A Progressive Approach for Uncertainty Visualization in Diffusion Tensor Imaging

dc.contributor.authorSiddiqui, Faizanen_US
dc.contributor.authorHöllt, Thomasen_US
dc.contributor.authorVilanova, Annaen_US
dc.contributor.editorBorgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonen_US
dc.date.accessioned2021-06-12T11:02:20Z
dc.date.available2021-06-12T11:02:20Z
dc.date.issued2021
dc.description.abstractDiffusion Tensor Imaging (DTI) is a non-invasive magnetic resonance imaging technique that, combined with fiber tracking algorithms, allows the characterization and visualization of white matter structures in the brain. The resulting fiber tracts are used, for example, in tumor surgery to evaluate the potential brain functional damage due to tumor resection. The DTI processing pipeline from image acquisition to the final visualization is rather complex generating undesirable uncertainties in the final results. Most DTI visualization techniques do not provide any information regarding the presence of uncertainty. When planning surgery, a fixed safety margin around the fiber tracts is often used; however, it cannot capture local variability and distribution of the uncertainty, thereby limiting the informed decision-making process. Stochastic techniques are a possibility to estimate uncertainty for the DTI pipeline. However, it has high computational and memory requirements that make it infeasible in a clinical setting. The delay in the visualization of the results adds hindrance to the workflow. We propose a progressive approach that relies on a combination of wild-bootstrapping and fiber tracking to be used within the progressive visual analytics paradigm. We present a local bootstrapping strategy, which reduces the computational and memory costs, and provides fibertracking results in a progressive manner. We have also implemented a progressive aggregation technique that computes the distances in the fiber ensemble during progressive bootstrap computations. We present experiments with different scenarios to highlight the benefits of using our progressive visual analytic pipeline in a clinical workflow along with a use case and analysis obtained by discussions with our collaborators.en_US
dc.description.number3
dc.description.sectionheadersBio-Medical Image Analysis
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume40
dc.identifier.doi10.1111/cgf.14317
dc.identifier.issn1467-8659
dc.identifier.pages411-422
dc.identifier.urihttps://doi.org/10.1111/cgf.14317
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14317
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectHuman centered computing
dc.subjectVisual analytics
dc.subjectScientific visualization
dc.titleA Progressive Approach for Uncertainty Visualization in Diffusion Tensor Imagingen_US
Files
Original bundle
Now showing 1 - 2 of 2
Loading...
Thumbnail Image
Name:
v40i3pp411-422.pdf
Size:
7.46 MB
Format:
Adobe Portable Document Format
Loading...
Thumbnail Image
Name:
cgf14317_v40i3pp411-422_cc-by.pdf
Size:
8.05 MB
Format:
Adobe Portable Document Format
Description:
CC-BY
Collections