Linked exploratory visualizations for uncertain MR spectroscopy data

Feng, David; Kwock, Lester; Lee, Yueh Z.; Taylor, Russell M.

doi:10.1117/12.839818

Cited by 6 publications

(6 citation statements)

References 26 publications

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“…Previous work visualizes uncertain multivariate data with PC plots [8]. This work generalizes and formalizes those ideas into a methodology based on KDEs that demonstrably applies to other information visualization techniques.…”

Section: Related Workmentioning

confidence: 99%

“…Researchers often address the challenge of multivariate 3D visualization by linking spatial views to abstract views. Tools such as Spotfire [1], tri-space visualization [2], Xmd-vTool [37], GGobi [33], and a previous MR spectroscopy visualization [8] demonstrate how to combine multiple views of the same data in a single interface. This work addresses the issue of how to incorporate statistical uncertainty into the abstract plots used by these tools.…”

Section: Related Workmentioning

confidence: 99%

“…Also, angular brushing is a subset of the more general linear function brushing, as the latter enables the user to select points using a wider set of linear functions [8]. Angular brushing is equivalent to selecting points within a distance threshold to a line in Cartesian space, which is to say within two bounding lines L 1 and L 2 around a drawn line L .…”

Section: Uncertain Plotsmentioning

confidence: 99%

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Matching Visual Saliency to Confidence in Plots of Uncertain Data

Feng

Kwock

Lee

et al. 2010

IEEE Trans. Visual. Comput. Graphics

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Conveying data uncertainty in visualizations is crucial for preventing viewers from drawing conclusions based on untrustworthy data points. This paper proposes a methodology for efficiently generating density plots of uncertain multivariate data sets that draws viewers to preattentively identify values of high certainty while not calling attention to uncertain values. We demonstrate how to augment scatter plots and parallel coordinates plots to incorporate statistically modeled uncertainty and show how to integrate them with existing multivariate analysis techniques, including outlier detection and interactive brushing. Computing high quality density plots can be expensive for large data sets, so we also describe a probabilistic plotting technique that summarizes the data without requiring explicit density plot computation. These techniques have been useful for identifying brain tumors in multivariate magnetic resonance spectroscopy data and we describe how to extend them to visualize ensemble data sets.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Uncertain Plotsmentioning

confidence: 99%

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Matching Visual Saliency to Confidence in Plots of Uncertain Data

Feng

Kwock

Lee

et al. 2010

IEEE Trans. Visual. Comput. Graphics

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“…Feng's work on visualization of MRSI data [21,22,23] provides both rendering and analysis of MRSI data and its uncertainty. Initially, by extending a 2D multivariate visualization technique, Feng used sphere sizes to map the expression level and colors to differentiate metabolites.…”

Section: Studies and Systems Involving Multi-modal Medical Datamentioning

confidence: 99%

An integrated visual analysis system for fusing MR spectroscopy and multi-modal radiology imaging

Nunes

Rowland

Schlachter

et al. 2014

2014 IEEE Conference on Visual Analytics Science and Technology (VAST)

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a) (b) (c) (d) Fig. 1:Analysing and visualizing MRSI data in our system. Red brush selecting the originally drawn CE region by a user in a histogram (a). Green brush selecting voxels with CNR ≥ 2 depicting high tumour intensity (b). Yellow convex hull brush gathering similar voxels in a scatter plot relating NAA/Creatine and Choline/Creatine ratios (c). Multi-modal rendering of Axial view of MR T1 Gado with manually thresholded CNR ≥ 2, FLAIR, CNR color map and yellow, red and green segmentations obtained from the respective brushes (d).Abstract-For cancers such as glioblastoma multiforme, there is an increasing interest in defining "biological target volumes" (BTV), high tumour-burden regions which may be targeted with dose boosts in radiotherapy. The definition of a BTV requires insight into tumour characteristics going beyond conventionally defined radiological abnormalities and anatomical features. Molecular and biochemical imaging techniques, like positron emission tomography, the use of Magnetic Resonance (MR) Imaging contrast agents or MR Spectroscopy deliver this information and support BTV delineation. MR Spectroscopy Imaging (MRSI) is the only non-invasive technique in this list. Studies with MRSI have shown that voxels with certain metabolic signatures are more susceptible to predict the site of relapse. Nevertheless, the discovery of complex relationships between a high number of different metabolites, anatomical, molecular and functional features is an ongoing topic of research -still lacking appropriate tools supporting a smooth workflow by providing data integration and fusion of MRSI data with other imaging modalities. We present a solution bridging this gap which gives fast and flexible access to all data at once. By integrating a customized visualization of the multi-modal and multi-variate image data with a highly flexible visual analytics (VA) framework, it is for the first time possible to interactively fuse, visualize and explore user defined metabolite relations derived from MRSI in combination with markers delivered by other imaging modalities. Real-world medical cases demonstrate the utility of our solution. By making MRSI data available both in a VA tool and in a multi-modal visualization renderer we can combine insights from each side to arrive at a superior BTV delineation. We also report feedback from domain experts indicating significant positive impact in how this work can improve the understanding of MRSI data and its integration into radiotherapy planning.

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“…For this reason multi-window linked-view systems are popular for addressing uncertainty [FKLTI10,HMH08, PWB*09,SZD*10]. …”

Section: Introductionmentioning

confidence: 99%