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Biomedical subjects

Sarang Joshi

Publications and source records attributed to Sarang Joshi.

3 recordsLinked to original sources

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↗

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↗

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↗