Single-shot multi-parametric mapping based on multiple overlapping-echo detachment (MOLED) imaging

参数统计 计算机科学 重复性 磁共振成像 灵敏度(控制系统) 人工智能 计算机视觉 核磁共振 物理 模式识别(心理学) 数学 工程类 医学 统计 电子工程 放射科
作者
Lingceng Ma,Jian Wu,Qinqin Yang,Zihan Zhou,Hongjian He,Jianfeng Bao,Lijun Bao,Xiaoyin Wang,Pujie Zhang,Jianhui Zhong,Congbo Cai,Shuhui Cai,Zhong Chen
出处
期刊:NeuroImage [Elsevier]
卷期号:263: 119645-119645 被引量:8
标识
DOI:10.1016/j.neuroimage.2022.119645
摘要

Multi-parametric quantitative magnetic resonance imaging (mqMRI) allows the characterization of multiple tissue properties non-invasively and has shown great potential to enhance the sensitivity of MRI measurements. However, real-time mqMRI during dynamic physiological processes or general motions remains challenging. To overcome this bottleneck, we propose a novel mqMRI technique based on multiple overlapping-echo detachment (MOLED) imaging, termed MQMOLED, to enable mqMRI in a single shot. In the data acquisition of MQMOLED, multiple MR echo signals with different multi-parametric weightings and phase modulations are generated and acquired in the same k-space. The k-space data is Fourier transformed and fed into a well-trained neural network for the reconstruction of multi-parametric maps. We demonstrated the accuracy and repeatability of MQMOLED in simultaneous mapping apparent proton density (APD) and any two parameters among T2, T2*, and apparent diffusion coefficient (ADC) in 130-170 ms. The abundant information delivered by the multiple overlapping-echo signals in MQMOLED makes the technique potentially robust to system imperfections, such as inhomogeneity of static magnetic field or radiofrequency field. Benefitting from the single-shot feature, MQMOLED exhibits a strong motion tolerance to the continuous movements of subjects. For the first time, it captured the synchronous changes of ADC, T2, and T1-weighted APD in contrast-enhanced perfusion imaging on patients with brain tumors, providing additional information about vascular density to the hemodynamic parametric maps. We expect that MQMOLED would promote the development of mqMRI technology and greatly benefit the applications of mqMRI, including therapeutics and analysis of metabolic/functional processes.
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