Sherin Sugathan

• Melkonyan, Dzhema & Sugathan, Sherin (2024). Spatiotemporal Variation and Long-Range Correlation of Groundwater Levels in Odessa, Ukraine. Water. 16(1). doi: 10.3390/w16010147. Fulltekst i vitenarkiv
• Sugathan, Sherin; Bartsch, Hauke; Riemer, Frank; Gr¨¹ner, Eli Renate; Lawonn, Kai & Smit, Noeska Natasja (2022). Longitudinal visualization for exploratory analysis of multiple sclerosis lesions. Computers & graphics. ISSN 0097-8493. 107, s. 208¨C219. doi: 10.1016/j.cag.2022.07.023. Vis sammendrag

  In multiple sclerosis (MS), the amount of brain damage, anatomical location, shape, and changes are important aspects that help medical researchers and clinicians to understand the temporal patterns of the disease. Interactive visualization for longitudinal MS data can support studies aimed at exploratory analysis of lesion and healthy tissue topology. Existing visualizations in this context comprise bar charts and summary measures, such as absolute numbers and volumes to summarize lesion trajectories over time, as well as summary measures such as volume changes. These techniques can work well for datasets having dual time point comparisons. For frequent follow-up scans, understanding patterns from multimodal data is difficult without suitable visualization approaches. As a solution, we propose a visualization application, wherein we present lesion exploration tools through interactive visualizations that are suitable for large time-series data. In addition to various volumetric and temporal exploration facilities, we include an interactive stacked area graph with other integrated features that enable comparison of lesion features, such as intensity or volume change. We derive the input data for the longitudinal visualizations from automated lesion tracking. For cases with a larger number of follow-ups, our visualization design can provide useful summary information while allowing medical researchers and clinicians to study features at lower granularities. We demonstrate the utility of our visualization on simulated datasets through an evaluation with domain experts.

• Sugathan, Sherin; Bartsch, Hauke; Riemer, Frank; Gr¨¹ner, Eli Renate; Lawonn, Kai & Smit, Noeska Natasja (2021). Interactive Multimodal Imaging Visualization for Multiple Sclerosis Lesion Analysis. Eurographics Workshop on Visual Computing for Biomedicine. ISSN 2070-5778. 2021. doi: 10.2312/vcbm.20211346. Vis sammendrag

  Multiple Sclerosis (MS) is a brain disease that is diagnosed and monitored extensively through MRI scans. One of the criteria is the appearance of so-called brain lesions. The lesions show up on MRI scans as regions with elevated or reduced contrast compared to the surrounding healthy tissue. Understanding the complex interplay of contrast, location and shape in images from multiple modalities from 2D MRI slices is challenging. Advanced visualization of appearance- and location-related features of lesions would help researchers in defining better disease characterization through MS research. Since a permanent cure is not possible in MS and medication-based disease modification is a common treatment path, providing better visualizations would strengthen research which investigates the effect of white matter lesions. Here we present an advanced visualization solution that supports analysis from multiple imaging modalities acquired in a clinical routine examination. The solution holds potential for enabling researchers to have a more intuitive perception of lesion features. As an example for enhancing the analytic possibilities, we demonstrate the benefits of lesion projection using both Diffusion Tensor Imaging (DTI) and gradient-based techniques. This approach enables users to assess brain structures across individuals as the atlas-based analysis provides 3D anchoring and labeling of regions across a series of brain scans from the same participant and across different participants. The projections on the brain surface also enable researchers to conduct detailed studies on the relationship between cognitive disabilities and location of lesions. This allows researchers to correlate lesions to Brodmann areas and related brain functions. We realize the solutions in a prototype application that supports both DTI and structural data. A qualitative evaluation demonstrates that our approach supports MS researchers by providing new opportunities for MS research.

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• Sugathan, Sherin & Razick, Sabry (2024). HPC Visualization using ParaView.

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