Abnormal vessel tortuosity as a marker of treatment response of malignant gliomas: preliminary report

pre-printDespite multiple advances in medical imaging, noninvasive monitoring of therapeutic efficacy for malignant gliomas remains problematic. An underutilized observation is that malignancy induces characteristic abnormalities of vessel shape. These characteristic shape abnormalities affect both capillaries and much larger vessels in the tumor vicinity, involve larger vessels prior to sprout formation, and are generally not present in hypervascular benign tumors. Vessel shape abnormalities associated with malignancy thus may appear independently of increase in vessel density. We hypothesize that an automated, computerized analysis of vessel shape as defined from high-resolution MRA can provide valuable information about tumor activity during the treatment of malignant gliomas. This report describes vessel shape properties in 10 malignant gliomas prior to treatment, in 2 patients in remission during treatment, and in 2 patients with recurrent disease. One subject was scanned multiple times. The method involves an automated, statistical analysis of vessel shape within a region of interest for each tumor, normalized by the values obtained from the vessels within the same region of interest of 34 healthy subjects. Results indicate that untreated tumors display statistically significant vessel tortuosity abnormalities. These abnormalities involve vessels not only within the tumor margins as defined from MR but also vessels in the surrounding tissue. The abnormalities resolve during effective treatment and recur with tumor recurrence. We conclude that vessel shape analysis could provide an important means of assessing tumor activity

Visualizing the Structure of Large Trees

This study introduces a new method of visualizing complex tree structured objects. The usefulness of this method is illustrated in the context of detecting unexpected features in a data set of very large trees. The major contribution is a novel two-dimensional graphical representation of each tree, with a covariate coded by color. The motivating data set contains three dimensional representations of brain artery systems of 105 subjects. Due to inaccuracies inherent in the medical imaging techniques, issues with the reconstruction algo- rithms and inconsistencies introduced by manual adjustment, various discrepancies are present in the data. The proposed representation enables quick visual detection of the most common discrepancies. For our driving example, this tool led to the modification of 10% of the artery trees and deletion of 6.7%. The benefits of our cleaning method are demonstrated through a statistical hypothesis test on the effects of aging on vessel structure. The data cleaning resulted in improved significance levels.Comment: 17 pages, 8 figure

Dimension reduction in principal component analysis for trees

The statistical analysis of tree structured data is a new topic in statistics with wide application areas. Some Principal Component Analysis (PCA) ideas were previously developed for binary tree spaces. In this study, we extend these ideas to the more general space of rooted and labeled trees. We re-de ne concepts such as tree-line and forward principal component tree-line for this more general space, and generalize the optimal algorithm that fi nds them. We then develop an analog of classical dimension reduction technique in PCA for the tree space. To do this, we de ne the components that carry the least amount of variation of a tree data set, called backward principal components. We present an optimal algorithm to find them. Furthermore, we investigate the relationship of these the forward principal components, and prove a path-independency property between the forward and backward techniques. We apply our methods to a data set of brain artery data set of 98 subjects. Using our techniques, we investigate how aging affects the brain artery structure of males and females. We also analyze a data set of organization structure of a large US company and explore the structural differences across different types of departments within the company

