Three co-authors including two neuropsychologists and one cardiologist have tested our system with a sequence of datasets: four human brain datasets, two pig heart datasets, and one pig leg model. The DTI resolutions for the these datasets are 1.7 mm × 1.7 mm × 1.7 mm for the brain datasets, 1.17 mm × 1.17 mm × 2.4 mm for the pig hearts, and 0.938 mm × 0.938 mm × 6 mm for the pig leg. The fiber numbers for the datasets are 13,644, 12,121, 13,169, 3,520 (for the four brain datasets), 6,644, 2,717 (for the two pig heart datasets), and 6,097 (for the pig leg dataset) respectively. After a short practice with a user manual on the software, each user individually conducted several case studies. In general, the users were satisfied with the interactivity and performance of the user interface.
5.2 Dual Domain Clustering for a Pig Heart Model
Cardiac muscles are naturally grouped into layers and tracts . Visualization of these layers and tracts could be helpful for a cardiologist examining the localized effects of various heart diseases. However, the heart model consists of many spatially close muscle fibers, the grouping of which is more challenging than the pig leg muscle. Using the 2D embedding process alone can hardly get a reasonable result. Therefore, more user interactions are needed. From an in-depth examination of the 3D view, the orientations and shapes of the various fiber bundles are significantly different, as shown in Figure 9 (a). This inspection in conjunction with the anatomical knowledge of the cardiologist induced a feasible solution for clustering the fiber tracts.
The entire process was divided into three stages. At the beginning, a length-based filtering was used to remove very short fiber tracts, most of which were likely the result of noise. Then, the user carefully studied possible types of fiber bundle structures by performing coupled query and selection. The user found that the curvature-based measure (i.e., let γ be 1.0 in the weighted proximity measure presented in Table 1) is effective in distinguishing fiber tracts and yielded four types of fiber bundles. The user then manually labeled some fiber tracts in potential fiber bundles. Subsequently, the user-specified labels were propagated to all other fibers by leveraging a semi-supervised learning approach . Note that the mean distance  was used for the proximity measure at this stage Figure 9 shows the exploration process accomplished in 30 minutes. The analysis allowed for the tentative identification of a papillary muscle in one of the hearts examined. DTI analysis identified fiber tracts suggesting muscle fibers running from the apex toward the base of the heart along with the more abundant concentric muscle fibers.
Fig. 9. A dual domain clustering for (a) a pig heart model with 2,717 fibers; (b) Noisy data is removed by using the length filter; (c) Using the curvature filter, some fiber bundles are clearly formed; (d) The MDS with respect to (c); (e) Manual labeling on some potential fiber bundles, which can be used as the input for a semi-supervised clustering process; (f) 13 clusters were generated after refinement.
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5.4 Expert Evaluation
A preliminary user test was conducted to evaluate the capability of our interface for improving the efficiency of cerebral white matter tract selection. The goal was to identify well-known fiber tracts and test for the efficiency of tract selection and tract refinement (e.g., the ease with which erroneous fibers are identified on visual inspection and removed or the ease with which inadvertently removed fibers can be added back). This initial test focused on the basic manipulation tools (e.g., brush tool, single-fiber selection tool) and box tools with an emphasis on the utility of the novel 2D MDS. The evaluation was performed by two clinical researchers with considerable knowledge of white matter anatomy and experience with other tract selection platforms. The fiber model was generated from a healthy elderly subject who is part of a research study database. The research study was approved by the Institutional Review Board at Butler Hospital, Providence, RI, and the participant provided written informed consent.
Fiber tracts selected by the clinical researchers for interface evaluation included the corpus callosum, bilateral cingulate bundles, right superior longitudinal fasciculus, right uncinate fasciculus, and bilateral corticospinal tract (superior to the pyramidal decussation). They were selected because the users are familiar to them and they represent a combination of commissural, association, and projection fibers. Moreover, the users deliberately chose some tracts that are relatively easy to select because their fibers are generally oriented mainly in a single plane (e.g., anterior-posterior in the case of the cingulate bundles) and others with trajectories that pass through several planes (e.g., uncinate fasciculus). The users spent approximately 30 minutes gaining a familiarity with the software.
The specific tasks performed by the users to identify tracts varied by user and tract but the broad operations employed were similar across tracts, and involved initial selection, refinement, and classification. The users first inspected a fiber model interactively by rotating it and then decided on an optimal starting orientation for selecting a particular target tract. Then, specific tools were used depending on the trajectory of the tract and the user's preference. In general, the users found the brush tool to be very helpful for initial tract selection and removing unwanted fibers particularly when the unwanted fibers had a similar orientation. The box tool was helpful for making initial selections of curved tracts (e.g., uncinate fasciculus).
