Publications by Year: 2006

Sharon Peled, Ola Friman, Ferenc A Jolesz, and Carl-Fredrik Westin. 11/2006. “Geometrically Constrained Two-Tensor Model for Crossing Tracts in DWI.” Magn Reson Imaging, 24, 9, Pp. 1263-70.Abstract

MR diffusion tensor imaging (DTI) of the brain and spine provides a unique tool for both visualizing directionality and assessing intactness of white matter fiber tracts in vivo. At the spatial resolution of clinical MRI, much of primate white matter is composed of interdigitating fibers. Analyses based on an assumed single diffusion tensor per voxel yield important information about the average diffusion in the voxel but fail to reveal structure in the presence of crossing tracts. Until today, all clinical scans assume only one tensor, causing potential serious errors in tractography. Since high angular resolution imaging remains, so far, untenable for routine clinical use, a method is proposed whereby the single-tensor field is augmented with additional information gleaned from standard clinical DTI. The method effectively resolves two distinct tract directions within voxels, in which only two tracts are assumed to exist. The underlying constrained two-tensor model is fitted in two stages, utilizing the information present in the single-tensor fit. As a result, the necessary MRI time can be drastically reduced when compared with other approaches, enabling widespread clinical use. Upon evaluation in simulations and application to in vivo human brain DTI data, the method appears to be robust and practical and, if correctly applied, could elucidate tract directions at critical points of uncertainty.

Simon K Warfield, Kelly H Zou, and William M Wells III. 10/2006. “Validation of Image Segmentation by Estimating Rater Bias and Variance.” Med Image Comput Comput Assist Interv, 9, Pt 2, Pp. 839-47.Abstract

The accuracy and precision of segmentations of medical images has been difficult to quantify in the absence of a "ground truth" or reference standard segmentation for clinical data. Although physical or digital phantoms can help by providing a reference standard, they do not allow the reproduction of the full range of imaging and anatomical characteristics observed in clinical data. An alternative assessment approach is to compare to segmentations generated by domain experts. Segmentations may be generated by raters who are trained experts or by automated image analysis algorithms. Typically these segmentations differ due to intra-rater and inter-rater variability. The most appropriate way to compare such segmentations has been unclear. We present here a new algorithm to enable the estimation of performance characteristics, and a true labeling, from observations of segmentations of imaging data where segmentation labels may be ordered or continuous measures. This approach may be used with, amongst others, surface, distance transform or level set representations of segmentations, and can be used to assess whether or not a rater consistently over-estimates or under-estimates the position of a boundary.

Kilian M Pohl, John Fisher, Eric WL Grimson, Ron Kikinis, and William M Wells III. 5/2006. “A Bayesian Model for Joint Segmentation and Registration.” Neuroimage, 31, 1, Pp. 228-39.Abstract

A statistical model is presented that combines the registration of an atlas with the segmentation of magnetic resonance images. We use an Expectation Maximization-based algorithm to find a solution within the model, which simultaneously estimates image artifacts, anatomical labelmaps, and a structure-dependent hierarchical mapping from the atlas to the image space. The algorithm produces segmentations for brain tissues as well as their substructures. We demonstrate the approach on a set of 22 magnetic resonance images. On this set of images, the new approach performs significantly better than similar methods which sequentially apply registration and segmentation.

Lauren J O'Donnell, Marek Kubicki, Martha E Shenton, Mark H Dreusicke, WEL Grimson, and Carl-Fredrik Westin. 5/2006. “A Method for Clustering White Matter Fiber Tracts.” AJNR Am J Neuroradiol, 27, 5, Pp. 1032-6.Abstract

