@phdthesis{PonceGarcia2018,
  author    = {Ponce Garcia, Irene Paola},
  title     = {Strategies for optimizing dynamic MRI},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-162622},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2018},
  abstract  = {In Magnetic Resonance Imaging (MRI), acquisition of dynamic data may be highly complex due to rapid changes occurred in the object to be imaged. For clinical diagnostic, dynamic MR images require both high spatial and temporal resolution. The speed in the acquisition is a crucial factor to capture optimally dynamics of the objects to obtain accurate diagnosis. In the 90's, partially parallel MRI (pMRI) has been introduced to shorten scan times reducing the amount of acquired data. These approaches use multi-receiver coil arrays to acquire independently and simultaneously the data. Reduction in the amount of acquired data results in images with aliasing artifacts. Dedicated methods as such Sensitivity Encoding (SENSE) and Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA) were the basis of a series of algorithms in pMRI. Nevertheless, pMRI methods require extra spatial or temporal information in order to optimally reconstruct the data. This information is typically obtained by an extra scan or embedded in the accelerated acquisition applying a variable density acquisition scheme. In this work, we were able to reduce or totally eliminate the acquisition of the training data for kt-SENSE and kt-PCA algorithms obtaining accurate reconstructions with high temporal fidelity. For dynamic data acquired in an interleaved fashion, the temporal average of accelerated data can generate an artifact-free image used to estimate the coil sensitivity maps avoiding the need of extra acquisitions. However, this temporal average contains errors from aliased components, which may lead to signal nulls along the spectra of reconstructions when methods like kt-SENSE are applied. The use of a GRAPPA filter applied to the temporal average reduces these errors and subsequently may reduce the null components in the reconstructed data. In this thesis the effect of using temporal averages from radial data was investigated. Non-periodic artifacts performed by undersampling radial data allow a more accurate estimation of the true temporal average and thereby avoiding undesirable temporal filtering in the reconstructed images. kt-SENSE exploits not only spatial coil sensitivity variations but also makes use of spatio-temporal correlations in order to separate the aliased signals. Spatio-temporal correlations in kt-SENSE are learnt using a training data set, which consists of several central k-space lines acquired in a separate scan. The scan of these extra lines results in longer acquisition times even for low resolution images. It was demonstrate that limited spatial resolution of training data set may lead to temporal filtering effects (or temporal blurring) in the reconstructed data. In this thesis, the auto-calibration for kt-SENSE was proposed and its feasibility was tested in order to completely eliminate the acquisition of training data. The application of a prior TSENSE reconstruction produces the training data set for the kt-SENSE algorithm. These training data have full spatial resolution. Furthermore, it was demonstrated that the proposed auto-calibrating method reduces significantly temporal filtering in the reconstructed images compared to conventional kt-SENSE reconstructions employing low resolution training images. However, the performance of auto-calibrating kt-SENSE is affected by the Signal-to-Noise Ratio (SNR) of the first pass reconstructions that propagates to the final reconstructions. Another dedicated method used in dynamic MRI applications is kt-PCA, that was first proposed for the reconstruction of MR cardiac data. In this thesis, kt-PCA was employed for the generation of spatially resolved M0, T1 and T2 maps from a single accelerated IRTrueFISP or IR-Snapshot FLASH measurement. In contrast to cardiac dynamic data, MR relaxometry experiments exhibit signal at all temporal frequencies, which makes their reconstruction more challenging. However, since relaxometry measurements can be represented by only few parameters, the use of few principal components (PC) in the kt-PCA algorithm can significantly simplify the reconstruction. Furthermore, it was found that due to high redundancy in relaxometry data, PCA can efficiently extract the required information from just a single line of training data. It has been demonstrated in this thesis that auto-calibrating kt-SENSE is able to obtain high temporal fidelity dynamic cardiac reconstructions from moderate accelerated data avoiding the extra acquisition of training data. Additionally, kt-PCA has been proved to be a suitable method for the reconstruction of highly accelerated MR relaxometry data. Furthermore, a single central training line is necessary to obtain accurate reconstructions. Both reconstruction methods are promising for the optimization of training data acquisition and seem to be feasible for several clinical applications.},
  subject      = {Kernspintomografie},
  language  = {en}
}