Browsing by Author "Chandorkar, Sujay"

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    Developing New Image Registration Techniques and 3D Displays for Neuroimaging and Neurosurgery
    (Office of the Vice Chancellor for Research, 2014-04-11) Zheng, Yuese; Chiu, Kai-Wen; Jin, Dongcheng; Chandorkar, Sujay; Zajac, Sarah; Nicholson, Emily
    Image guided surgery requires that the pre-operative data used for planning the surgery should be aligned with the patient during surgery. For this surgical application a fast, effective volume registration algorithm is needed. In addition, such an algorithm can also be used to develop surgical training presentations. This research extends existing methods and techniques to improve convergence and speed of execution. The aim is to find the most promising speed improvements while maintaining accuracy to best fit the neurosurgery application. In the recent phase, we focus on algorithm speed up by translating the registration algorithm from Matlab into Java. Medical image volumes acquired fromMRI scans and a depth map from the video data provided by Indiana University School of Medicine were used as testing images. Accuracy of the results from the translated algorithm is compared against the ground truth evaluated with mean squared error metrics. Algorithm execution time with and without the code translation is measured on standard personal computer (PC) hardware. The 3D registered model is developed by the Informatics students to show the results of the speed improvements from the remaining students’ work. Additionally, the surgical and preoperative data overlay will be presented in a 3D movie. Our past testing indicates that an intelligent subset of the data points that are needed for registration improved the speed significantly but was still time taking. Preliminary results show that even though image registration in real-time is a challenging task for real time neurosurgery applications, intelligent preprocessing provides a promising solution. Final results will be available at poster presentation.
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    TOWARDS A PATHWAY MODELING APPROACH TO ALZHEIMER’S DISEASE DRUG DISCOVERY
    (Office of the Vice Chancellor for Research, 2012-04-13) Ibrahim, Sara; Capouch, Don; Chandorkar, Sujay; Chen, Jake Yue; Saykin, Andrew J.; Wu, Xiaogang; Huang, Hui
    Network pharmacology has emerged as a new topic of study in recent years. Molecular connectivity maps between drugs and genes/proteins in specific disease contexts can be particularly valuable, since the functional approach with these maps helps researchers gain global perspectives on both the therapeutic and toxicological profiles of drugs. To assess drug pharmacological effects, we assume that “ideal” drugs for a patient can treat or prevent the disease by modulating gene expression profiles of this patient to the similar level with those in healthy people. Starting from this hypothesis, we build comprehensive disease-gene-drug connectivity relationships with drug-protein directionality (inhibit/activate) information based on a computational connectivity maps (CMaps) platform. In this work, we develop a novel approach based on integrative pathway modeling. Using Alzheimer’s disease (AD) as an example, we identify and rank AD-related drugs/compounds with their overall drug-protein “connectivity map” profile. First, we retrieve AD-associated proteins through the CMaps platform by using “Alzheimer’s disease” as a query term. Second, we retrieve AD-related pathways by using those AD-associated proteins as input and searching in the Human Pathway Database (HPD) and the PubMed. Third, we integrate the AD-related pathways into unified pathway models, from which we categorize the pharmaceutical effects of candidate drugs on all AD-associated proteins as either “therapeutic” or “toxic” (Figure 1). Finally, we transform the integrated pathways into network models and rank drugs based on the network topological features of drug targets, drug-affecting genes/proteins, and curated AD-associated proteins. We demonstrate that our approach can help identify AD drug candidates with significant therapeutic potentials with small toxic side effects. The case study correlates very well with the existing pharmacology of AD drugs and highlights the significance of the CMaps platform. Ongoing studies towards this direction also have the potential of changing future process of AD drug development. 1Indiana University School of Medicine.