Visualization Techniques of Time-Varying Volumetric Functional Neuroimaging Data: a Survey
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CS 524, March 2015 Visualization Techniques of Time-Varying Volumetric functional Neuroimaging Data: A Survey Kyle Reese Almryde Abstract Within the field of neuroimaging the use of time-varying data is both commonplace and a critical component towards the development of a critical medical diagnosis as well towards the advancement of our understanding of the human brain. Present visual analysis techniques revolve around two-dimensional (2D) slice representations which though useful in their own right limits access to the spatio-temporal features inherent in these datasets. This work assess the current state of the art with regards to time-varying volume visualizations with a discussion on some of the most common tools used to visualize time-varying medical images as well as providing a discussion on current challenges and techniques when dealing with these complex types of data. Categories and Subject Descriptors (according to ACM CCS): I.3.3 [Computer Graphics]: Time-varying— Volumetric Neuroimaging data 1. Introduction often time-consuming when attempting to compare different images of the same patient slice-by-slice, or when attempt- The human brain is an incredibly complex and mysterious ing to examine changes within a functional network across organ which has fascinated the human race for ages [BG11]. a collection of datasets, such as those involved in language The study of the human brain shares an equally long and learning or the progression of neuro-degenerative diseases. complex history, experiencing constant changes in terms of our understanding of how this incredible structure works The aim of this work is to provide a survey of visualiza- [Rai09]. While there are many aspects to the study of the tion tools and techniques used in the representation of Multi- human brain, perhaps the most exciting, or at least relevant temporal and time-varying volumetric data, with a special within the modern age of Information, is the field of Neu- emphasis on current and future applications within the do- roimaging. Within the study of the human brain, neuroimag- main of neuroimaging. The contributions of this paper in- ing has proven to be an incredibly valuable contributor, both clude a high level overview of the domain that is neuroimag- in terms of its ability to elucidate complex cognitive func- ing as it relates to the types of data used and their role within tions within live brains, but also as a mean in which to see temporally focused research such as imaging the neural cor- the brain [VHGRG04]. relates of language learning networks. This paper also of- The use of time-varying data within the field of neu- fers a discussion of the most common tasks associated with roimaging is both commonplace and a critical component this domain in addition to a high level overview of the State towards the development of a critical medical diagnosis for of the art when it comes to visualization methods and tech- a patient or to one’s hypothesis when investigating neural niques currently used by visualization and neuroimaging re- functions such as language processing or visual-cognition searchers. Finally, supplementary material has been included [GWL∗08,VHGRG04]. Thanks to advances in modern med- from qualitative interviews with domain experts for the read- ical imaging, images can be collected faster while still main- ers reference. taining high spatial resolutions which has opened the door towards the acquisition of larger, more complex multi-modal 2. Domain data [EWS14]. With new technology comes new problems, particularly as data grows in terms of size and complex- Advances in modern medical imaging have paved the way ity [EWS14,VHGRG04]. Present visual analysis techniques for numerous techniques, such as Magnetic Resonance revolve around two-dimensional (2D) slice representations Imaging (MRI) and Electroencephalography (EEG), which which, though useful in their own right, can be limiting and have opened the door towards acquiring complex multi- K. Almryde / Volume rendering neuroimaging data modal data. One technique, known as functional Magnetic alization in general as proper visual encoding will invariably Resonance Imaging (fMRI), has enabled neuroimaging re- assist researchers and domain experts towards to the pursuit searchers and clinicians to detect metabolic changes within of understanding their data. The most common tasks include: the cerebral tissue. This effect is known as the Blood- Exploration, Comparison, and Identification Oxygen-Level-Dependent (BOLD) signal and is the result of an increase in oxygen delivery to active cerebral tissue, which in turn results in changes to the local magnetic field. 