Connecting MRI physics and artificial intelligence to advance novel acquisition and analyses technologies for neuroimaging applications

有越来越多的MRI方法可以评估器官的结构和生理状态。然而，他们的应用程序的关键区域，如脊髓或心脏，是强烈阻碍了由运动引起的图像伪影。能够在MRI采集期间准确地估计和补偿这些生理变化将是去除这些伪影的有效手段，这些伪影目前限制了临床解释。 人工智能领域近年来蓬勃发展。深度学习（DL）尤其如此，它在图像分析任务（如解剖和病理特征分割）方面表现出前所未有的性能。将在目标1（O1）“利用深度学习来推进MR采集”中探索的一个想法是开发可以学习驱动图像伪影的生理模式的DL模型，并将这些模型纳入MRI采集链中，以便可以实时补偿生理变化。 除了在获取定量MRI数据方面的挑战之外，这种类型的数据需要复杂的分析管道，这些分析管道通常是手动执行的，因此具有较差的再现性。同样，DL似乎是帮助自动化某些分析任务的理想候选者。然而，尽管每年都有数十篇关于DL应用于医学成像的论文发表，但大多数方法都已在单中心数据集中得到验证，并且在应用于其他中心时通常会失败。这是因为不同中心的图像与用于训练算法的图像（对比度，分辨率等）略有不同。在O2“利用MRI物理学来推进医学成像中的深度学习应用”中，我们将探索新的DL架构，该架构可以通过在训练期间输入采集参数来学习图像对比度机制的起源。这些“更好地了解”的DL模型有望在多个中心更好地执行和推广。 在O3“转化研究：测试，验证，实施和沟通”中，我们将开发，培训和验证特定于医学分析任务的DL模型（例如，MS病变的分割）。我们将把它们作为开源云计算平台（用于研究）和专有PACS系统（用于临床）来实施和分发。培训和知识传播将发挥重要作用。因此，该研究计划的总体目的是开发MRI采集和分析的创新方法，将人工智能作为手段和目的，并在整个研究中心和医院传播这些方法。该项目将为目前尚不可行的雄心勃勃的定量MRI应用打开大门。鉴于神经影像中心的指数级发展，我预计我的计划将培养出优秀的HQP，这将有助于加拿大在世界医学影像领域的进步。

There is a growing number of MRI methods that can assess the structural and physiological state of organs. However, their application to critical regions, such as the spinal cord or the heart, is strongly hampered by image artifacts caused by motion. Being able to accurately estimate and compensate those physiological variations during the MRI acquisition would be an effective means to remove those artifacts, which currently limit clinical interpretation. The field of artificial intelligence has flourished in recent years. This is particularly true for deep learning (DL), which shows unprecedented performance for image analysis tasks, such as segmentation of anatomical and pathological features. One idea, which will be explored in Objective 1 (O1) "Leverage deep learning to advance MR acquisition", is to develop DL models that can learn the physiological patterns that drive the image artifacts, and incorporate these models within the MRI acquisition chain such that physiological variations could be compensated in real-time. In addition to the challenges in acquiring quantitative MRI data, this type of data requires complex analysis pipelines that are often executed manually and hence suffer from poor reproducibility. Again, DL appears to be an ideal candidate to help automatize certain analysis tasks. However, while dozens of papers on DL applied to medical imaging are published every year, most methods have been validated in single-center datasets and usually fail when applied to other centers. This happens because images across different centers have slightly different features than those used to train the algorithm (contrast, resolution, etc.). In O2 "Leverage MRI physics to advance deep learning applications in medical imaging", we will explore novel DL architectures that could learn the origin of image contrast mechanisms by inputting acquisition parameters during training. These "better informed" DL models are expected to perform and generalize better across multiple centers. In O3 "Translational research: Test, Validate, Implement and Communicate", we will develop, train and validate DL models specific to medical analysis tasks (e.g., segmentation of MS lesions). We will implement and distribute them as open-source cloud computing platforms (for research use) and into proprietary PACS systems (for clinical use). Training and knowledge dissemination will play a big role. The overarching purpose of this research program is thus to develop innovative methods for MRI acquisition and analysis, by exploiting AI both as a means and an end, and disseminating those methods throughout research centers and hospitals. This project will open the door to ambitious quantitative MRI applications that are currently not feasible. Given the exponential development of neuroimaging centers, I anticipate that my program will train outstanding HQP that will contribute to the advancement of Canada on the world medical imaging scene.