AI enabled motion corrected quantitative MRI of the fetal brain

人工智能支持胎儿大脑的运动校正定量 MRI

• 批准号: 2434728
• 金额: --
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2434728
• 关键词: AI; enabled; motion; corrected; quantitative

Aim of the PhD Project:During the second half of pregnancy the human brain undergoes exuberant growth, with both microscopic and macroscopic changes happening rapidly. In consequence the MRI relaxation times (T1 and T2), which are key tissue properties, change substantially. These relaxation times can be used to characterise development, particularly in white matter. Relaxometry is a well established tool for assessing the brain in health and disease, however, there are currently no established methods for doing this in the fetus in utero. The aim of the project is to develop reliable motion tolerant methods for measuring T1 and T2 in the moving fetus, and to deploy these to conduct systematic quantitative studies over gestational age and so provide normative benchmark data.Project Description / Background:Our group has substantial track record in developing and deploying fully motion corrected methods for fetal 3D brain imaging in utero. A highly effective strategy is snapshot imaging of individual slices or small groups of slices, acquired fast enough to freeze fetal motion, which are then realigned to correct for changes in head position using slice to volume reconstruction (SVR)1. Past research has included developing comprehensive methods for anatomical, diffusion and functional imaging. We have also been able to measure the relaxation time T2* by fitting a standard physics signal model directly to single shot slices acquired using a dedicated multi-echo methodology. This approach allows the model fiting to be separated from motion correction. Methods for measuring T1 and T2 in moving tissue have been developed for the heart (ex utero) but these generally rely the constrained repetitive nature of cardiac motion. Measuring these parameters in fetal brain constitutes a special challenge because of the unpredictable nature of fetal motion. These parameters are also harder to measure than T2* as fitting the appropriate relaxometry models is likely to require combination of images with different contrasts acquired over multiple shots and controlling the spin physics is more complex. It is also critical that any methods developed have low RF power deposition, control risk of peripheral nerve stimulation in the mother and are time efficient as prolonged fetal examinations can be challenging for pregnant mothers, and increasingly the scope of these examinations is expanding as more comprehensive MRI methods are developed. The project will thus involve both research into novel sequences to achieve optimised acquisition strategies and also development of reconstruction methods that allow joint estimation of motion parameters and the desired relaxation parameters. Past reconstruction methods have been extremely computationally demanding and hence slow, so we propose to explore machine learning methods to shift the computational burden from examination time to a training phase allowing a much more clinically acceptable rapid image generation. This will build on our prior work on Deep Learning based reconstruction of cine cardiac images, which delivered state of the art performance, and has recently started to include motion correction as part of the reconstruction. We have also explored the application of Deep Leaning methods to SVR for anatomical imaging, so have a strong basis from which to build the current project. If robust quantitative T1 and T2 mapping can be achieved at high enough resolution, then it will become feasible to map oxygen extraction by exploiting the specific relaxation properties of haemaglobin. Recent results on fetal angiography provide encouraging evidence that it is feasible to resolve fetal vessels, although achieving this with quantitative methods will be a stretch target.

博士项目的目标：在怀孕的后半段，人脑经历了旺盛的生长，微观和宏观变化都发生得很快。因此，作为关键组织属性的MRI弛豫时间(T1和T2)实质上改变。这些松弛时间可以用来描述发育，特别是白质。松弛测量法是一种成熟的工具，用于评估健康和疾病中的大脑，然而，目前还没有在子宫内胎儿中进行这一评估的既定方法。该项目的目的是开发可靠的运动耐受方法来测量运动中胎儿的T1和T2，并利用这些方法对胎龄进行系统的定量研究，从而提供标准化的基准数据。项目描述/背景：我们团队在开发和部署用于宫内胎儿3D脑成像的完全运动校正方法方面有着丰富的记录。一种高效的策略是对单个或一小组切片进行快照成像，这些切片获得的速度足以冻结胎儿的运动，然后通过切片到体积重建(SVR)重新对齐以校正头部位置的变化1。过去的研究包括开发解剖、扩散和功能成像的综合方法。我们还能够通过将标准物理信号模型直接拟合到使用专用多回波方法获取的单次激发切片来测量弛豫时间T2*。这种方法可以将模型匹配与运动校正分开。测量运动组织中T1和T2的方法已经被开发出来用于心脏(体外)，但这些方法通常依赖于心脏运动的限制性重复性质。由于胎儿运动的不可预测性，在胎儿大脑中测量这些参数构成了一个特殊的挑战。这些参数也比T2*更难测量，因为适应适当的松弛测量模型可能需要结合在多个镜头中获取的具有不同对比度的图像，并且控制自旋物理更加复杂。同样重要的是，所开发的任何方法都必须具有低射频功率沉积，控制母亲周围神经刺激的风险，并具有时间效率，因为延长胎儿检查对孕妇来说可能是一种挑战，随着更全面的MRI方法的开发，这些检查的范围越来越大。因此，该项目将包括对新序列的研究，以实现优化的捕获策略，以及开发允许联合估计运动参数和所需松弛参数的重建方法。过去的重建方法对计算的要求非常高，因此速度很慢，因此我们建议探索机器学习方法，将计算负担从检查时间转移到训练阶段，从而允许更多临床可接受的快速图像生成。这将建立在我们之前的基于深度学习的电影心脏图像重建工作的基础上，该工作提供了最先进的性能，最近开始将运动校正作为重建的一部分。我们还探索了深度学习方法在用于解剖成像的SVR中的应用，从而为构建当前项目奠定了坚实的基础。如果能够在足够高的分辨率下实现可靠的定量T1和T2映射，那么通过利用血球蛋白的特定弛豫特性来绘制氧提取图将是可行的。最近的胎儿血管造影术结果提供了令人鼓舞的证据，表明分离胎儿血管是可行的，尽管用定量方法实现这一目标将是一个艰巨的目标。