Faculty Bibliography

OBJECTIVE: Arteriovenous malformations (AVMs) are difficult lesions to treat, partly because it is difficult to formulate a three-dimensional mental image of the nidus and its supplying arteries, draining veins, and arteries of passage. Our purpose is to develop personal computer software that allows better visualization of complex, three-dimensional, connected vascular anatomy for surgical planning. METHODS: Vessels are defined from magnetic resonance angiograms and are symbolically linked to form vascular trees. The nidus of the AVM is also defined by magnetic resonance angiography. These representations of the nidus and vasculature can be viewed together in a software program that allows the user to color-code groups of vessels or to selectively turn connected groups of vessels "off" to avoid obscuring the part of the image that the user wants to observe. Structures can be viewed from any angle. The vessels can also be shown intersecting any magnetic resonance angiogram slice or superimposed upon digital subtraction angiograms obtained from the same patient. RESULTS: We report results from two patients with AVMs in which our representations were compared with the findings during surgery. Our three-dimensional vascular trees correctly depicted the relationship of the nidus to feeding vessels in three dimensions. We show findings in an additional, unoperated patient for whom vessel trees were created from three-dimensional digital subtraction angiography data and compared with a volume rendering of the original data set. CONCLUSION: Computer-assisted, three-dimensional visualizations of complex vascular anatomy can be helpful in planning the surgical excision of AVMs. Software programs that produce these images can provide important information that is difficult to obtain by traditional techniques. This imaging method is also applicable to guidance of endovascular procedures and removal of complex tumors.

This paper presents a novel processing scheme for the automatic computation of a medial shape model which is representative for an object population with shape variability. The sensitivity of medial descriptions to object variations and small boundary perturbations are fundamental problems of any skeletonization technique. These problems are approached with the computation of a model with common medial branching topology and grid sampling. This model is then used for a medial shape description of individual objects via a constrained model fit. The process starts from parametric 3D boundary representations with existing point-to-point homology between objects. The Voronoi diagram of each sampled object boundary is grouped into medial sheets and simplified by a pruning algorithm using a volumetric contribution criterion. Medial sheets are combined to form a common medial branching topology. Finally, the medial sheets are sampled and represented as meshes of medial primitives. We present new results on populations of up to 184 biological objects. For these objects the common medial branching topology is described by a small number of sheets. Despite the coarse medial sampling, a close approximation of individual objects is achieved.

This paper presents a new approach to the correction of intensity inhomogeneities in magnetic resonance imaging (MRI) that significantly improves intensity-based tissue segmentation. The distortion of the image brightness values by a low-frequency bias field impedes visual inspection and segmentation. The new correction method called parametric bias field correction (PABIC) is based on a simplified model of the imaging process, a parametric model of tissue class statistics, and a polynomial model of the inhomogeneity field. We assume that the image is composed of pixels assigned to a small number of categories with a priori known statistics. Further we assume that the image is corrupted by noise and a low-frequency inhomogeneity field. The estimation of the parametric bias field is formulated as a nonlinear energy minimization problem using an evolution strategy (ES). The resulting bias field is independent of the image region configurations and thus overcomes limitations of methods based on homomorphic filtering. Furthermore, PABIC can correct bias distortions much larger than the image contrast. Input parameters are the intensity statistics of the classes and the degree of the polynomial function. The polynomial approach combines bias correction with histogram adjustment, making it well suited for normalizing the intensity histogram of datasets from serial studies. We present simulations and a quantitative validation with phantom and test images. A large number of MR image data acquired with breast, surface, and head coils, both in two dimensions and three dimensions, have been processed and demonstrate the versatility and robustness of this new bias correction scheme.

This paper presents a new method for the automatic segmentation and characterization of object changes in time series of three-dimensional data sets. The technique was inspired by procedures developed for analysis of functional MRI data sets. After precise registration of serial volume data sets to 4-D data, we applied a time series analysis taking into account the characteristic time function of variable lesions. The images were preprocessed with a correction of image field inhomogeneities and a normalization of the brightness over the whole time series. Thus, static regions remain unchanged over time, whereas changes in tissue characteristics produce typical intensity variations in the voxel's time series. A set of features was derived from the time series, expressing probabilities for membership to the sought structures. These multiple sources of uncertain evidence were combined to a single evidence value using Dempster-Shafer's theory. The project was driven by the objective of improving the segmentation and characterization of white matter lesions in serial MR data of multiple sclerosis patients. Pharmaceutical research and patient follow-up requires efficient and robust methods with a high degree of automation. The new approach replaces conventional segmentation of series of 3-D data sets by a 1-D processing of the temporal change at each voxel in the 4-D image data set. The new method has been applied to a total of 11 time series from different patient studies, covering time resolutions of 12 and 24 data sets over a period of about 1 year. The results demonstrate that time evolution is a highly sensitive feature for detection of fluctuating structures.