Biomedical subjects

Sarang Joshi

Publications and source records attributed to Sarang Joshi.

15 recordsLinked to original sources

PubMed2006

Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.

Quantitative diffusion tensor imaging (DTI) has become the major imaging modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics of tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that systematically includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. A new measure of tensor anisotropy, called geodesic anisotropy (GA) is applied and compared with FA. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics (average and variance) calculated within cross-sections. Feasibility of our approach is demonstrated on various fiber tracts of a single data set. A validation study, based on six repeated scans of the same subject, assesses the reproducibility of this new DTI data analysis framework.

Algorithms↗

PubMed2006

Multi-modal image set registration and atlas formation.

In this paper, we present a Bayesian framework for both generating inter-subject large deformation transformations between two multi-modal image sets of the brain and for forming multi-class brain atlases. In this framework, the estimated transformations are generated using maximal information about the underlying neuroanatomy present in each of the different modalities. This modality independent registration framework is achieved by jointly estimating the posterior probabilities associated with the multi-modal image sets and the high-dimensional registration transformations mapping these posteriors. To maximally use the information present in all the modalities for registration, Kullback-Leibler divergence between the estimated posteriors is minimized. Registration results for image sets composed of multi-modal MR images of healthy adult human brains are presented. Atlas formation results are presented for a population of five infant human brains.

Algorithms↗

PubMed2005

Large deformation three-dimensional image registration in image-guided radiation therapy.

In this paper, we present and validate a framework, based on deformable image registration, for automatic processing of serial three-dimensional CT images used in image-guided radiation therapy. A major assumption in deformable image registration has been that, if two images are being registered, every point of one image corresponds appropriately to some point in the other. For intra-treatment images of the prostate, however, this assumption is violated by the variable presence of bowel gas. The framework presented here explicitly extends previous deformable image registration algorithms to accommodate such regions in the image for which no correspondence exists. We show how to use our registration technique as a tool for organ segmentation, and present a statistical analysis of this segmentation method, validating it by comparison with multiple human raters. We also show how the deformable registration technique can be used to determine the dosimetric effect of a given plan in the presence of non-rigid tissue motion. In addition to dose accumulation, we describe a method for estimating the biological effects of tissue motion using a linear-quadratic model. This work is described in the context of a prostate treatment protocol, but it is of general applicability.

Algorithms↗

PubMed2005

Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis.

Diffusion tensor imaging (DTI) has become the major modality to study properties of white matter and the geometry of fiber tracts of the human brain. Clinical studies mostly focus on regional statistics of fractional anisotropy (FA) and mean diffusivity (MD) derived from tensors. Existing analysis techniques do not sufficiently take into account that the measurements are tensors, and thus require proper interpolation and statistics based on tensors, and that regions of interest are fiber tracts with complex spatial geometry. We propose a new framework for quantitative tract-oriented DTI analysis that includes tensor interpolation and averaging, using nonlinear Riemannian symmetric space. As a result, tracts of interest are represented by the geometry of the medial spine attributed with tensor statistics calculated within cross-sections. Examples from a clinical neuroimaging study of the early developing brain illustrate the potential of this new method to assess white matter fiber maturation and integrity.

Algorithms↗

PubMed2005

Unbiased atlas formation via large deformations metric mapping.

The construction of population atlases is a key issue in medical image analysis, and particularly in brain mapping. Large sets of images are mapped into a common coordinate system to study intra-population variability and inter-population differences, to provide voxel-wise mapping of functional sites, and to facilitate tissue and object segmentation via registration of anatomical labels. We formulate the unbiased atlas construction problem as a Fréchet mean estimation in the space of diffeomorphisms via large deformations metric mapping. A novel method for computing constant speed velocity fields and an analysis of atlas stability and robustness using entropy are presented. We address the question: how many images are required to build a stable brain atlas?

Algorithms↗

PubMed2005

Vessel tortuosity and brain tumor malignancy: a blinded study.

RATIONALE AND OBJECTIVES: Malignancy provokes regional changes to vessel shape. Characteristic vessel tortuosity abnormalities appear early during tumor development, affect initially healthy vessels, spread beyond the confines of tumor margins, and do not simply mirror tissue perfusion. The ability to detect and quantify tortuosity abnormalities on high-resolution magnetic resonance angiography (MRA) images offers a new approach to the noninvasive diagnosis of malignancy. This report evaluates a computerized, statistical method of analyzing the shapes of vessels extracted from MRA in diagnosing cancer. MATERIALS AND METHODS: The regional vasculature of 34 healthy subjects was compared with the tumor-associated vasculature of 30 brain tumors before surgical resection. The operator performing the analysis was blinded to the diagnosis. Vessels were segmented from an MRA of each subject, a region of interest was defined in each tumor patient and was mapped to all healthy controls, and a statistical analysis of vessel shape measures was then performed over the region of interest. Many difficult cases were included, such as pinpoint, hemorrhagic, and irradiated tumors, as were hypervascular benign tumors. Tumors were identified as benign or malignant on the basis of histological evaluation. RESULTS: A discriminant analysis performed at the study's conclusion successfully classified all but one of the 30 tumors as benign or malignant on the basis of vessel tortuosity. CONCLUSIONS: Quantitative, statistical measures of vessel shape offer a new approach to the diagnosis and staging of disease. Although the methods developed under the current report must be tested against a new series of cases, initial results are promising.

