Health AI Central
Share:
Peer-Reviewed Publication
J Magn Reson2016;26281-90.January 1, 2016Journal Article

Multiband RF pulses with improved performance via convex optimization.

Hong Shang1, Peder E Z Larson2, Adam Kerr3, Galen Reed4, Subramaniam Sukumar5, Adam Elkhaled2, Jeremy W Gordon5, Michael A Ohliger5, John M Pauly3, Michael Lustig6, Daniel B Vigneron2
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA; UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, CA, USA. Electronic address: shanghong@berkeley.edu.
2Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA; UC Berkeley-UCSF Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, CA, USA.
3Electrical Engineering, Stanford University, Stanford, CA, USA.
4HeartVista, Menlo Park, CA, USA.
5Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
6Electrical Engineering and Computer Science, University of California, Berkeley, CA, USA.
PubMed PMC DOI PDF

Abstract

Selective RF pulses are commonly designed with the desired profile as a low pass filter frequency response. However, for many MRI and NMR applications, the spectrum is sparse with signals existing at a few discrete resonant frequencies. By specifying a multiband profile and releasing the constraint on "don't-care" regions, the RF pulse performance can be improved to enable a shorter duration, shar…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.