@phdthesis{TranGia2014,
  author    = {Tran-Gia, Johannes},
  title     = {Model-Based Reconstruction Methods for MR Relaxometry},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-109774},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2014},
  abstract  = {In this work, a model-based acceleration of parameter mapping (MAP) for the determination of the tissue parameter T1 using magnetic resonance imaging (MRI) is introduced. The iterative reconstruction uses prior knowledge about the relaxation behavior of the longitudinal magnetization after a suitable magnetization preparation to generate a series of fully sampled k-spaces from a strongly undersampled acquisition. A Fourier transform results in a spatially resolved time course of the longitudinal relaxation process, or equivalently, a spatially resolved map of the longitudinal relaxation time T1. In its fastest implementation, the MAP algorithm enables the reconstruction of a T1 map from a radial gradient echo dataset acquired within only a few seconds after magnetization preparation, while the acquisition time of conventional T1 mapping techniques typically lies in the range of a few minutes. After validation of the MAP algorithm for two different types of magnetization preparation (saturation recovery \& inversion recovery), the developed algorithm was applied in different areas of preclinical and clinical MRI and possible advantages and disadvantages were evaluated.},
  subject      = {Kernspintomographie},
  language  = {en}
}