Center for Advanced Imaging Innovation and Research (CAI2R) and Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, New York, USA.
Vilcek Institute of Graduate Biomedical Sciences, New York University Grossman School of Medicine, New York, New York, USA.
J Magn Reson Imaging. 2021 Dec;54(6):1952-1964. doi: 10.1002/jmri.27816. Epub 2021 Jul 4.
Signal-to-noise ratio (SNR) is used to evaluate the performance of magnetic resonance (MR) imaging systems. Accurate and consistent estimations are needed to routinely use SNR to assess coils and image reconstruction techniques.
To identify a reliable and practical method for SNR estimation in multiple-coil reconstructions.
Technical evaluation and comparison.
SUBJECTS/PHANTOM: A uniform phantom and four healthy volunteers: 35, 38, 39 y/o males, 25 y/o female.
FIELD STRENGTH/SEQUENCE: Two-dimensional multislice gradient-echo pulse sequence at 3 T and 7 T.
Reference-standard SNR was calculated from 100 multiple replicas. Six SNR methods were compared against it: difference image (DI), analytic array combination (AC), pseudo-multiple-replica (PMR), generalized pseudo-replica (GPR), smoothed image subtraction (SIS), and DI with temporal instability correction (TIC). The assessment was repeated for different multiple-coil reconstructions.
SNR methods were evaluated in terms of relative deviation (RD) and normalized mutual information (NMI) with respect to the reference-standard, using a linear regression (0.05 significance level) to assess how different factors affect accuracy.
Average RD (phantom) for DI, AC, PMR, GPR, SIS, and TIC was 7.9%, 6%, 6.7%, 10.1%, 40%, and 14.6%, respectively. RD increased with acceleration. SNR maps with AC were the most similar to the reference standard (NMI = 0.358). Considering all brain regions of interest, average RD for all SNR methods varied 96% among volunteers but remained approximately 10% for AC, PMR, and GPR, whereas it was more than 30% for DI, SIS, and TIC. RD was mainly affected by image reconstruction (beta = 12) for AC and SNR entropy for SIS (beta = 19).
AC provided accurate and robust SNR estimation. PMR and GPR are more generally applicable than AC. DI and TIC should be used only at low acceleration factors, when an additional noise-only scan cannot be acquired. SIS is a single-acquisition alternative to DI for generalized autocalibrating partial parallel acquisition (GRAPPA) reconstructions.
1 TECHNICAL EFFICACY: Stage 1.
信噪比（SNR）用于评估磁共振（MR）成像系统的性能。为了常规使用 SNR 来评估线圈和图像重建技术，需要进行准确且一致的估计。
确定一种用于多线圈重建中 SNR 估计的可靠且实用的方法。
技术评估和比较。
受试者/体模：均匀体模和 4 名健康志愿者：35、38、39 岁男性，25 岁女性。
磁场强度/序列：3T 和 7T 下的二维多切片梯度回波脉冲序列。
从 100 个重复中计算出参考标准 SNR。比较了 6 种 SNR 方法：差值图像（DI）、解析数组组合（AC）、伪多重复（PMR）、广义伪重复（GPR）、平滑图像减法（SIS）和具有时间不稳定性校正的 DI（TIC）。针对不同的多线圈重建重复了评估。
使用线性回归（0.05 显著性水平）评估不同因素对准确性的影响，根据相对偏差（RD）和归一化互信息（NMI）评估 SNR 方法相对于参考标准的表现。
DI、AC、PMR、GPR、SIS 和 TIC 的平均 RD（体模）分别为 7.9%、6%、6.7%、10.1%、40%和 14.6%。RD 随加速而增加。AC 生成的 SNR 图与参考标准最相似（NMI=0.358）。考虑所有感兴趣的脑区，所有 SNR 方法的平均 RD 在志愿者之间变化 96%，但对于 AC、PMR 和 GPR 保持约 10%，而对于 DI、SIS 和 TIC 则超过 30%。RD 主要受 AC 的图像重建（β=12）和 SIS 的 SNR 熵（β=19）影响。
AC 提供了准确且稳健的 SNR 估计。PMR 和 GPR 比 AC 更具通用性。DI 和 TIC 仅应在无法获取额外噪声仅扫描的情况下在低加速因子下使用。SIS 是用于广义自校准部分并行采集（GRAPPA）重建的 DI 的单采集替代方法。
1 技术功效：阶段 1。
