Laura Garrison

PhD Student

I’m a PhD student working in the Visual Data Science for Large Scale Hypothesis Management in Imaging Biomarker Discovery (VIDI) project. This is a joint project between UiB and the Mohn Medical Imaging and Visualization (MMIV) centre, and is funded by UiB and the Trond Mohn Foundation.

My research focuses on the development of novel visual encoding and interaction techniques for multivariate heterogenous medical data, drawing from my background as a medical illustrator with expertise in anatomy and physiology.

Publications

2019

[Bibtex]
@inproceedings {bm.20191236,
booktitle = {Eurographics Workshop on Visual Computing for Biology and Medicine},
editor = {Kozlíková, Barbora and Linsen, Lars and Vázquez, Pere-Pau and Lawonn, Kai and Raidou, Renata Georgia},
title = {{MedUse: A Visual Analysis Tool for Medication Use Data in the ABCD Study}},
author = {Bartsch, Hauke and Garrison, Laura and Bruckner, Stefan and Wang, Ariel and Tapert, Susan F. and Grüner, Renate},
abstract = {The RxNorm vocabulary is a yearly-published biomedical resource providing normalized names for medications. It is used to capture medication use in the Adolescent Brain Cognitive Development (ABCD) study, an active and publicly available longitudinal research study following 11,800 children over 10 years. In this work, we present medUse, a visual tool allowing researchers to explore and analyze the relationship of drug category to cognitive or imaging derived measures using ABCD study data. Our tool provides position-based context for tree traversal and selection granularity of both study participants and drug category. Developed as part of the Data Exploration and Analysis Portal (DEAP), medUse is available to more than 600 ABCD researchers world-wide. By integrating medUse into an actively used research product we are able to reach a wide audience and increase the practical relevance of visualization for the biomedical field.},
year = {2019},
publisher = {The Eurographics Association},
ISSN = {2070-5786},
ISBN = {978-3-03868-081-9},
DOI = {10.2312/vcbm.20191236},
project = {VIDI}
}
[Bibtex]
@INPROCEEDINGS {Garrison2019SM,
author = {Garrison, Laura and Va\v{s}\'{\i}\v{c}ek, Jakub and Gr\"{u}ner, Renate and Smit, Noeska and Bruckner, Stefan},
title = {SpectraMosaic: An Exploratory Tool for the Interactive Visual Analysis of Magnetic Resonance Spectroscopy Data},
journal = {Computer Graphics Forum},
month = {sep},
year = {2019},
booktitle = {Proceedings of 9th EG Workshop on Visual Computing for Biology and Medicine (VCBM)},
event = "VCBM 2019",
proceedings = "Proceedings of the 9th Eurographics Workshop on Visual Computing in Biology and Medicine",
keywords = {medical visualization, magnetic resonance spectroscopy data, information visualization, user-centered design},
images = "images/garrison_VCBM19spectramosaic_full.PNG",
thumbnails = "images/garrison_VCBM19spectramosaic_thumb.png",
pdf = "pdfs/garrison_VCBM19spectramosaic.pdf",
abstract = {Magnetic resonance spectroscopy (MRS) allows for assessment of tissue metabolite characteristics used often for early detection and treatment evaluation of brain-related pathologies. 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 design study, we collaborated with domain experts to design a novel visualization tool for the exploration of tissue metabolite concentration ratios in spectroscopy clinical and research studies. We present a data and task analysis for this domain, where MRS data attributes can be categorized into tiers of visual priority. We furthermore introduce a novel set of visual encodings for these attributes. Our result is SpectraMosaic (see Figure~\ref{fig:teaser}), an interactive insight-generation tool for rapid exploration and comparison of metabolite ratios. We validate our approach with two case studies from MR spectroscopy experts, providing early qualitative evidence of the efficacy of the system for visualization of spectral data and affording deeper insights into these complex heterogeneous data.},
git = "https://git.app.uib.no/Laura.Garrison/spectramosaic",
project = "VIDI"
}
[Bibtex]
@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",
}