3.1. Exploration [SRWE07] This provides for high spatial resolution of active One of the most important tasks for a neuroimaging re- brain regions which, when combined with an anatomical ref- searcher is to simply explore data. By visually examining erence such as MRI data enables researchers to glean impor- ones dataset, the researcher can gain insight into the struc- tant insights into the complex operations of human cognitive ture of the image, its quality, and often some insight into the processes, such as learning. However, fMRI data is time- results. The ability to simply explore the data in order to sup- dependent and highly complex, requiring immense prepro- port sense making is a fundamental task in Neuroimaging. cessing in order to extract viable signal from the data, and Though typically, researchers have a well defined assump- dozen of volumes must be collected in order to capture and tion about the contents of their data. For example, a com- model this signal in relation to brain function. mon exploration task involves visually checking for "oddi- In typical fMRI visualization applications, multiple vol- ties" and "inconsistencies" potentially residing in the dataset. umes are rendered individually or in conjunction with one These "oddities" can be related to subject motion, which is another in an overlaying fashion. This is acceptable if the the result of patients moving during a scan, or they could image(s) in question are static, or represent the averages of be scanner related artifacts such as a sudden drop in signal ∗ multiple sessions. However, studies investigating the pro- in a region of the brain [COA 12]. Dedicated neuroimaging gressive degeneration of neural tissue and function, such as analysis tools, like AFNI [Cox96] and SPM [spm14] have corticobasal syndrome and Alzheimer’s disease, require sev- built-in components which enable a user to visually exam- eral sets of images describing the diseases progression over ine their data at each point along the data processing and time. More recently, work has begun to examine how learn- analysis pipeline. Furthermore, visually assessing the spatial ing networks evolve over time, such as through the course layout of clusters of activation can provide researcher with of learning a novel language. In these cases, several series of an accurate sense of the distribution of neural resources the images are generated which show the progressive evolution brain employs when solving a task, or, as in the case of dis- of neural networks through time. Through the use of advance ordered brains, where potential sources of problems may be statistical analyses and data reduction techniques, additional occurring. dimensions become accessible, but which require more ad- vanced visualization techniques in order to effectively repre- 3.2. Comparison sent their meaning and implications. In these cases, existing tools can be cumbersome and limit the user’s ability to ex- Frequently, researchers need to compare one dataset to an- plore and analyze the dynamic aspects of their data. other, for example when comparing a "typically developing" brain’s data to a developmentally impaired brain, or compar- It should be mentioned that the terms temporal and time- ing the results of two experimental conditions against one varying data bear wide interpretations depending on the do- another. Recently, Plante et. al, [PPD∗14] explored the neu- main and even the data in question. For the purpose of this ral correlates of novel language learning between a group survey, when referring to temporal and time-varying in the of controls and experimental subjects. They found that con- context of neuroimaging and fMRI data, we mean a tem- trol subjects recruited fewer regions of the brain when asked poral within data resolution on the order of seconds. That to listen to ’shallow’ stimuli. That is, they were presented is, a single fMRI data volume typically consists of 100 or with a fake language that for all intents and purpose sounded more whole brain snapshots. Images are collected sequen- like natural language, but which had no meaningful informa- tially with a latency between 1000ms up to 5000ms. For tion contained within. By contrast, the experimental group data collected using systems like EEG, this latency is fre- showed numerous regions of the brain recruited when pre- quently on the order of nanoseconds. Outside the discussion sented with real Norwegian sentences. In this instance, sim- of fMRI and neuroimaging related data collection methods, ply being able to see on a whole brain scale, the topological the author will provide clarification as needed. differences between the two images can be incredibly valu- able towards developing the next steps in a high-level analy- 3. Domain Specific Tasks sis. By the very nature of the data collected, visual tasks are Beyond analysis procedures comparison tasks are em- an intrinsic component of the domain’s research. A cardi- ployed during the preprocessing stages of the analysis as a nal rule within the domain of neuroimaging, is to Always means of checking that the data is visually conforming to inspect your data visually. This rule lends itself well to visu- your expectations in terms of those data processing steps. K. Almryde / Volume rendering neuroimaging data For example, a common step in processing brain imaging [BRGIG∗14].