BACKGROUND/PURPOSE: Despite its potential for visualizing white matter fiber tracts in vivo, diffusion tensor tractography has found only limited applications in clinical research in which specific anatomic connections between distant regions need to be evaluated. We introduce a robust method for fiber clustering that guides the separation of anatomically distinct fiber tracts and enables further estimation of anatomic connectivity between distant brain regions. METHODS: Line scanning diffusion tensor images (LSDTI) were acquired on a 1.5T magnet. Regions of interest for several anatomically distinct fiber tracts were manually drawn; then, white matter tractography was performed by using the Runge-Kutta method to interpolate paths (fiber traces) following the major directions of diffusion, in which traces were seeded only within the defined regions of interest. Next, a fully automatic procedure was applied to fiber traces, grouping them according to a pairwise similarity function that takes into account the shapes of the fibers and their spatial locations. RESULTS: We demonstrated the ability of the clustering algorithm to separate several fiber tracts which are otherwise difficult to define (left and right fornix, uncinate fasciculus and inferior occipitofrontal fasciculus, and corpus callosum fibers). CONCLUSION: This method successfully delineates fiber tracts that can be further analyzed for clinical research purposes. Hypotheses regarding specific fiber connections and their abnormalities in various neuropsychiatric disorders can now be tested.

Simon P Dimaio, Daniel F Kacher, Randy E Ellis, Gabor Fichtinger, Nobuhiko Hata, Gary P Zientara, Lawrence P Panych, Ron Kikinis, and Ferenc A Jolesz. 1/2006. “Needle Artifact Localization in 3T MR Images.” Stud Health Technol Inform, 119, Pp. 120-5.Abstract

This work explores an image-based approach for localizing needles during MRI-guided interventions, for the purpose of tracking and navigation. Susceptibility artifacts for several needles of varying thickness were imaged, in phantoms, using a 3 tesla MRI system, under a variety of conditions. The relationship between the true needle positions and the locations of artifacts within the images, determined both by manual and automatic segmentation methods, have been quantified and are presented here.

