Biomedical subjects

Stephan Kannengiesser

Publications and source records attributed to Stephan Kannengiesser.

3 recordsLinked to original sources

PubMed2006

High-resolution continuously acquired peripheral MR angiography featuring partial parallel imaging GRAPPA.

Continuously-moving-table MRI, in contrast to traditional multistation techniques, potentially can improve the scan time efficiency of whole-body applications and provide seamless images of an extended field of view (FOV). Contrast-enhanced MR angiography (CE-MRA) in particular requires high spatial resolution and at the same time has rigid scan time constraints due to the limited arterial contrast window. In this study a reconstruction method for continuously acquired 3D data sets during table movement was combined with a self-calibrated partial parallel imaging algorithm (generalized autocalibrating partially parallel acquisitions (GRAPPA)). The method was applied to peripheral CE-MRA and compared with a standard continuously-moving-table MRA protocol. The gain in scan time was used to increase the data acquisition matrix and decrease the slice thickness. The method was evaluated in five healthy volunteers and applied to one patient with peripheral arterial occlusive disease (PAOD). The protocols were intraindividually compared with respect to the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in selected vessel segments, as well as overall vessel depiction. The combination of the continuously-moving-table technique with parallel imaging enabled the acquisition of seamless peripheral 3D MRA with increased resolution and an overall crisper appearance.

Algorithms↗

PubMed2006

Autocalibrated coil sensitivity estimation for parallel imaging.

Parallel imaging has proven to be a robust solution to the problem of acquisition speed in MRI. These methods are based on extracting spatial information from an array of multiple surface coils in order to speed up image acquisition. One of the most essential elements of any parallel imaging method is the information describing the coil sensitivity distribution throughout the sample. This paper covers some of the advanced methods to obtain coil sensitivity-related information, focusing particularly on the class of methods referred to as autocalibrating. These methods all acquire the data for coil sensitivity estimation directly before, during or directly after the reduced data acquisition. After a review of standard methods for coil sensitivity estimation, some of the basic and advanced autocalibrating methods are reviewed, and some example applications shown.

Algorithms↗

PubMed2004

Field-of-view limitations in parallel imaging.

Parallel imaging is one of the most promising developments in recent years for the acceleration of MR acquisitions. One area of practical importance where different parallel imaging methods perform differently is the manner in which they deal with aliasing in the full-FOV reconstructed image. It has been reported that sensitivity encoding (SENSE) reconstruction fails whenever the reconstructed FOV is smaller than the object being imaged. On the other hand, generalized autocalibrating partially parallel acquisition (GRAPPA) has been used successfully to reconstruct images with aliasing in the reconstructed FOV, as in conventional imaging. The disparate behavior of these methods can be easily demonstrated by a few simple illustrative examples. Additional in vivo examples using GRAPPA and modified SENSE (mSENSE) make this distinction clear. These experiments demonstrate that SENSE fails to reconstruct correct images when coil sensitivity maps are used that do not automatically account for the object size and therefore the aliasing in the reconstructed images. However, with the use of aliased high-resolution coil sensitivity maps, accurate SENSE reconstructions can be generated. On the other hand, GRAPPA produces images with an aliasing appearance that is exactly as would be expected from normal nonaccelerated acquisitions. An understanding of these effects could potentially lead to fewer operator-dependent errors, as well as a better understanding of the differences between the underlying reconstruction processes.

Computer Simulation↗