Maxim and Stefanescu, Maria Roxana and Lohr, David and Herz, Stefan and Reiter, Theresa and Ankenbrand, Markus and Kosmala, Aleksander and Gassenmaier, Tobias and Juchem, Christoph and Schreiber, Laura Maria},
  title     = {B\(_{0}\) shimming of the human heart at 7T},
  series = {Magnetic Resonance in Medicine},
  volume    = {85},
  journal   = {Magnetic Resonance in Medicine},
  number    = {1},
  doi       = {10.1002/mrm.28423},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-218096},
  pages     = {182 -- 196},
  year      = {2021},
  abstract  = {Purpose Inhomogeneities of the static magnetic B\(_{0}\) field are a major limiting factor in cardiac MRI at ultrahigh field (≥ 7T), as they result in signal loss and image distortions. Different magnetic susceptibilities of the myocardium and surrounding tissue in combination with cardiac motion lead to strong spatio-temporal B\(_{0}\)-field inhomogeneities, and their homogenization (B0 shimming) is a prerequisite. Limitations of state-of-the-art shimming are described, regional B\(_{0}\) variations are measured, and a methodology for spherical harmonics shimming of the B\(_{0}\) field within the human myocardium is proposed. Methods The spatial B\(_{0}\)-field distribution in the heart was analyzed as well as temporal B\(_{0}\)-field variations in the myocardium over the cardiac cycle. Different shim region-of-interest selections were compared, and hardware limitations of spherical harmonics B\(_{0}\) shimming were evaluated by calibration-based B0-field modeling. The role of third-order spherical harmonics terms was analyzed as well as potential benefits from cardiac phase-specific shimming. Results The strongest B\(_{0}\)-field inhomogeneities were observed in localized spots within the left-ventricular and right-ventricular myocardium and varied between systolic and diastolic cardiac phases. An anatomy-driven shim region-of-interest selection allowed for improved B\(_{0}\)-field homogeneity compared with a standard shim region-of-interest cuboid. Third-order spherical harmonics terms were demonstrated to be beneficial for shimming of these myocardial B\(_{0}\)-field inhomogeneities. Initial results from the in vivo implementation of a potential shim strategy were obtained. Simulated cardiac phase-specific shimming was performed, and a shim term-by-term analysis revealed periodic variations of required currents. Conclusion Challenges in state-of-the-art B\(_{0}\) shimming of the human heart at 7 T were described. Cardiac phase-specific shimming strategies were found to be superior to vendor-supplied shimming.},
  language  = {en}
}