Adult↗

PubMed2005

A method and software for segmentation of anatomic object ensembles by deformable m-reps.

Deformable shape models (DSMs) comprise a general approach that shows great promise for automatic image segmentation. Published studies by others and our own research results strongly suggest that segmentation of a normal or near-normal object from 3D medical images will be most successful when the DSM approach uses (1) knowledge of the geometry of not only the target anatomic object but also the ensemble of objects providing context for the target object and (2) knowledge of the image intensities to be expected relative to the geometry of the target and contextual objects. The segmentation will be most efficient when the deformation operates at multiple object-related scales and uses deformations that include not just local translations but the biologically important transformations of bending and twisting, i.e., local rotation, and local magnification. In computer vision an important class of DSM methods uses explicit geometric models in a Bayesian statistical framework to provide a priori information used in posterior optimization to match the DSM against a target image. In this approach a DSM of the object to be segmented is placed in the target image data and undergoes a series of rigid and nonrigid transformations that deform the model to closely match the target object. The deformation process is driven by optimizing an objective function that has terms for the geometric typicality and model-to-image match for each instance of the deformed model. The success of this approach depends strongly on the object representation, i.e., the structural details and parameter set for the DSM, which in turn determines the analytic form of the objective function. This paper describes a form of DSM called m-reps that has or allows these properties, and a method of segmentation consisting of large to small scale posterior optimization of m-reps. Segmentation by deformable m-reps, together with the appropriate data representations, visualizations, and user interface, has been implemented in software that accomplishes 3D segmentations in a few minutes. Software for building and training models has also been developed. The methods underlying this software and its abilities are the subject of this paper.

Algorithms↗

PubMed2005

Four-dimensional radiotherapy planning for DMLC-based respiratory motion tracking.

Four-dimensional (4D) radiotherapy is the explicit inclusion of the temporal changes in anatomy during the imaging, planning, and delivery of radiotherapy. Temporal anatomic changes can occur for many reasons, though the focus of the current investigation is respiration motion for lung tumors. The aim of this study was to develop 4D radiotherapy treatment-planning methodology for DMLC-based respiratory motion tracking. A 4D computed tomography (CT) scan consisting of a series of eight 3D CT image sets acquired at different respiratory phases was used for treatment planning. Deformable image registration was performed to map each CT set from the peak-inhale respiration phase to the CT image sets corresponding to subsequent respiration phases. Deformable registration allows the contours defined on the peak-inhale CT to be automatically transferred to the other respiratory phase CT image sets. Treatment planning was simultaneously performed on each of the eight 3D image sets via automated scripts in which the MLC-defined beam aperture conforms to the PTV (which in this case equaled the GTV due to CT scan length limitations) plus a penumbral margin at each respiratory phase. The dose distribution from each respiratory phase CT image set was mapped back to the peak-inhale CT image set for analysis. The treatment intent of 4D planning is that the radiation beam defined by the DMLC tracks the respiration-induced target motion based on a feedback loop including the respiration signal to a real-time MLC controller. Deformation with respiration was observed for the lung tumor and normal tissues. This deformation was verified by examining the mapping of high contrast objects, such as the lungs and cord, between image sets. For the test case, dosimetric reductions for the cord, heart, and lungs were found for 4D planning compared with 3D planning. 4D radiotherapy planning for DMLC-based respiratory motion tracking is feasible and may offer tumor dose escalation and/or a reduction in treatment-related complications. However, 4D planning requires new planning tools, such as deformable registration and automated treatment planning on multiple CT image sets.

Dose-Response Relationship, Radiation↗

PubMed2004

Principal geodesic analysis for the study of nonlinear statistics of shape.

A primary goal of statistical shape analysis is to describe the variability of a population of geometric objects. A standard technique for computing such descriptions is principal component analysis. However, principal component analysis is limited in that it only works for data lying in a Euclidean vector space. While this is certainly sufficient for geometric models that are parameterized by a set of landmarks or a dense collection of boundary points, it does not handle more complex representations of shape. We have been developing representations of geometry based on the medial axis description or m-rep. While the medial representation provides a rich language for variability in terms of bending, twisting, and widening, the medial parameters are not elements of a Euclidean vector space. They are in fact elements of a nonlinear Riemannian symmetric space. In this paper, we develop the method of principal geodesic analysis, a generalization of principal component analysis to the manifold setting. We demonstrate its use in describing the variability of medially-defined anatomical objects. Results of applying this framework on a population of hippocampi in a schizophrenia study are presented.

Algorithms↗

PubMed2004

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

Despite 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.