Julien Dauguet, Sharon Peled, Vladimir Berezovskii, Thierry Delzescaux, Simon K Warfield, Richard Born, and Carl-Fredrik Westin. 2006. “3D histological reconstruction of fiber tracts and direct comparison with diffusion tensor MRI tractography.” Med Image Comput Comput Assist Interv, 9, Pt 1, Pp. 109-16.Abstract
A classical neural tract tracer, WGA-HRP, was injected at multiple sites within the brain of a macaque monkey. Histological sections of the labeled fiber tracts were reconstructed in 3D, and the fibers were segmented and registered with the anatomical post-mortem MRI from the same animal. Fiber tracing along the same pathways was performed on the DTI data using a classical diffusion tracing technique. The fibers derived from the DTI were compared with those segmented from the histology in order to evaluate the performance of DTI fiber tracing. While there was generally good agreement between the two methods, our results reveal certain limitations of DTI tractography, particularly at regions of fiber tract crossing or bifurcation.
Yan Yang, Stephanie George, Diego R Martin, Allen R Tannenbaum, and Don P Giddens. 2006. “3D modeling of patient-specific geometries of portal veins using MR images.” Conf Proc IEEE Eng Med Biol Soc, 1, Pp. 5290-3.Abstract
In this note, we present an approach for developing patient-specific 3D models of portal veins to provide geometric boundary conditions for computational fluid dynamics (CFD) simulations of the blood flow inside portal veins. The study is based on MRI liver images of individual patients to which we apply image registration and segmentation techniques and inlet and outlet velocity profiles acquired using PC-MRI in the same imaging session. The portal vein and its connected veins are then extracted and visualized in 3D as surfaces. Image registration is performed to align shifted images between each breath-hold when the MRI images are acquired. The image segmentation method first labels each voxel in the 3D volume of interest by using a Bayesian probability approach, and then isolates the portal veins via active surfaces initialized inside the vessel. The method was tested with two healthy volunteers. In both cases, the main portal vein and its connected veins were successfully modeled and visualized.
Raúl San José Estépar, George G Washko, Edwin K Silverman, John J Reilly, Ron Kikinis, and Carl-Fredrik Westin. 2006. “Accurate airway wall estimation using phase congruency.” Med Image Comput Comput Assist Interv, 9, Pt 2, Pp. 125-34.Abstract
Quantitative analysis of computed tomographic (CT) images of the lungs is becoming increasingly useful in the medical and surgical management of subjects with Chronic Obstructive Pulmonary Disease (COPD). Current methods for the assessment of airway wall work well in idealized models of the airway. We propose a new method for airway wall detection based on phase congruency. This method does not rely on either a specific model of the airway or the point spread function of the scanner. Our results show that our method gives a better localization of the airway wall than "full width at a half max" and is less sensitive to different reconstruction kernels and radiation doses.
Gunnar Farnebäck and Carl-Fredrik Westin. 2006. “Affine and deformable registration based on polynomial expansion.” Med Image Comput Comput Assist Interv, 9, Pt 1, Pp. 857-64.Abstract
This paper presents a registration framework based on the polynomial expansion transform. The idea of polynomial expansion is that the image is locally approximated by polynomials at each pixel. Starting with observations of how the coefficients of ideal linear and quadratic polynomials change under translation and affine transformation, algorithms are developed to estimate translation and compute affine and deformable registration between a fixed and a moving image, from the polynomial expansion coefficients. All algorithms can be used for signals of any dimensionality. The algorithms are evaluated on medical data.
Neculai Archip, Robert Rohling, Vincent Dessenne, Pierre-Jean Erard, and Lutz Peter Nolte. 2006. “Anatomical structure modeling from medical images.” Comput Methods Programs Biomed, 82, 3, Pp. 203-15.Abstract
Some clinical applications, such as surgical planning, require volumetric models of anatomical structures represented as a set of tetrahedra. A practical method of constructing anatomical models from medical images is presented. The method starts with a set of contours segmented from the medical images by a clinician and produces a model that has high fidelity with the contours. Unlike most modeling methods, the contours are not restricted to lie on parallel planes. The main steps are a 3D Delaunay tetrahedralization, culling of non-object tetrahedra, and refinement of the tetrahedral mesh. The result is a high-quality set of tetrahedra whose surface points are guaranteed to match the original contours. The key is to use the distance map and bit volume structures that were created along with the contours. The method is demonstrated on computed tomography, MRI and 3D ultrasound data. Models of 170,000 tetrahedra are constructed on a standard workstation in approximately 10s. A comparison with related methods is also provided.
Gordon Kindlmann, Xavier Tricoche, and Carl-Fredrik Westin. 2006. “Anisotropy creases delineate white matter structure in diffusion tensor MRI.” Med Image Comput Comput Assist Interv, 9, Pt 1, Pp. 126-33.Abstract
