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A Visual Encoding System for Comparative Exploration of Magnetic Resonance Spectroscopy Data

L. Garrison, J. Vašíček, R. Grüner, N. Smit, and S. Bruckner

Abstract

Magnetic resonance spectroscopy (MRS) allows for assessment of tissue metabolite characteristics used often for early detection and treatment evaluation of intracranial pathologies. In particular, this non-invasive technique is important in the study of metabolic changes related to brain tumors, strokes, seizure disorders, Alzheimer's disease, depression, as well as other diseases and disorders affecting the brain. However, meaningful variations in ratios of tissue metabolites within a sample area are difficult to capture with current visualization tools. Furthermore, the learning curve to interpretation is steep and limits the more widespread adoption of MRS in clinical practice. In this work we present a novel, tiered visual encoding system for multi-dimensional MRS data to aid in the visual exploration of metabolite concentration ratios. Our system was developed in close collaboration with domain experts including detailed data and task analyses. This visual encoding system was subsequently realized as part of an interactive insight-generation tool for rapid exploration and comparison of metabolite ratio variation for deeper insights to these complex data.

L. Garrison, J. Vašíček, R. Grüner, N. Smit, and S. Bruckner, A Visual Encoding System for Comparative Exploration of Magnetic Resonance Spectroscopy Data, 2019.
[BibTeX]

Magnetic resonance spectroscopy (MRS) allows for assessment of tissue metabolite characteristics used often for early detection and treatment evaluation of intracranial pathologies. In particular, this non-invasive technique is important in the study of metabolic changes related to brain tumors, strokes, seizure disorders, Alzheimer's disease, depression, as well as other diseases and disorders affecting the brain. However, meaningful variations in ratios of tissue metabolites within a sample area are difficult to capture with current visualization tools. Furthermore, the learning curve to interpretation is steep and limits the more widespread adoption of MRS in clinical practice. In this work we present a novel, tiered visual encoding system for multi-dimensional MRS data to aid in the visual exploration of metabolite concentration ratios. Our system was developed in close collaboration with domain experts including detailed data and task analyses. This visual encoding system was subsequently realized as part of an interactive insight-generation tool for rapid exploration and comparison of metabolite ratio variation for deeper insights to these complex data.
@MISC {Garrison2019SM_eurovis,
title = {A Visual Encoding System for Comparative Exploration of Magnetic Resonance Spectroscopy Data},
author = {Garrison, Laura and Va\v{s}\'{\i}\v{c}ek, Jakub and Gr\"{u}ner, Renate and Smit, Noeska and Bruckner, Stefan},
abstract = "Magnetic resonance spectroscopy (MRS) allows for assessment of tissue metabolite characteristics used often for early detection and treatment evaluation of intracranial pathologies. In particular, this non-invasive technique is important in the study of metabolic changes related to brain tumors, strokes, seizure disorders, Alzheimer's disease, depression, as well as other diseases and disorders affecting the brain. However, meaningful variations in ratios of tissue metabolites within a sample area are difficult to capture with current visualization tools. Furthermore, the learning curve to interpretation is steep and limits the more widespread adoption of MRS in clinical practice. In this work we present a novel, tiered visual encoding system for multi-dimensional MRS data to aid in the visual exploration of metabolite concentration ratios. Our system was developed in close collaboration with domain experts including detailed data and task analyses. This visual encoding system was subsequently realized as part of an interactive insight-generation tool for rapid exploration and comparison of metabolite ratio variation for deeper insights to these complex data.",
booktitle = {Proceedings of the EuroVis Conference - Posters (EuroVis ’19)},
year = {2019},
howpublished = "Poster presented at the EuroVis conference 2019",
keywords = {medical visualization, magnetic resonance spectroscopy data, information visualization, user-centered design},
images = "images/garrison_eurovis2019_SM_encodings.png",
thumbnails = "images/garrison_eurovis2019_SM_encodings.png",
pdf = "pdfs/garrison_eurovis2019_SM.pdf",
youtube = "https://youtu.be/Rzl7sl4WvdQ",
project = "VIDI"
}
projectidVIDIprojectid

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