𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Improved spatial harmonic selection for SMASH image reconstructions

✍ Scribed by Charles A. McKenzie; Ernest N. Yeh; Daniel K. Sodickson


Book ID
102531192
Publisher
John Wiley and Sons
Year
2001
Tongue
English
Weight
179 KB
Volume
46
Category
Article
ISSN
0740-3194

No coin nor oath required. For personal study only.

✦ Synopsis


Abstract

The fitting of coil sensitivity functions to spatial harmonics is central to image reconstructions using the simultaneous acquisition of spatial harmonics (SMASH) technique. It has previously been shown that the selection of the set of spatial harmonics used in a SMASH reconstruction can have a noticeable effect on the quality of the reconstructed image. However, a mechanism for automatic selection of the best set of harmonics in any particular situation has not been provided. In this work, a modification to the SMASH reconstruction procedure is introduced that allows the use of a weighted average of all possible harmonics in a reconstruction. The new reconstruction procedure is shown to allow automatic selection of the spatial harmonics and substantially improve SNR for both phantom and in vivo images. Magn Reson Med 46:831–836, 2001. Β© 2001 Wiley‐Liss, Inc.


πŸ“œ SIMILAR VOLUMES


Interresolution Look-up Table for Improv
✍ Guoping Qiu πŸ“‚ Article πŸ“… 2000 πŸ› Elsevier Science 🌐 English βš– 358 KB

The most commonly used image magnification techniques are interpolation based: nearest neighbor, bilinear, and bicubic. The drawbacks of these traditional methods are that images magnified by the simple nearest neighbor method often appear "blocky," while images magnified by linear and cubic interpo

Iterative GRAPPA (iGRAPPA) for improved
✍ Tiejun Zhao; Xiaoping Hu πŸ“‚ Article πŸ“… 2008 πŸ› John Wiley and Sons 🌐 English βš– 308 KB

## Abstract In this work an iterative reconstruction method based on generalized autocalibrating partially parallel acquisitions (GRAPPA) reconstruction is introduced. In the new method the reconstructed lines are used to reestimate and refine the weights from all the acquired data by applying the