Cerebrovascular Circulation↗

PubMed2003

Feature selection for shape-based classification of biological objects.

This paper introduces a method for selecting subsets of relevant statistical features in biological shape-based classification problems. The method builds upon existing feature selection methodology by introducing a heuristic that favors the geometric locality of the selected features. This heuristic effectively reduces the combinatorial search space of the feature selection problem. The new method is tested on synthetic data and on clinical data from a study of hippocampal shape in schizophrenia. Results on clinical data indicate that features describing the head of the right hippocampus are most relevant for discrimination.

Algorithms↗

PubMed2003

Gaussian distributions on Lie groups and their application to statistical shape analysis.

The Gaussian distribution is the basis for many methods used in the statistical analysis of shape. One such method is principal component analysis, which has proven to be a powerful technique for describing the geometric variability of a population of objects. The Gaussian framework is well understood when the data being studied are elements of a Euclidean vector space. This is the case for geometric objects that are described by landmarks or dense collections of boundary points. We have been using medial representations, or m-reps, for modelling the geometry of anatomical objects. The medial parameters are not elements of a Euclidean space, and thus standard PCA is not applicable. In our previous work we have shown that the m-rep model parameters are instead elements of a Lie group. In this paper we develop the notion of a Gaussian distribution on this Lie group. We then derive the maximum likelihood estimates of the mean and the covariance of this distribution. Analogous to principal component analysis of covariance in Euclidean spaces, we define principal geodesic analysis on Lie groups for the study of anatomical variability in medially-defined objects. Results of applying this framework on a population of hippocampi in a schizophrenia study are presented.

Algorithms↗

PubMed2003

Structural and radiometric asymmetry in brain images.

This paper presents a general framework for analyzing structural and radiometric asymmetry in brain images. In a healthy brain, the left and right hemispheres are largely symmetric across the mid-sagittal plane. Brain tumors may belong to one or both of the following categories: mass-effect, in which the diseased tissue displaces healthy tissue; and infiltrating, in which healthy tissue has become diseased. Mass-effect brain tumors cause structural asymmetry by displacing healthy tissue, and may cause radiometric asymmetry in adjacent normal structures due to edema. Infiltrating tumors have a different radiometric response from healthy tissue. Thus, structural and radiometric asymmetries across the mid-sagittal plane in brain images provide important cues that tumors may be present. We have developed a framework that registers images with their reflections across the mid-sagittal plane. The registration process accounts for tissue displacement through large deformation image warping. Radiometric differences are taken into account through an additive intensity field. We present an efficient multi-scale algorithm for the joint estimation of structural and radiometric asymmetry. Results for nine MR images of patients with tumors and four normal control subjects are presented.

Algorithms↗

PubMed2003

Hippocampal deformation-based shape analysis in epilepsy and unilateral mesial temporal sclerosis.

PURPOSE: To assess shape changes in patients with mesial temporal sclerosis (MTS) and temporal lobe epilepsy (TLE), by using deformation-based hippocampal shape analysis. METHODS: We retrospectively reviewed magnetic resonance imaging (MRI) studies in 30 subjects with unilateral MTS (15 right-sided MTS, 15 left-sided MTS) and TLE. We defined the "average" hippocampus in the right- and left-MTS groups by generating a mean transformation for the 15 deformation images in each group. Further to quantify the difference between the hippocampi, we coregistered the mean transformation of the involved hippocampus (with MTS) to the contralateral hippocampus, considering the left- and right-MTS groups independently. We generated a color "flame" scale showing degrees of outward and inward deviation of the coregistered hippocampi. RESULTS: Both the right- and left-MTS groups showed similar shape changes, with maximal inward deformation in the medial and lateral hippocampal head and the hippocampal tail. However, more extensive involvement was seen in the lateral hippocampal body in the right-MTS group as compared with the left. CONCLUSIONS: Deformation-based hippocampal shape analysis shows specific regions of hippocampal surface anatomy that are most affected in MTS. This technique may aid in detection of clinically significant anatomic abnormalities of the hippocampus in patients with epilepsy.

Adolescent↗

PubMed2002

Multiscale deformable model segmentation and statistical shape analysis using medial descriptions.

This paper presents a multiscale framework based on a medial representation for the segmentation and shape characterization of anatomical objects in medical imagery. The segmentation procedure is based on a Bayesian deformable templates methodology in which the prior information about the geometry and shape of anatomical objects is incorporated via the construction of exemplary templates. The anatomical variability is accommodated in the Bayesian framework by defining probabilistic transformations on these templates. The transformations, thus, defined are parameterized directly in terms of natural shape operations, such as growth and bending, and their locations. A preliminary validation study of the segmentation procedure is presented. We also present a novel statistical shape analysis approach based on the medial descriptions that examines shape via separate intuitive categories, such as global variability at the coarse scale and localized variability at the fine scale. We show that the method can be used to statistically describe shape variability in intuitive terms such as growing and bending.

Algorithms↗