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定制磁共振指纹识别

Tailored magnetic resonance fingerprinting.

作者信息

Poojar Pavan, Qian Enlin, Fernandes Tiago T, Nunes Rita G, Fung Maggie, Quarterman Patrick, Jambawalikar Sachin R, Lignelli Angela, Geethanath Sairam

机构信息

Icahn School of Medicine at Mt. Sinai, New York, NY, USA; Columbia Magnetic Resonance Research Center, Columbia University in the city of New York, NY, USA.

Columbia Magnetic Resonance Research Center, Columbia University in the city of New York, NY, USA.

出版信息

Magn Reson Imaging. 2023 Jun;99:81-90. doi: 10.1016/j.mri.2023.02.002. Epub 2023 Feb 9.

Abstract

Neuroimaging of certain pathologies requires both multi-parametric qualitative and quantitative imaging. The role of the quantitative MRI (qMRI) is well accepted but suffers from long acquisition times leading to patient discomfort, especially in geriatric and pediatric patients. Previous studies show that synthetic MRI can be used in order to reduce the scan time and provide qMRI as well as multi-contrast data. However, this approach suffers from artifacts such as partial volume and flow. In order to increase the scan efficiency (the number of contrasts and quantitative maps acquired per unit time), we designed, simulated, and demonstrated rapid, simultaneous, multi-contrast qualitative (T weighted, T fluid attenuated inversion recovery (FLAIR), T weighted, water, and fat), and quantitative imaging (T and T maps) through the approach of tailored MR fingerprinting (TMRF) to cover whole-brain in approximately four minutes. We performed TMRF on in vivo four healthy human brains and in vitro ISMRM/NIST phantom and compared with vendor supplied gold standard (GS) and MRF sequences. All scans were performed on a 3 T GE Premier system and images were reconstructed offline using MATLAB. The reconstructed qualitative images were then subjected to custom DL denoising and gradient anisotropic diffusion denoising. The quantitative tissue parametric maps were reconstructed using a dense neural network to gain computational speed compared to dictionary matching. The grey matter and white matter tissues in qualitative and quantitative data for the in vivo datasets were segmented semi-automatically. The SNR and mean contrasts were plotted and compared across all three methods. The GS images show better SNR in all four subjects compared to MRF and TMRF (GS > TMRF>MRF). The T and T values of MRF are relatively overestimated as compared to GS and TMRF. The scan efficiency for TMRF is 1.72 min which is higher compared to GS (0.32 min) and MRF (0.90 min).

摘要

某些病症的神经成像需要多参数定性和定量成像。定量磁共振成像(qMRI)的作用已得到广泛认可,但其采集时间长,会给患者带来不适,尤其是老年和儿科患者。先前的研究表明,合成磁共振成像可用于减少扫描时间,并提供qMRI以及多对比度数据。然而,这种方法存在诸如部分容积和血流等伪影。为了提高扫描效率(每单位时间获取的对比度和定量图谱数量),我们通过定制磁共振指纹识别(TMRF)方法设计、模拟并展示了快速、同步的多对比度定性成像(T加权、液体衰减反转恢复序列(FLAIR)、T加权、水和脂肪成像)以及定量成像(T和T图谱),可在约四分钟内覆盖全脑。我们对四名健康人类活体大脑和体外ISMRM/NIST体模进行了TMRF,并与供应商提供的金标准(GS)和MRF序列进行了比较。所有扫描均在3T的GE Premier系统上进行,图像使用MATLAB离线重建。然后,对重建的定性图像进行定制的深度学习去噪和梯度各向异性扩散去噪。与字典匹配相比,使用密集神经网络重建定量组织参数图谱以提高计算速度。对活体数据集定性和定量数据中的灰质和白质组织进行半自动分割。绘制并比较了所有三种方法的信噪比和平均对比度。与MRF和TMRF相比,GS图像在所有四名受试者中显示出更好的信噪比(GS>TMRF>MRF)。与GS和TMRF相比,MRF的T和T值相对高估。TMRF的扫描效率为1.72分钟,高于GS(0.32分钟)和MRF(0.90分钟)。

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