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Effect of hybrid of compressed sensing and parallel imaging on the quantitative values measured by 3D quantitative
Syo Murata1, Akifumi Hagiwara1, Shohei Fujita2
1Department of Radiology, Juntendo University Hospital, Tokyo, Japan.
Introduction:
Recently, three-dimensional (3D) quantitative synthetic magnetic resonance imaging (MRI), which quantifies tissue properties and creates multiple contrast-weighted images, has been enabled by 3D-quantification using an interleaved Look-Locker acquisition sequence with a T2 preparation pulse (3D-QALAS). However, the relatively long scan time has hindered its introduction into clinical practice. A hybrid of compressed sensing and parallel imaging (Compressed sensing-sensitivity encoding: CS-SENSE) can accelerate 3D-QALAS; however, whether CS-SENSE affects the quantitative values acquired by 3D-QALAS remains unexplored. Therefore, this study aimed to examine the effects of reduction factors of CS-SENSE (RCSS) on the quantitative values derived from 3D-QALAS, by assessing the signal-to-noise ratio (SNR) of the quantitative maps, as well as accuracy (linearity and bias) and repeatability of measured quantitative values.
Methods:
In this study, the ISMRM/NIST standardized phantom was scanned on a 1.5-T MRI scanner with 3D-QALAS using RCSS in the range between 1 and 3, with intervals of 0.2, and between 3 and 10 with intervals of 0.5. The T1, T2, and proton density (PD) values were calculated from the imaging data. For each quantitative value, the SNR, the coefficient of determination (R2) of a linear regression model, the error rate, and the within-subject coefficient of variation (wCV) were calculated for each RCSS and compared.
Results:
Within the clinically-relevant dynamic range of the brain of T1 and T2 (T1: 200-1400 ms; T2; 50-400 ms) and PD value of 15-100% calculated from 3D-QALAS, the effects of RCSS on quantitative values was small between 1 and 2.8, with SNR ≧ 10, R2 ≧ 0.9, error rate ≦ 10%, and wCV ≦ 10%, except for T2 values of 186.1 and 258.4 ms.
Conclusions:
CS-SENSE enabled the reduction of the scan time of 3D-QALAS by 63.5% (RCSS = 2.8) while maintaining the SNR of quantitative maps and accuracy and repeatability of the quantitative values.
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