19. Advances in Statistical Methods for Multiple Sclerosis Imaging
The video recording of this talk can be viewed here.
Organizer: Elizabeth Sweeney
Weill Cornell
Email: ems4003@med.cornell.edu
Chair: Elizabeth Sweeney
Weill Cornell
Email: ems4003@med.cornell.edu
Speakers:
1. Russell Shinohara
University of Pennsylvania
Email: rshi@mail.med.upenn.edu
Title: A statistical revisiting of multiple sclerosis lesions based on MRI
Time: 2:00pm-2:20pm
Abstract:
Lesions in the white matter of the brain, including those that arise in multiple sclerosis, are abnormalities measurable on MRI. While much literature has focused on the identification of these lesions, less work has focused on the nature of these lesions. As new imaging modalities arise that allow us to better interrogate these lesions, new statistical modeling problems that include spatial constraints and overlapping domains of analysis are increasingly important. Leveraging multi-modal imaging approaches that focus on knowledge about etiology is critical for developing the next generation of robust and generalizable imaging biomarkers.
2. Julia Wrobel
University of Colorado, Denver
Email: julia.wrobel@cuanschutz.edu
Title: Intensity warping for multisite MRI harmonization
Time: 2:20pm-2:40pm
Abstract:
In multisite neuroimaging studies there is often unwanted technical variation across scanners and sites. These scanner effects can hinder detection of biological features of interest, produce inconsistent results, and lead to spurious associations. We assess scanner effects in two brain magnetic resonance imaging (MRI) studies where subjects were measured on multiple scanners within a short time frame, so that one could assume any differences between images were due to technical rather than biological effects. We propose mica (multisite image harmonization by CDF alignment), a tool to harmonize images taken on different scanners by identifying and removing within-subject scanner effects. Our goals in the present study were to (1) establish a method that removes scanner effects by leveraging multiple scans collected on the same subject, and, building on this, (2) develop a technique to quantify scanner effects in large multisite trials so these can be reduced as a preprocessing step. We found that unharmonized images were highly variable across site and scanner type, and our method effectively removed this variability by warping intensity distributions. We further studied the ability to predict intensity harmonization results for a scan taken on an existing subject at a new site using cross-validation.
3. Carolyn Lou
University of Pennsylvania
Email: louc@pennmedicine.upenn.edu
Title: Towards an Automatic Detection Method of Chronic Active Lesions in Multiple Sclerosis
Time: 2:40pm-3:00pm
Abstract:
Recent developments in magnetic resonance imaging (MRI) have shown that chronic active multiple sclerosis lesions can be assessed in vivo by a hypointense rim of iron deposits around the border of a white matter lesion. These lesions are typically characterized by a dark rim indicating increased iron-laden microglia/macrophages at their edge, and their presence is associated with worse disease outcomes. In this project, we present some candidate methods for the automatic detection of these rims on 3T-weighted MRI images, specifically on T2*-phase images, a byproduct of the T2* imaging sequence. The first method extracts radiomic features from the phase images and aims to predict rim presence on the lesion level. The second quantifies the covariance structure of multi-modal images via inter-modal coupling analysis, and the third method aims to identify these rims with intensity gradients, both of which aim to predict rim presence on the voxel level.