𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Advances in sensitivity encoding with arbitrary k-space trajectories

✍ Scribed by Klaas P. Pruessmann; Markus Weiger; Peter Börnert; Peter Boesiger


Publisher
John Wiley and Sons
Year
2001
Tongue
English
Weight
482 KB
Volume
46
Category
Article
ISSN
0740-3194

No coin nor oath required. For personal study only.

✦ Synopsis


Abstract

New, efficient reconstruction procedures are proposed for sensitivity encoding (SENSE) with arbitrary k‐space trajectories. The presented methods combine gridding principles with so‐called conjugate‐gradient iteration. In this fashion, the bulk of the work of reconstruction can be performed by fast Fourier transform (FFT), reducing the complexity of data processing to the same order of magnitude as in conventional gridding reconstruction. Using the proposed method, SENSE becomes practical with nonstandard k‐space trajectories, enabling considerable scan time reduction with respect to mere gradient encoding. This is illustrated by imaging simulations with spiral, radial, and random k‐space patterns. Simulations were also used for investigating the convergence behavior of the proposed algorithm and its dependence on the factor by which gradient encoding is reduced. The in vivo feasibility of non‐Cartesian SENSE imaging with iterative reconstruction is demonstrated by examples of brain and cardiac imaging using spiral trajectories. In brain imaging with six receiver coils, the number of spiral interleaves was reduced by factors ranging from 2 to 6. In cardiac real‐time imaging with four coils, spiral SENSE permitted reducing the scan time per image from 112 ms to 56 ms, thus doubling the frame‐rate. Magn Reson Med 46:638–651, 2001. © 2001 Wiley‐Liss, Inc.


📜 SIMILAR VOLUMES


Optimization of sensitivity encoding wit
✍ Mark Bydder; Joanna E. Perthen; Jiang Du 📂 Article 📅 2007 🏛 Elsevier Science 🌐 English ⚖ 525 KB

Sensitivity encoding (SENSE) is a magnetic resonance technique that unifies gradient and receive coil encoding. SENSE reconstructs the image by solving a large, ill-conditioned inverse problem, which generally requires regularization and preconditioning. The present study suggests a simple heuristic

A statistical approach to SENSE regulari
✍ Leslie Ying; Bo Liu; Michael C. Steckner; Gaohong Wu; Min Wu; Shi-Jiang Li 📂 Article 📅 2008 🏛 John Wiley and Sons 🌐 English ⚖ 867 KB

## Abstract SENSE reconstruction suffers from an ill‐conditioning problem, which increasingly lowers the signal‐to‐noise ratio (SNR) as the reduction factor increases. Ill‐conditioning also degrades the convergence behavior of iterative conjugate gradient reconstructions for arbitrary trajectories.

Parallel spectroscopic imaging reconstru
✍ Meng Gu; Chunlei Liu; Daniel M. Spielman 📂 Article 📅 2009 🏛 John Wiley and Sons 🌐 English ⚖ 409 KB

## Abstract Parallel imaging reconstruction has been successfully applied to magnetic resonance spectroscopic imaging (MRSI) to reduce scan times. For undersampled k‐space data on a Cartesian grid, the reconstruction can be achieved in image domain using a sensitivity encoding (SENSE) algorithm for

3Parallel magnetic resonance imaging wit
✍ Ernest N. Yeh; Charles A. McKenzie; Michael A. Ohliger; Daniel K. Sodickson 📂 Article 📅 2005 🏛 John Wiley and Sons 🌐 English ⚖ 481 KB

## Abstract A parallel image reconstruction algorithm is presented that exploits the __k__‐space locality in radiofrequency (RF) coil encoded data. In RF coil encoding, information relevant to reconstructing an omitted datum rapidly diminishes as a function of __k__‐space separation between the omi