@phdthesis{Lohr2021,
  author    = {Lohr, David},
  title     = {Functional and Structural Characterization of the Myocardium},
  doi       = {10.25972/OPUS-23448},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-234486},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2021},
  abstract  = {Clinical practice in CMR with respect to cardiovascular disease is currently focused on tissue characterization, and cardiac function, in particular. In recent years MRI based diffusion tensor imaging (DTI) has been shown to enable the assessment of microstructure based on the analysis of Brownian motion of water molecules in anisotropic tissue, such as the myocardium. With respect to both functional and structural imaging, 7T MRI may increase SNR, providing access to information beyond the reach of clinically applied field strengths. To date, cardiac 7T MRI is still a research modality that is only starting to develop towards clinical application. In this thesis we primarily aimed to advance methods of ultrahigh field CMR using the latest 7T technology and its application towards the functional and structural characterization of the myocardium. Regarding the assessment of myocardial microstructure at 7T, feasibility of ex vivo DTI of large animal hearts was demonstrated. In such hearts a custom sequence implemented for in vivo DTI was evaluated and fixation induced alterations of derived diffusion metrics and tissue properties were assessed. Results enable comparison of prior and future ex vivo DTI studies and provide information on measurement parameters at 7T. Translating developed methodology to preclinical studies of mouse hearts, ex vivo DTI provided highly sensitive surrogates for microstructural remodeling in response to subendocardial damage. In such cases echocardiography measurements revealed mild diastolic dysfunction and impaired longitudinal deformation, linking disease induced structural and functional alterations. Complementary DTI and echocardiography data also improved our understanding of structure-function interactions in cases of loss of contractile myofiber tracts, replacement fibrosis, and LV systolic failure. Regarding the functional characterization of the myocardium at 7T, sequence protocols were expanded towards a dedicated 7T routine protocol, encompassing accurate cardiac planning and the assessment of cardiac function via cine imaging in humans. This assessment requires segmentation of myocardial contours. For that, artificial intelligence (AI) was developed and trained, enabling rapid automatic generation of cardiac segmentation in clinical data. Using transfer learning, AI models were adapted to cine data acquired using the latest generation 7T system. Methodology for AI based segmentation was translated to cardiac pathology, where automatic segmentation of scar tissue, edema and healthy myocardium was achieved. Developed radiofrequency hardware facilitates translational studies at 7T, providing controlled conditions for future method development towards cardiac 7T MRI in humans. In this thesis the latest 7T technology, cardiac DTI, and AI were used to advance methods of ultrahigh field CMR. In the long run, obtained results contribute to diagnostic methods that may facilitate early detection and risk stratification in cardiovascular disease.},
  subject      = {Diffusionsgewichtete Magnetresonanztomografie},
  language  = {en}
}
@article{HalderVarkutiBogdanetal.2013,
  author    = {Halder, Sebastian and Varkuti, Balint and Bogdan, Martin and K{\"u}bler, Andrea and Rosenstiel, Wolfgang and Sitaram, Ranganatha and Birbaumer, Niels},
  title     = {Prediction of brain-computer interface aptitude from individual brain structure},
  series = {Frontiers in Human Neuroscience},
  journal   = {Frontiers in Human Neuroscience},
  doi       = {10.3389/fnhum.2013.00105},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-96558},
  year      = {2013},
  abstract  = {Objective: Brain-computer interface (BCI) provide a non-muscular communication channel for patients with impairments of the motor system. A significant number of BCI users is unable to obtain voluntary control of a BCI-system in proper time. This makes methods that can be used to determine the aptitude of a user necessary. Methods: We hypothesized that integrity and connectivity of involved white matter connections may serve as a predictor of individual BCI-performance. Therefore, we analyzed structural data from anatomical scans and DTI of motor imagery BCI-users differentiated into high and low BCI-aptitude groups based on their overall performance. Results: Using a machine learning classification method we identified discriminating structural brain trait features and correlated the best features with a continuous measure of individual BCI-performance. Prediction of the aptitude group of each participant was possible with near perfect accuracy (one error). Conclusions: Tissue volumetric analysis yielded only poor classification results. In contrast, the structural integrity and myelination quality of deep white matter structures such as the Corpus Callosum, Cingulum, and Superior Fronto-Occipital Fascicle were positively correlated with individual BCI-performance. Significance: This confirms that structural brain traits contribute to individual performance in BCI use.},
  language  = {en}
}