The cohort size required in epidemiological imaging genetics studies often mandates the pooling of data from multiple hospitals. Patient data, however, is subject to strict privacy protection regimes, and physical data storage may be legally restricted to a hospital network. To enable biomarker discovery, fast data access and interactive data exploration must be combined with high-performance computing resources, while respecting privacy regulations. We present a system using fast and inherently secure light-paths to access distributed data, thereby obviating the need for a central data repository. A secure private cloud computing framework facilitates interactive, computationally intensive exploration of this geographically distributed, privacy sensitive data. As a proof of concept, MRI brain imaging data hosted at two remote sites were processed in response to a user command at a third site. The system was able to automatically start virtual machines, run a selected processing pipeline and write results to a user accessible database, while keeping data locally stored in the hospitals. Individual tasks took approximately 50% longer compared to a locally hosted blade server but the cloud infrastructure reduced the total elapsed time by a factor of 40 using 70 virtual machines in the cloud. We demonstrated that the combination light-path and private cloud is a viable means of building an analysis infrastructure for secure data analysis. The system requires further work in the areas of error handling, load balancing and secure support of multiple users.
Many computer aided diagnosis (CAD) schemes have been developed for colon cancer detection using Virtual
Colonoscopy (VC). In earlier work, we developed an automatic polyp detection method integrating flow visualization
techniques, that forms part of the CAD functionality of an existing Virtual Colonoscopy pipeline. Curvature
streamlines were used to characterize polyp surface shape. Features derived from curvature streamlines correlated
highly with true polyp detections. During testing with a large number of patient data sets, we found
that the correlation between streamline features and true polyps could be affected by noise and our streamline
generation technique. The seeding and spacing constraints and CT noise could lead to streamline fragmentation,
which reduced the discriminating power of our streamline features.
In this paper, we present two major improvements of our curvature streamline generation. First, we adapted
our streamline seeding strategy to the local surface properties and made the streamline generation faster. It
generates a significantly smaller number of seeds but still results in a comparable and suitable streamline distribution.
Second, based on our observation that longer streamlines are better surface shape descriptors, we
improved our streamline tracing algorithm to produce longer streamlines. Our improved techniques are more
effcient and also guide the streamline geometry to correspond better to colonic surface shape. These two adaptations
support a robust and high correlation between our streamline features and true positive detections and
lead to better polyp detection results.
This paper explores new methods to visualize and fuse multi-2D bioluminescence imaging (BLI) data with structural
imaging modalities such as micro CT and MR. A geometric, back-projection-based 3D reconstruction for superficial
lesions from multi-2D BLI data is presented, enabling a coarse estimate of the 3D source envelopes from the multi-2D
BLI data. Also, an intuitive 3D landmark selection is developed to enable fast BLI / CT registration. Three modes of
fused BLI / CT visualization were developed: slice visualization, carousel visualization and 3D surface visualization.
The added value of the fused visualization is demonstrated in three small-animal experiments, where the sensitivity of
BLI to detect cell clusters is combined with anatomical detail from micro-CT imaging.
The surgical removal of brain tumors can lead to functional impairment. Therefore it is crucial to minimize the damage to
important functional areas during surgery. These areas can be mapped before surgery by using functional MRI. However,
functional impairment is not only caused by damage to these areas themselves. It is also caused by damage to the fiber
bundles that connect these areas with the rest of the brain. Diffusion Tensor Images (DTI) can add information about
these connecting fiber bundles. In this paper we present interactive visualization techniques that combine DTI, fMRI and
structural MRI to assist the planning of brain tumor surgery. Using a fusion of these datasets, we can extract the fiber
bundles that pass through an offset region around the tumor, as can be seen in Figure 1. These bundles can then be explored
by filtering on distance to the tumor, or by selecting a specific functional area. This approach enables the surgeon to
combine all this information in a highly interactive environment in order to explore the pre-operative situation.
Direct volume rendering (DVR) is a very useful visualisation technique. However, the difficulty in specifying a suitable transfer function often discourages its application to visualisation problems. This work presents a technique for providing meaningful and fast visual feedback that greatly facilitates the transfer function specification process. The feedback is based on a slice-based preview of the actual volume rendering that can be calculated in real-time and can be superimposed on a slice-based view of the data that is being rendered.
For successful ball-joint replacement surgery, it is important to maintain the joint's geometric center. Pre-operative detection of this center is achieved by detecting the sphere that fits onto the articular surfaces in CT or MRI images. We have developed a novel technique to automatically determine the sub-voxel position and size of a sphere in unsegmented 3D images. The method is invariant to size and robust to noise. It only needs one fourth of a sphere to detect the center. Isotropically as well as anisotropically sampled images can be used. As no segmentation is required, it can be applied directly to clinical images.