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Imaging Breast Adipose And Fibroglandular Tissue Molecular Signatures By Using Hybrid Mri-Guided Near-Infrared Spectral Tomography, Ben Brooksby, Brian W. Pogue, Shudong Jiang, Hamid Dehghani, Subhadra Srinivasan, Christine Kogel, Tor D. Tosteson, John Weaver, Steven P. Poplack, Keith D. Paulsen
Imaging Breast Adipose And Fibroglandular Tissue Molecular Signatures By Using Hybrid Mri-Guided Near-Infrared Spectral Tomography, Ben Brooksby, Brian W. Pogue, Shudong Jiang, Hamid Dehghani, Subhadra Srinivasan, Christine Kogel, Tor D. Tosteson, John Weaver, Steven P. Poplack, Keith D. Paulsen
Dartmouth Scholarship
Magnetic resonance (MR)-guided near-infrared spectral tomography was developed and used to image adipose and fibroglandular breast tissue of 11 normal female subjects, recruited under an institutional review board-approved protocol. Images of hemoglobin, oxygen saturation, water fraction, and subcellular scattering were reconstructed and show that fibroglandular fractions of both blood and water are higher than in adipose tissue. Variation in adipose and fibroglandular tissue composition between individuals was not significantly different across the scattered and dense breast categories. Combined MR and near-infrared tomography provides fundamental molecular information about these tissue types with resolution governed by MR T1 images.
Reliable In-Plane Velocity Measurements With Magnetic Resonance Velocity Imaging, Haosen Zhang, Sandra S. Halliburton, Andan K. Venkatachari, Randolph M. Setser, Richard D. White, George P. Chatzimavroudis
Reliable In-Plane Velocity Measurements With Magnetic Resonance Velocity Imaging, Haosen Zhang, Sandra S. Halliburton, Andan K. Venkatachari, Randolph M. Setser, Richard D. White, George P. Chatzimavroudis
Chemical & Biomedical Engineering Faculty Publications
Magnetic resonance (MR) imaging is a well-known diagnostic imaging modality. In addition to its high-quality imaging capabilities, hydrogen-based MR can also provide non-invasively the velocity of water-based fluids in all three spatial directions (through-plane and in-plane) in an image. Many previous studies showed that MR velocity imaging can accurately measure the through-plane velocity. The aim of this study was to evaluate how reliable are the in-plane velocity measurements in an image. The axial velocity of water in horizontal tubes (inner diameter: 14.7–26.2 mm) was measured with segmented (fast) and non-segmented (slow) k-space MR velocity …
Least Squares Support Vector Machine Based Classification Of Abnormalities In Brain Mr Images, S. Thamarai Selvi, D. Selvathi, R. Ramkumar, Henry Selvaraj
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 …