Francesco Molinari1, Ananth J Madhuranthakam, Robert Lenkinski, Alexander A Bankier. 1. From the Department of Radiology (F.M. e-mail: francescomolinari.dr@gmail.com, R.L., A.A.B.), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA; Global Applied Science Laboratory (A.J.M.), GE Healthcare, Boston, Massachusetts, USA.
Abstract
PURPOSE: We aimed to develop a predictive model for lung water content using ultrashort echo time (UTE) magnetic resonance imaging (MRI) and a sponge phantom. MATERIALS AND METHODS: Image quality was preliminarily optimized, and the signal-to-noise ratio (SNR) of UTE was compared with that obtained from a three-dimensional fast gradient echo (FGRE) sequence. Four predetermined volumes of water (3.5, 3.0, 2.5, and 2.0 mL) were soaked in cellulose foam sponges 1.8 cm3 in size and were imaged with UTE-MRI at 1.5 and 3.0 Tesla (T). A multiple echo time experiment (range, 0.1-9.6 ms) was conducted, and the T2 signal decay curve was determined at each volume of water. A three-parameter equation was fitted to the measured signal, allowing for the calculation of proton density and T2*. The calculation error of proton density was determined as a function of echo time. The constants that allowed for the determination of unknown volumes of water from the measured proton density were calculated using linear regression. RESULTS: UTE-MRI provided excellent image quality for the four phantoms and showed a higher SNR, compared to that of FGRE. Proton density decreased proportionally with the decreases in both lung water and field strength (from 3.5 to 2.0 mL; proton density range at 1.5 T, 30.5-17.3; at 3.0 T, 84.2-41.5). Minimum echo time less than 0.6 ms at 1.5 T and 1 ms at 3.0 T maintained calculation errors for proton density within the range of 0%-10%. The slopes of the lines for determining the unknown volumes of water with UTE-MRI were 0.12±0.003 at 1.5 T and 0.05±0.002 at 3.0 T (P < 0.0001). CONCLUSION: In a sponge phantom imaged at 1.5 and 3.0 T, unknown volumes of water can be predicted with high accuracy using UTE-MRI.
PURPOSE: We aimed to develop a predictive model for lung water content using ultrashort echo time (UTE) magnetic resonance imaging (MRI) and a sponge phantom. MATERIALS AND METHODS: Image quality was preliminarily optimized, and the signal-to-noise ratio (SNR) of UTE was compared with that obtained from a three-dimensional fast gradient echo (FGRE) sequence. Four predetermined volumes of water (3.5, 3.0, 2.5, and 2.0 mL) were soaked in cellulose foam sponges 1.8 cm3 in size and were imaged with UTE-MRI at 1.5 and 3.0 Tesla (T). A multiple echo time experiment (range, 0.1-9.6 ms) was conducted, and the T2 signal decay curve was determined at each volume of water. A three-parameter equation was fitted to the measured signal, allowing for the calculation of proton density and T2*. The calculation error of proton density was determined as a function of echo time. The constants that allowed for the determination of unknown volumes of water from the measured proton density were calculated using linear regression. RESULTS: UTE-MRI provided excellent image quality for the four phantoms and showed a higher SNR, compared to that of FGRE. Proton density decreased proportionally with the decreases in both lung water and field strength (from 3.5 to 2.0 mL; proton density range at 1.5 T, 30.5-17.3; at 3.0 T, 84.2-41.5). Minimum echo time less than 0.6 ms at 1.5 T and 1 ms at 3.0 T maintained calculation errors for proton density within the range of 0%-10%. The slopes of the lines for determining the unknown volumes of water with UTE-MRI were 0.12±0.003 at 1.5 T and 0.05±0.002 at 3.0 T (P < 0.0001). CONCLUSION: In a sponge phantom imaged at 1.5 and 3.0 T, unknown volumes of water can be predicted with high accuracy using UTE-MRI.
Authors: Francesco Molinari; Denis M Tack; Philip Boiselle; Long Ngo; Christina Mueller-Mang; Diana Litmanovich; Alexander A Bankier Journal: Diagn Interv Radiol Date: 2013 May-Jun Impact factor: 2.630
Authors: Laura L Walkup; Jean A Tkach; Nara S Higano; Robert P Thomen; Sean B Fain; Stephanie L Merhar; Robert J Fleck; Raouf S Amin; Jason C Woods Journal: Am J Respir Crit Care Med Date: 2015-11-15 Impact factor: 21.405
Authors: Bruno M L Rocha; Gonçalo J L Cunha; Pedro Freitas; Pedro M D Lopes; Ana C Santos; Sara Guerreiro; António Tralhão; António Ventosa; Maria J Andrade; João Abecasis; Carlos Aguiar; Carla Saraiva; Miguel Mendes; António M Ferreira Journal: Sci Rep Date: 2021-10-11 Impact factor: 4.379