VIP Club Connection, February 2007, Vol. 7, no. 1.

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

VIP Connection, February 2006, Vol. 6, no. 1

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

VIP Connection, May, 2006, Vol. 6, no. 2

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

VIP Connection, August 2006, Vol. 6, no. 3

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

VIP Connection, November 2006, Vol. 6. no. 4

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

VIP Connection, February 2005, Vol. 5, no. 1

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

VIP Connection, May 2005, Vol. 5, no. 2

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

VIP Connection, August 2005, Vol. 5, no. 3

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

VIP Connection, November 2005, Vol. 5, no. 4

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

Parallel imaging with phase scrambling.

PURPOSE: Most existing methods for accelerated parallel imaging in MRI require additional data, which are used to derive information about the sensitivity profile of each radiofrequency (RF) channel. In this work, a method is presented to avoid the acquisition of separate coil calibration data for accelerated Cartesian trajectories. METHODS: Quadratic phase is imparted to the image to spread the signals in k-space (aka phase scrambling). By rewriting the Fourier transform as a convolution operation, a window can be introduced to the convolved chirp function, allowing a low-resolution image to be reconstructed from phase-scrambled data without prominent aliasing. This image (for each RF channel) can be used to derive coil sensitivities to drive existing parallel imaging techniques. As a proof of concept, the quadratic phase was applied by introducing an offset to the x(2) - y(2) shim and the data were reconstructed using adapted versions of the image space-based sensitivity encoding and GeneRalized Autocalibrating Partially Parallel Acquisitions algorithms. RESULTS: The method is demonstrated in a phantom (1 × 2, 1 × 3, and 2 × 2 acceleration) and in vivo (2 × 2 acceleration) using a 3D gradient echo acquisition. CONCLUSION: Phase scrambling can be used to perform parallel imaging acceleration without acquisition of separate coil calibration data, demonstrated here for a 3D-Cartesian trajectory. Further research is required to prove the applicability to other 2D and 3D sampling schemes. Magn Reson Med, 2014. © 2014 Wiley Periodicals, Inc.

Iowa Lottery VIP Club Connection, May, 2004, vol. 4, no. 2.

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

VIP Connection, August 2004, Vol. 4, no. 3

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

VIP Connection, November 2004, Vol. 4, no. 4

Iowa Lottery newsletter for members of the Iowa Lottery VIP Club.

Inherently self-calibrating non-Cartesian parallel imaging.

The use of self-calibrating techniques in parallel magnetic resonance imaging eliminates the need for coil sensitivity calibration scans and avoids potential mismatches between calibration scans and subsequent accelerated acquisitions (e.g., as a result of patient motion). Most examples of self-calibrating Cartesian parallel imaging techniques have required the use of modified k-space trajectories that are densely sampled at the center and more sparsely sampled in the periphery. However, spiral and radial trajectories offer inherent self-calibrating characteristics because of their densely sampled center. At no additional cost in acquisition time and with no modification in scanning protocols, in vivo coil sensitivity maps may be extracted from the densely sampled central region of k-space. This work demonstrates the feasibility of self-calibrated spiral and radial parallel imaging using a previously described iterative non-Cartesian sensitivity encoding algorithm.

Girl Connection, June 2007

A monthly newsletter for Iowa Commission on the Status of Women