imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium; μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium.
Comput Med Imaging Graph. 2022 Sep;100:102071. doi: 10.1016/j.compmedimag.2022.102071. Epub 2022 May 10.
Quantitative Magnetic Resonance (MR) imaging provides reproducible measurements of biophysical parameters, and has become an essential tool in clinical MR studies. Unfortunately, 3D isotropic high resolution (HR) parameter mapping is hardly feasible in clinical practice due to prohibitively long acquisition times. Moreover, accurate and precise estimation of quantitative parameters is complicated by inevitable subject motion, the risk of which increases with scanning time. In this paper, we present a model-based super-resolution reconstruction (SRR) method that jointly estimates HR quantitative parameter maps and inter-image motion parameters from a set of 2D multi-slice contrast-weighted images with a low through-plane resolution. The method uses a Bayesian approach, which allows to optimally exploit prior knowledge of the tissue and noise statistics. To demonstrate its potential, the proposed SRR method is evaluated for a T1 and T2 quantitative mapping protocol. Furthermore, the method's performance in terms of precision, accuracy, and spatial resolution is evaluated using simulated as well as real brain imaging experiments. Results show that our proposed fully flexible, quantitative SRR framework with integrated motion estimation outperforms state-of-the-art SRR methods for quantitative MRI.
定量磁共振（MR）成像可提供生物物理参数的可重复测量，已成为临床 MR 研究中的重要工具。不幸的是，由于采集时间过长，3D 各向同性高分辨率（HR）参数映射在临床实践中几乎难以实现。此外，由于不可避免的受试者运动，定量参数的准确和精确估计变得复杂，而这种风险随着扫描时间的增加而增加。在本文中，我们提出了一种基于模型的超分辨率重建（SRR）方法，该方法可以从一组具有低穿透分辨率的 2D 多切片对比加权图像中联合估计 HR 定量参数图和图像间运动参数。该方法使用贝叶斯方法，这允许最优地利用组织和噪声统计的先验知识。为了展示其潜力，我们针对 T1 和 T2 定量映射协议评估了所提出的 SRR 方法。此外，还使用模拟和真实脑成像实验评估了该方法在精度、准确性和空间分辨率方面的性能。结果表明，我们提出的具有集成运动估计的完全灵活的定量 SRR 框架在定量 MRI 方面优于最先进的 SRR 方法。
