Browsing by Subject "image registration"

Now showing 1 - 4 of 4
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    Building a Surface Atlas of Hippocampal Subfields from MRI Scans using FreeSurfer, FIRST and SPHARM
    (Institute of Electrical and Electronics Engineers, 2014-08) Cong, Shan; Rizkalla, Maher; Du, Eliza Y.; West, John; Risacher, Shannon; Saykin, Andrew J.; Shen, Li; Alzheimer's Disease Neuroimaging Initiative; Department of Medicine, IU School of Medicine
    The hippocampus is widely studied with neuroimaging techniques given its importance in learning and memory and its potential as a biomarker for brain disorders such as Alzheimer's disease and epilepsy. However, its complex folding anatomy often presents analytical challenges. In particular, the critical hippocampal subfield information is usually ignored by hippocampal registration in detailed morphometric studies. Such an approach is thus inadequate to accurately characterize hippocampal morphometry and effectively identify hippocampal structural changes related to different conditions. To bridge this gap, we present our initial effort towards building a computational framework for subfield-guided hippocampal morphometry. This initial effort is focused on surface-based morphometry and aims to build a surface atlas of hippocampal subfields. Using the FreeSurfer software package, we obtain valuable hippocampal subfield information. Using the FIRST software package, we extract reliable hippocampal surface information. Using SPHARM, we develop an approach to create an atlas by mapping interpolated subfield information onto an average surface. The empirical result using ADNI data demonstrates the promise and good reproducibility of the proposed method.
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    COMPARISON OF 3D VOLUME REGISTRATION TECHNIQUES APPLIED TO NEUROSURGERY
    (Office of the Vice Chancellor for Research, 2012-04-13) Verma, Romil; Cottingham, Chris; Nguyen, Thanh; Kale, Ashutosh; Catania, Robin; Wright, Jacob; Christopher, Lauren; Tuceryan, Mihan; William, Albert
    Introduction: Image guided surgery requires that the pre-operative da-ta used for planning the surgery should be aligned with the patient during surgery. For this surgical application a fast, effective volume registration al-gorithm is needed. In addition, such an algorithm can also be used to devel-op surgical training presentations. This research tests existing methods of image and volume registration with synthetic 3D models and with 3D skull data. The aim of this research is to find the most promising algorithms in ac-curacy and execution time that best fit the neurosurgery application. Methods: Medical image volumes acquired from MRI or CT medical im-aging scans provided by the Indiana University School of Medicine were used as Test image cases. Additional synthetic data with ground truth was devel-oped by the Informatics students. Each test image was processed through image registration algorithms found in four common medical imaging tools: MATLAB, 3D Slicer, VolView, and VTK/ITK. The resulting registration is com-pared against the ground truth evaluated with mean squared error metrics. Algorithm execution time is measured on standard personal computer (PC) hardware. Results: Data from this extensive set of tests reveal that the current state of the art algorithms all have strengths and weaknesses. These will be categorized and presented both in a poster form and in a 3D video presenta-tion produced by Informatics students in an auto stereoscopic 3D video. Conclusions: Preliminary results show that execution of image registra-tion in real-time is a challenging task for real time neurosurgery applica-tions. Final results will be available at paper presentation. Future research will focus on optimizing registration and also implementing deformable regis-tration in real-time.
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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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    Image Registration and Predictive Modeling: Learning the Metric on the Space of Diffeomorphisms
    (Springer, 2018-09) Mussabayeva, Ayagoz; Kroshnin, Alexey; Kurmukov, Anvar; Dodonova, Yulia; Shen, Li; Cong, Shan; Wang, Lei; Gutman, Boris A.; Computer Information and Graphics Technology, School of Engineering and Technology
    We present a method for metric optimization in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, by treating the induced Riemannian metric on the space of diffeomorphisms as a kernel in a machine learning context. For simplicity, we choose the kernel Fischer Linear Discriminant Analysis (KLDA) as the framework. Optimizing the kernel parameters in an Expectation-Maximization framework, we define model fidelity via the hinge loss of the decision function. The resulting algorithm optimizes the parameters of the LDDMM norm-inducing differential operator as a solution to a group-wise registration and classification problem. In practice, this may lead to a biology-aware registration, focusing its attention on the predictive task at hand such as identifying the effects of disease. We first tested our algorithm on a synthetic dataset, showing that our parameter selection improves registration quality and classification accuracy. We then tested the algorithm on 3D subcortical shapes from the Schizophrenia cohort Schizconnect. Our Schizophrenia-Control predictive model showed significant improvement in ROC AUC compared to baseline parameters.