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

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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.