Biomedical Engineering and Bioengineering Commons^™

Magnetic Resonance Imaging Compatible Remote Catheter Navigation System, Mohammad Ali Tavallaei

Electronic Thesis and Dissertation Repository

Many vascular and cardiac diseases are diagnosed and treated using a medical technique known as percutaneous transluminal catheter intervention (PTC). In PTC, the interventionalist inserts a catheter into the vasculature, and using the vessel as the guiding passageway, the catheter is navigated to desired anatomical targets where it would be used for various purposes such as catheter ablation for the treatment/management of cardiac arrhythmias. The catheterization procedure is conventionally guided with x-ray fluoroscopic imaging and more recently, but rarely, with Magnetic Resonance Imaging (MRI). X-ray imaging irradiates the patient directly during the procedure, and the staff and interventionalists indirectly through …

A Comparative Study Of Two Prediction Models For Brain Tumor Progression, Deqi Zhou, Loc Tran, Jihong Wang, Jiang Li, Karen O. Egiazarian (Ed.), Sos S. Agaian (Ed.), Atanas P. Gotchev (Ed.)

Electrical & Computer Engineering Faculty Publications

MR diffusion tensor imaging (DTI) technique together with traditional T1 or T2 weighted MRI scans supplies rich information sources for brain cancer diagnoses. These images form large-scale, high-dimensional data sets. Due to the fact that significant correlations exist among these images, we assume low-dimensional geometry data structures (manifolds) are embedded in the high-dimensional space. Those manifolds might be hidden from radiologists because it is challenging for human experts to interpret high-dimensional data. Identification of the manifold is a critical step for successfully analyzing multimodal MR images.

We have developed various manifold learning algorithms (Tran et al. 2011; Tran et al. …

Under-Sampled Reconstruction Techniques For Accelerated Magnetic Resonance Imaging, Mohammad H. Kayvanrad

Electronic Thesis and Dissertation Repository

Due to physical and biological constraints and requirements on the minimum resolution and SNR, the acquisition time is relatively long in magnetic resonance imaging (MRI). Consequently, a limited number of pulse sequences can be run in a clinical MRI session because of constraints on the total acquisition time due to patient comfort and cost considerations. Therefore, it is strongly desired to reduce the acquisition time without compromising the reconstruction quality. This thesis concerns under-sampled reconstruction techniques for acceleration of MRI acquisitions, i.e., parallel imaging and compressed sensing.

While compressed sensing MRI reconstructions are commonly regularized by penalizing the decimated wavelet …

Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj

Electrical & Computer Engineering Faculty Research

The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed. This research paper proposes an intelligent classification technique to the problem of classifying four types of brain abnormalities viz. Metastases, Meningiomas, Gliomas, and Astrocytomas. The abnormalities are classified based on Two/Three/ Four class classification using statistical and textural features. In this work, classification techniques based on Least Squares Support Vector Machine (LS-SVM) using textural features computed from the MR images of patient are …