- Walking poster presentation
- Open Access
Reducing scan time for calibration of through-time radial GRAPPA using PCA coil compression
© Hamilton et al; licensee BioMed Central Ltd. 2015
- Published: 3 February 2015
- Parallel Imaging
- Coil Sensitivity
- 128x128 Matrix
- Short Axis Orientation
- Calibration Frame
Although using more coils may improve parallel imaging performance, it produces a burden on the amount of calibration data required for autocalibrating methods. This work investigates the use of principal component analysis (PCA) to project the original set of coils onto a smaller set of virtual coils to reduce calibration scan time for through-time radial GRAPPA [Seiberlich, et al. Magn Reson Med. 2011; 65(2):492-505].
Simulations were performed using a randomly moving Shepp-Logan phantom and a simulated 24-channel array. Calibration data (144 projection, 80 frames, 128x128 matrix) were generated along a fully-sampled radial trajectory, and an additional 40 frames were undersampled to 24, 16, and 12 projections. Through-time radial GRAPPA reconstruction was performed with a 4x1 (read x projection) k-space segment size and varying numbers of calibration frames. The reconstructions were repeated after PCA coil compression that captured 93%, 91%, 87% and 81% of the variation in the original coil maps (12, 9, 6, and 4 virtual coils). In vivo cardiac data were also collected in one healthy volunteer in this IRB approved, HIPAA compliant study using a 30 channel receiver array on a 3T scanner. Both calibration (144 projections, 160 frames, 68s) and undersampled (24, 16, and 12 projections) data were collected using a radial bSSFP sequence with 128x128 matrix and TR/TE=2.94/1.47ms. Data were collected in short-axis and 4-chamber orientations to demonstrate how differences in coil geometry affect the acceptable amount of PCA coil compression when combined with parallel imaging.
PCA coil compression can be used to reduce calibration scan time for through-time radial GRAPPA. This technique may be especially useful for interventional imaging or 3D parallel imaging.
NIH/NIBIB R00EB011527 and Siemens Medical Solutions.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.