Current methods for extracting models of white matter architecture from diffusion tensor MRI are generally based on fiber tractography. For some purposes a compelling alternative may be found in analyzing the first and second derivatives of diffusion anisotropy. Anisotropy creases are ridges and valleys of locally extremal anisotropy, where the gradient of anisotropy is orthogonal to one or more eigenvectors of its Hessian. We propose that anisotropy creases provide a basis for extracting a skeleton of white matter pathways, in that ridges of anisotropy coincide with interiors of fiber tracts, and valleys of anisotropy coincide with the interfaces between adjacent but distinctly oriented tracts. We describe a crease extraction algorithm that generates high-quality polygonal models of crease surfaces, then demonstrate the method on a measured diffusion tensor dataset, and visualize the result in combination with tractography to confirm its anatomic relevance.
Ying Wu, Simon K Warfield, Leng I Tan, William M Wells, Dominik S Meier, Ronald A van Schijndel, Frederik Barkhof, and Charles RG Guttmann. 2006. “Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI.” Neuroimage, 32, 3, Pp. 1205-15.Abstract
PURPOSE: To automatically segment multiple sclerosis (MS) lesions into three subtypes (i.e., enhancing lesions, T1 "black holes", T2 hyperintense lesions). MATERIALS AND METHODS: Proton density-, T2- and contrast-enhanced T1-weighted brain images of 12 MR scans were pre-processed through intracranial cavity (IC) extraction, inhomogeneity correction and intensity normalization. Intensity-based statistical k-nearest neighbor (k-NN) classification was combined with template-driven segmentation and partial volume artifact correction (TDS+) for segmentation of MS lesions subtypes and brain tissue compartments. Operator-supervised tissue sampling and parameter calibration were performed on 2 randomly selected scans and were applied automatically to the remaining 10 scans. Results from this three-channel TDS+ (3ch-TDS+) were compared to those from a previously validated two-channel TDS+ (2ch-TDS+) method. The results of both the 3ch-TDS+ and 2ch-TDS+ were also compared to manual segmentation performed by experts. RESULTS: Intra-class correlation coefficients (ICC) of 3ch-TDS+ for all three subtypes of lesions were higher (ICC between 0.95 and 0.96) than that of 2ch-TDS+ for T2 lesions (ICC = 0.82). The 3ch-TDS+ also identified the three lesion subtypes with high specificity (98.7-99.9%) and accuracy (98.5-99.9%). Sensitivity of 3ch-TDS+ for T2 lesions was 16% higher than with 2ch-TDS+. Enhancing lesions were segmented with the best sensitivity (81.9%). "Black holes" were segmented with the least sensitivity (62.3%). CONCLUSION: 3ch-TDS+ is a promising method for automated segmentation of MS lesion subtypes.
Ola Friman, Gunnar Farnebäck, and Carl-Fredrik Westin. 2006. “A Bayesian approach for stochastic white matter tractography.” IEEE Trans Med Imaging, 25, 8, Pp. 965-78.Abstract
White matter fiber bundles in the human brain can be located by tracing the local water diffusion in diffusion weighted magnetic resonance imaging (MRI) images. In this paper, a novel Bayesian modeling approach for white matter tractography is presented. The uncertainty associated with estimated white matter fiber paths is investigated, and a method for calculating the probability of a connection between two areas in the brain is introduced. The main merits of the presented methodology are its simple implementation and its ability to handle noise in a theoretically justified way. Theory for estimating global connectivity is also presented, as well as a theorem that facilitates the estimation of the parameters in a constrained tensor model of the local water diffusion profile.
Charles RG Guttmann, Dominik S Meier, and Christopher M Holland. 2006. “Can MRI reveal phenotypes of multiple sclerosis?” Magn Reson Imaging, 24, 4, Pp. 475-81.Abstract
The multicontrast capability of magnetic resonance imaging (MRI) is discussed in its role in the search for phenotypes of multiple sclerosis (MS). Aspects of MRI specificity, putative markers for pathogenetic components of disease and issues of spatial and temporal distribution are discussed. While particular reference is made to MS, the concepts apply to common pathological features of many neurologic diseases and to neurodegenerative disease in general. The assessment and dissociation of disease activity and disease severity, as well as the combination of varied metrics for the purposes of inferential and predictive disease modeling, are explored with respect to biomarkers and clinical outcomes. By virtue of its noninvasive nature and multicontrast capabilities depicting multiple facets of MS pathology, MRI lends itself to the systematic search of pathogenetically distinct subtypes of MS in large populations of patients. In conjunction with clinical, immunological, serological and genetic information, clusters of MS patients with distinct clinical prognosis and diverse response profiles to available and future treatments may be identified.
Andrzej Szymczak, Arthur Stillman, Allen Tannenbaum, and Konstantin Mischaikow. 2006. “Coronary vessel trees from 3D imagery: a topological approach.” Med Image Anal, 10, 4, Pp. 548-59.Abstract
We propose a simple method for reconstructing vascular trees from 3D images. Our algorithm extracts persistent maxima of the intensity on all axis-aligned 2D slices of the input image. The maxima concentrate along 1D intensity ridges, in particular along blood vessels. We build a forest connecting the persistent maxima with short edges. The forest tends to approximate the blood vessels present in the image, but also contains numerous spurious features and often fails to connect segments belonging to one vessel in low contrast areas. We improve the forest by applying simple geometric filters that trim short branches, fill gaps in blood vessels and remove spurious branches from the vascular tree to be extracted. Experiments show that our technique can be applied to extract coronary trees from heart CT scans.
Erik G Learned-Miller. 2006. “Data driven image models through continuous joint alignment.” IEEE Trans Pattern Anal Mach Intell, 28, 2, Pp. 236-50.Abstract
