Dissertation Defense: Blake Dewey
Note: This is a virtual presentation. Here is the link for where the presentation will be taking place.
Title: Synthesis-Based Harmonization of Multi-Contrast Structural MRI
Abstract: The flexible design of the MRI system allows for the collection of multiple images with different acquisition parameters in a single scanning session. However, since MRI does not have any standards that regulate image acquisition (unlike other imaging modalities, such as computed tomography), differences in acquisition lead to variability in image appearance between manufacturers, imaging centers, and even individual scanners. Variability in images can cause significant problems in quality of analysis, setting the stage for harmonization.
This dissertation describes four main contributions to literature of synthesis-based harmonization for structural brain MR images. In synthesis-based harmonization, harmonized images are created that can be used confidently in automated analysis pipelines such as whole-brain segmentation, where image variability can cause inconsistent results. In our first contribution, we acquired a cross-domain dataset to provide training and validation data for our harmonization methods. This dataset is crucial to our work, as it provides examples of the same subjects under two different acquisition environments. In our second contribution, we used this unique, cross-domain dataset directly to develop a supervised method of harmonization. Our method, called DeepHarmony, uses state-of-the-art deep learning architecture and training strategies to provide significantly improved image harmonization over other synthesis methods. In our third contribution, we proposed an unsupervised harmonization framework to allow for broader applications where cross-domain data is not acquired. This novel framework is based on representation learning, where we aim to separate anatomical features from acquisition environment in a disentangled latent space. We used multi-contrast MRI images from the same scanning session as internal supervision to encourage this disentangled latent representation and we demonstrated that this regularization alone was able to generate disentanglement in a completely data-driven way. 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 substantially more complex dataset, we made improvements to the disentanglement architecture and training strategies to produce a more consistent latent space. This method was shown to properly enforce the expectations on our latent space and also has the ability to evaluate images for inconsistent acquisition.
- Jerry Prince, Department of Electrical and Computer Engineering
- Vishal Patel, Department of Electrical and Computer Engineering
- Webster Stayman, Department of Biomedical Engineering
- Peter van Zijl, Department of Radiology
- Peter Calabresi, Department of Neurology