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Richard Winkelmann

Publications and source records attributed to Richard Winkelmann.

2 recordsLinked to original sources

Free-breathing whole-heart coronary MR angiography on a clinical scanner in four minutes.

PURPOSE: To set up a robust and patient-friendly whole-heart protocol based on 32-receive-channel technology that will potentially allow a large part of the patient population to be addressed. MATERIALS AND METHODS: Ten volunteers were examined on a clinical 1.5 T scanner equipped with a 32-channel data acquisition system using an experimental 32-element coil array. A magnetization-prepared, navigator-gated and -tracked 3D Cartesian balanced FFE sequence was used for whole-heart coronary MR angiography (MRA). With the use of sensitivity encoding (SENSE) and partial Fourier encoding for scan acceleration, nearly isotropic high-resolution data sets were acquired during free breathing in four minutes. RESULTS: A high contrast and sufficient signal-to-noise ratio (SNR) were obtained, which allowed visualization of the major vessels up to the distal regions and detection of major branches. Phase encoding in the anterior-posterior (AP) direction was the most favorable SENSE configuration and allowed a reasonable scan time reduction with moderate SENSE factors. CONCLUSION: The employed 32-receive channel technology enabled a robust trade-off among SNR, spatial resolution, and scan time. In this study the most robust results were obtained using the smallest possible SENSE factors for a given voxel size and scan time.

Adult↗

Ghost artifact removal using a parallel imaging approach.

Parallel imaging techniques, which use several receive coils simultaneously, have been shown to enable a significant scan time reduction by subsampling k-space. Nevertheless, the data acquired with multiple coils in parallel exhibit some redundancy if the number of receive coils exceeds the subsampling factor. This redundancy leads to an overdetermination of the reconstruction problem, which is generally used to optimize the signal-to-noise ratio (SNR). However, it can yield further information about the quality of the reconstructed image, and can thus be used to identify and correct image artifacts. While some known approaches try to solve this problem in k-space, this study addresses it in the spatial domain and uses a modified SENSE reconstruction to reduce or completely remove ghost-type artifacts arising from processes such as motion or flow during data acquisition. Phantom and in vivo studies show significant improvements in image quality after correction, and serve as a basis for the discussion of the performance and limitations of this new approach.

Algorithms↗