OBJECT: To develop a signal model for accurate prediction of myocardial signal during cine-balanced steady-state free precession (bSSFP) imaging. METHODS: We present a signal model that takes into account the effects of non-ideal slice profile, off-resonance, and radio-frequency transmit variation on myocardial signal behavior. Each of the three factors was examined over the range of imaging parameters routinely used in cine bSSFP cardiac imaging at 3 Tesla. RESULTS: In five healthy volunteers and over a wide range of prescribed flip angles, the conventional on-resonance signal model exhibited 28.9 +/- 3.9% error, while the proposed model exhibited only 2.9 +/- 1.4% error, and therefore more accurate predictions of myocardial signal behavior. Slice profile effects were found to be significant and accounted for most of the improvement. Off-resonance and RF transmit inhomogeneity effects were less significant but did produce more accurate signal prediction. CONCLUSIONS: The proposed signal model produced more accurate predictions of myocardial signal compared to existing models and can be used for the optimization of pulse sequences and protocols.
OBJECT: To develop a signal model for accurate prediction of myocardial signal during cine-balanced steady-state free precession (bSSFP) imaging. METHODS: We present a signal model that takes into account the effects of non-ideal slice profile, off-resonance, and radio-frequency transmit variation on myocardial signal behavior. Each of the three factors was examined over the range of imaging parameters routinely used in cine bSSFP cardiac imaging at 3 Tesla. RESULTS: In five healthy volunteers and over a wide range of prescribed flip angles, the conventional on-resonance signal model exhibited 28.9 +/- 3.9% error, while the proposed model exhibited only 2.9 +/- 1.4% error, and therefore more accurate predictions of myocardial signal behavior. Slice profile effects were found to be significant and accounted for most of the improvement. Off-resonance and RF transmit inhomogeneity effects were less significant but did produce more accurate signal prediction. CONCLUSIONS: The proposed signal model produced more accurate predictions of myocardial signal compared to existing models and can be used for the optimization of pulse sequences and protocols.
Authors: Juerg Schwitter; Christian M Wacker; Albert C van Rossum; Massimo Lombardi; Nidal Al-Saadi; Hakan Ahlstrom; Thorsten Dill; Henrik B W Larsson; Scott D Flamm; Moritz Marquardt; Lars Johansson Journal: Eur Heart J Date: 2008-01-21 Impact factor: 29.983
Authors: Mevan Perera; Emeline J Ribot; Dean B Percy; Catherine McFadden; Carmen Simedrea; Diane Palmieri; Ann F Chambers; Paula J Foster Journal: Transl Oncol Date: 2012-06-01 Impact factor: 4.243
Authors: Donna H Murrell; Amanda M Hamilton; Christiane L Mallett; Robbert van Gorkum; Ann F Chambers; Paula J Foster Journal: Transl Oncol Date: 2015-06 Impact factor: 4.243