Opportunities for Understanding MS Mechanisms and Progression With MRI Using Large-Scale Data Sharing and Artificial Intelligence

Multiple sclerosis (MS) patients have heterogeneous clinical presentations, symptoms and progression over time, making MS difficult to assess and comprehend in vivo. The combination of large-scale data-sharing and artificial intelligence creates new opportunities for monitoring and understanding MS using magnetic resonance imaging (MRI).First, development of validated MS-specific image analysis methods can be boosted by verified reference, test and benchmark imaging data. Using detailed expert annotations, artificial intelligence algorithms can be trained on such MS-specific data. Second, understanding disease processes could be greatly advanced through shared data of large MS cohorts with clinical, demographic and treatment information. Relevant patterns in such data that may be imperceptible to a human observer could be detected through artificial intelligence techniques. This applies from image analysis (lesions, atrophy or functional network changes) to large multi-domain datasets (imaging, cognition, clinical disability, genetics, etc.).After reviewing data-sharing and artificial intelligence, this paper highlights three areas that offer strong opportunities for making advances in the next few years: crowdsourcing, personal data protection, and organized analysis challenges. Difficulties as well as specific recommendations to overcome them are discussed, in order to best leverage data sharing and artificial intelligence to improve image analysis, imaging and the understanding of MS

Synthesis-Based Harmonization of Multi-Contrast Structural MRI

Magnetic resonance imaging (MRI) is a flexible, noninvasive medical imaging modality that uses magnetic fields and radiofrequency (RF) pulses to produce images. MRI is especially useful in diagnosing and monitoring disorders of the central nervous system such as multiple sclerosis (MS). The flexible design of the MRI system allows for the collection of multiple images with different acquisition parameters in a single scanning session. This flexibility also poses challenges when pooling data collected on multiple scanners or at different sites. Since MRI does not have consistent standards that regulate image acquisition, differences in acquisition lead to variability in images that can cause problems in analysis. This problem sets the stage for harmonization. This dissertation describes developments in harmonization strategies for structural MRI of the brain. These strategies allow us to create similar harmonized images from varying source images. Harmonized images can then be used in automated analysis pipelines where image variability can cause inconsistent results. In this work, we make a number of contributions to research in harmonization of MRI. In our first contribution, we acquired a cross-domain dataset to provide training and validation data for our harmonization methods. These data were acquired on two different scanners with different acquisition protocols in a short time frame, providing examples of the same subjects under two different acquisition environments. Since the imaged object is the same between the two, this can be used as training and validation data in harmonization experiments. In our second contribution, we used this dataset to develop a supervised method of harmonization, called DeepHarmony, which uses state-of-the-art deep learning architecture and training strategies to provide improved image harmonization. This method provides a baseline for image harmonization, but the requirement for cross-domain training data is a major limitation. In our third contribution, we proposed an unsupervised harmonization framework. We used multi-contrast MRI images from the same scanning session to encourage a disentangled latent representation and we demonstrated that this regularization was able to generate disentanglement and allow for harmonization. In our final contribution, we extended our unsupervised work for a more diverse clinical trial dataset, which included T2-FLAIR and PD-weighted images. In this more complex dataset, we included a new adversarial loss to encourage consistency in the anatomy space and a distribution loss to impose a well-defined distribution on the acquisition space