This paper presents a family of techniques that we call congealing for modeling image classes from data. The idea is to start with a set of images and make them appear as similar as possible by removing variability along the known axes of variation. This technique can be used to eliminate "nuisance" variables such as affine deformations from handwritten digits or unwanted bias fields from magnetic resonance images. In addition to separating and modeling the latent images-i.e., the images without the nuisance variables-we can model the nuisance variables themselves, leading to factorized generative image models. When nuisance variable distributions are shared between classes, one can share the knowledge learned in one task with another task, leading to efficient learning. We demonstrate this process by building a handwritten digit classifier from just a single example of each class. In addition to applications in handwritten character recognition, we describe in detail the application of bias removal from magnetic resonance images. Unlike previous methods, we use a separate, nonparametric model for the intensity values at each pixel. This allows us to leverage the data from the MR images of different patients to remove bias from each other. Only very weak assumptions are made about the distributions of intensity values in the images. In addition to the digit and MR applications, we discuss a number of other uses of congealing and describe experiments about the robustness and consistency of the method.
Oleg V Michailovich and Allen Tannenbaum. 2006. “Despeckling of medical ultrasound images.” IEEE Trans Ultrason Ferroelectr Freq Control, 53, 1, Pp. 64-78.Abstract
Speckle noise is an inherent property of medical ultrasound imaging, and it generally tends to reduce the image resolution and contrast, thereby reducing the diagnostic value of this imaging modality. As a result, speckle noise reduction is an important prerequisite, whenever ultrasound imaging is used for tissue characterization. Among the many methods that have been proposed to perform this task, there exists a class of approaches that use a multiplicative model of speckled image formation and take advantage of the logarithmical transformation in order to convert multiplicative speckle noise into additive noise. The common assumption made in a dominant number of such studies is that the samples of the additive noise are mutually uncorrelated and obey a Gaussian distribution. The present study shows conceptually and experimentally that this assumption is oversimplified and unnatural. Moreover, it may lead to inadequate performance of the speckle reduction methods. The study introduces a simple preprocessing procedure, which modifies the acquired radio-frequency images (without affecting the anatomical information they contain), so that the noise in the log-transformation domain becomes very close in its behavior to a white Gaussian noise. As a result, the preprocessing allows filtering methods based on assuming the noise to be white and Gaussian, to perform in nearly optimal conditions. The study evaluates performances of three different, nonlinear filters--wavelet denoising, total variation filtering, and anisotropic diffusion--and demonstrates that, in all these cases, the proposed preprocessing significantly improves the quality of resultant images. Our numerical tests include a series of computer-simulated and in vivo experiments.
Marc Niethammer, William D Kalies, Konstantin Mischaikow, and Allen Tannenbaum. 2006. “On the detection of simple points in higher dimensions using cubical homology.” IEEE Trans Image Process, 15, 8, Pp. 2462-9.Abstract
Simple point detection is an important task for several problems in discrete geometry, such as topology preserving thinning in image processing to compute discrete skeletons. In this paper, the approach to simple point detection is based on techniques from cubical homology, a framework ideally suited for problems in image processing. A (d-dimensional) unitary cube (for a d-dimensional digital image) is associated with every discrete picture element, instead of a point in epsilon(d) (the d-dimensional Euclidean space) as has been done previously. A simple point in this setting then refers to the removal of a unitary cube without changing the topology of the cubical complex induced by the digital image. The main result is a characterization of a simple point p (i.e., simple unitary cube) in terms of the homology groups of the (3d - 1) neighborhood of p for arbitrary, finite dimensions
Orjan Bergmann, Gordon Kindlmann, Arvid Lundervold, and Carl-Fredrik Westin. 2006. “Diffusion k-tensor estimation from Q-ball imaging using discretized principal axes.” Med Image Comput Comput Assist Interv, 9, Pt 2, Pp. 268-75.Abstract
A reoccurring theme in the diffusion tensor imaging literature is the per-voxel estimation of a symmetric 3 x 3 tensor describing the measured diffusion. In this work we attempt to generalize this approach by calculating 2 or 3 or up to k diffusion tensors for each voxel. We show that our procedure can more accurately describe the diffusion particularly when crossing fibers or fiber-bundles are present in the datasets.
Lauren O'Donnell and Carl-Fredrik Westin. 2006. “High-dimensional white matter atlas generation and group analysis.” Med Image Comput Comput Assist Interv, 9, Pt 2, Pp. 243-51.Abstract
We present a two-step process including white matter atlas generation and automatic segmentation. Our atlas generation method is based on population fiber clustering. We produce an atlas which contains high-dimensional descriptors of fiber bundles as well as anatomical label information. We use the atlas to automatically segment tractography in the white matter of novel subjects and we present quantitative results (FA measurements) in segmented white matter regions from a small population. We demonstrate reproducibility of these measurements across scans. In addition, we introduce the idea of using clustering for automatic matching of anatomical structures across hemispheres.