Simulation of brain tumors in MR images for evaluation of segmentation efficacy

Obtaining validation data and comparison metrics for segmentation of magnetic resonance images (MRI) are difficult tasks due to the lack of reliable ground truth. This problem is even more evident for images presenting pathology, which can both alter tissue appearance through infiltration and cause geometric distortions. Systems for generating synthetic images with user-defined degradation by noise and intensity inhomogeneity offer the possibility for testing and comparison of segmentation methods. Such systems do not yet offer simulation of sufficiently realistic looking pathology. This paper presents a system that combines physical and statistical modeling to generate synthetic multi-modal 3D brain MRI with tumor and edema, along with the underlying anatomical ground truth, Main emphasis is placed on simulation of the major effects known for tumor MRI, such as contrast enhancement, local distortion of healthy tissue, infiltrating edema adjacent to tumors, destruction and deformation of fiber tracts, and multi-modal MRI contrast of healthy tissue and pathology. The new method synthesizes pathology in multi-modal MRI and diffusion tensor imaging (DTI) by simulating mass effect, warping and destruction of white matter fibers, and infiltration of brain tissues by tumor cells. We generate synthetic contrast enhanced MR images by simulating the accumulation of contrast agent within the brain. The appearance of the the brain tissue and tumor in MRI is simulated by synthesizing texture images from real MR images. The proposed method is able to generate synthetic ground truth and synthesized MR images with tumor and edema that exhibit comparable segmentation challenges to real tumor MRI. Such image data sets will find use in segmentation reliability studies, comparison and validation of different segmentation methods, training and teaching, or even in evaluating standards for tumor size like the RECIST (Response Evaluation Criteria in Solid Tumors) criteria

A review of micro- and macrovascular analyses in the assessment of tumor-associated vasculature as visualized by MR

There is currently no noninvasive, reliable method of assessing brain tumor malignancy or of monitoring tumor treatment response. Monitoring changes to tumor vasculature might provide an effective means of assessing both tumor aggressiveness and treatment efficacy. To date, most such research has concentrated upon tumor “microvascular” imaging, with permeability and/or perfusion imaging used to assess vessel changes at the subvoxel level. An alternative approach assesses tumor vasculature at the “macroscopic” level, calculating the numbers and shapes of the larger vessels discriminable by magnetic resonance angiography. This paper provides an overview of magnetic resonance (MR) vascular imaging at both the microscopic (dynamic MR perfusion and permeability) and macroscopic (MR angiographic) levels. The two approaches provide different, complementary information and together could provide important insights into cancer growth as well as new methods of assessing malignancy and tumor treatment response

Preventing facial recognition when rendering MR images of the head in three dimensions

In the United States it is not allowed to make public any patient-specific information without the patient's consent. This ruling has led to difficulty for those interested in sharing three-dimensional (3D) images of the head and brain since a patient's face might be recognized from a 3D rendering of the skin surface. Approaches employed to date have included brain stripping and total removal of the face anterior to a cut plane, each of which lose potentially important anatomical information about the skull surface, air sinuses, and orbits. This paper describes a new approach that involves a) definition of a plane anterior to which the face lies, and b) an adjustable level of deformation of the skin surface anterior to that plane. On the basis of a user performance study using forced choices, we conclude that approximately 30% of individuals are at risk of recognition from 3D renderings of unaltered images and that truncation of the face below the level of the nose does not preclude facial recognition. Removal of the face anterior to a cut plane may interfere with accurate registration and may delete important anatomical information. Our new method alters little of the underlying anatomy and does not prevent effective registration into a common coordinate system. Although the methods presented here were not fully effective (one subject was consistently recognized under the forced choice study design even at the maximum deformation level employed) this paper may point a way toward solution of a difficult problem that has received little attention in the literature

Population Shape Regression from Random Design Data

Regression analysis is a powerful tool for the study of changes in a dependent variable as a function of an independent regressor variable, and in particular it is applicable to the study of anatomical growth and shape change. When the underlying process can be modeled by parameters in a Euclidean space, classical regression techniques [15,38] are applicable and have been studied extensively. However, recent work suggests that attempts to describe anatomical shapes using at Euclidean spaces undermines our ability to represent natural biological variability [10, 12]. In this paper we develop a method for regression analysis of general, manifold-valued data. Speci cally, we extend Nadaraya-Watson kernel regression by recasting the regression problem in terms of Fr�echet expectation. Although this method is quite general, our driving problem is the study anatomical shape change as a function of age from random design image data. We demonstrate our method by analyzing shape change in the brain from a random design dataset of MR images of 97 healthy adults ranging in age from 20 to 79 years. To study the small scale changes in anatomy, we use the in nite dimensional manifold of diffeomorphic transformations, with an associated metric. We regress a representative anatomical shape, as a function of age, from this population