At first, the 2D MDS display seemed irrelevant to the process of tract selection. This initial impression was due to the lack of the awareness of the mapping of the spatial arrangement of the dots in 2D MDS space and the users' internal representation of brain anatomy. However, the users quickly learned the general mapping of the 2D MDS space onto the 3D anatomical space such that anterior regions were represented by dots in the upper part of the array whereas dots corresponding to posterior fibers were at the bottom of the display; dots near the midline of the 3D model were at the midline of the dot array and left and right were mapped intuitively.
With this insight, the utility of the 2D MDS panel emerged rather quickly. Large regions containing multiple fibers can be quickly selected for inclusion or removal. For example, removal of an entire hemisphere of fibers is particularly useful when selecting an association tract unilaterally (e.g., superior longitudinal fasciculus). The histogram controls (e.g., length) were useful for reducing clutter initially or after first approximating a selection. The MDS can be monitored while setting thresholds with histogram tools to avoid removal of fibers that might be obscured in the 3D space. In certain situations, it was easier to detect and select errant fibers in the MDS than in the 3D display. The MDS is particularly useful for identifying fibers that were captured by the brush that run in a direction that is orthogonal to that of the desired fibers or that may be hidden from view in the 3D space because they lie in the opposite hemisphere and are thus obscured by other fibers. Fibers selected on the basis of the MDS can be refined using the 3D manipulation tools to obtain an optimal representation of a desired track. Fibers that are grossly inaccurate typically appear as "outlier" dots on the MDS layout. These outliers can be easily and quickly selected in the MDS and removed from the model.
The MDS layout was less helpful when fine tuning a tract selection by removing or adding fibers that are similar in terms of their trajectory and curvature. In cases of similar curvature and trajectory, dots in the MDS panel appear very closely placed and are thus difficult to differentiate. In these situations, fine tuning is best done by judicious use of the brush and single fiber tools. The reader is referred to the accompanying video of the dual interface for additional understanding of how the various tools work.
Quantitative tests of intra- and inter-rater reliability were not conducted as part of this test. However, visual inspection of the selections indicated a high degree of agreement within and between raters.
The users found that many of the interactive tools were similar to those found in other software packages such as Brown University's in-house DTI software (BrainApp) ,  (e.g., box-based selection), CINCH ,  (e.g., brush-like tool), and MedINRIA (http://wwwsop.inria.fr/asclepios/) (e.g., box tools, FA thresholding). The 2D MDS display was clearly new. The users found the combination of many tools in a single package to be very convenient, and this added to the overall utility of the new interface. Surely, some tools or features that are part of the other software packages but not incorporated in the dual interface may be useful.
A more rigorous test of the interface would have involved having multiple raters select a pre-specified set of fibers, using multiple software platforms and multiple datasets. Ideally, the raters would be unfamiliar with the packages used in such a study to control for familiarity bias, and time to practice would be standardized across platforms. Moreover, the raters would be blind to which software was being evaluated. Dependent measures would be time to select each fiber bundle, accuracy, and confidence and ease-of-use ratings. In this preliminary user test, direct comparisons with other software packages were not performed as such a test would have necessarily been vulnerable to experimenter bias given the users were not blind to the "hypothesis" that the interface has advantages over other interfaces and due to their prior experience with multiple platforms.
Prior work in our lab, however, provides useful comparisons across several software platforms . In this prior study, four experienced raters (including one in this user study) with good knowledge of white matter anatomy were asked to identify the corpus callosum (in its entirety), the cingulum bundle (unilateral), the superior longitudinal fasciculus (unilateral), and the uncinate fasciculus (unilateral) using BrainApp, CINCH, and MedINRIA. Outcome measures were time to complete the selection and raters' confidence in the accuracy of their selections. The raters were given up to 30 minutes to familiarize themselves with the programs if needed. Average time to complete tract selection ranged from 3 minutes (superior longitudinal fasciculus using CINCH) to 6.25 minutes (cingulum bundle using BrainApp). For each tract, selection times were faster using CINCH than BrainApp and with selection times for MedINRIA being intermediate between the two. Confidence ratings were generally high.
In contrast, time to select these same bundles in our interface was not more than 3 minutes per fiber bundle for each rater with similarly high confidence in the accuracy of selection. The raters clearly preferred this tool to BrainApp, and the one rater who participated in the user study described above preferred this tool to all three of the tools used in that study, although the preference was not as great in comparison to CINCH.
In short, the users found that the software was quite useful for fiber selection and that time and ease in selecting all target tracts was greatly reduced and the results were superior in terms of precision-time tradeoffs when compared to other platforms with which they were familiar. The users agreed that although the 2D MDS display was initially not recognized to be of additional benefit, its utility was quickly appreciated. The 2D MDS display clearly provides a useful complement to the other tract selection tools. In certain situations, it provides a rapid method for identifying and removing unwanted fibers and a useful means of ensuring that wanted fibers are not inadvertently removed. The interface also appeared to hold promise for improving reliability in DTI fiber tract selection. That is, both raters were able to replicate their own and their counterparts' selections quickly and consistently.