Muge Tian Yanlei Xu Ben Hier Mohamed Ibrahim November 29, 2007.
Seminar Muge Karaman Mar2018Muge Karaman received her Ph.D. in Computational Sciences from Marquette...
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Muge Karaman received her Ph.D. in Computational Sciences from Marquette University. She is a post-doc- toral research associate in the Center for Magnetic Resonance Research at the University of Illinois College of Medicine at Chicago. Her current research interests include development of non-Gaussian diffusion MRI methods and their applica- tion for characterizing tissue complexity and heterogene- ity, particularly for cancer diagnosis and treatment evaluation. She has published 11 peer-reviewed papers and 28 conference abstracts. Muge Karaman Post-doctoral research associate Center for Magnetic Resonance Research University of Illinois College of Medicine at Chicago Tuesday, March 13, 2018 11:00 a.m. - 12:00 p.m. 2240 DCL, 1304 W. Springfield Ave., Urbana Tumor tissues exhibit a high degree of structural heterogeneity and complexity, which reflect the underlying biology. Encoding the diffusion-driven displacements of water molecules, diffusion-weighted MRI (DWI) has the potential to provide quantitative information related to the structural changes due to the physiologic and/or pathologic transformations in tissue. The assumptions made to characterize the distribution of diffusion displacements, however, have a direct impact on the tissue-characteristic information extracted from the DWI data. Given the heterogeneous nature of biological tissues, particularly the tumors, water diffusion does not follow the classical Gaussian distribution. Recognizing this complexity, several non-Gaussian diffusion models have been suggested to probe the underlying tissue microstructures and environment. Among these, the continuous-time random walk model, its predecessor fractional-order calculus model, and the fractional motion model were developed to examine the spatiotemporal and/or stochastic characteristics of biological tissue through non-Gaussian diffusion dynamics. This talk will present theory, implementation, and validation of these new non-Gaussian DWI models; and discuss their clinical applications in diagnosis and prognosis of different types of cancer, such as brain tumor, gastric cancer, and breast cancer. Non-Gaussian diffusion MRI for assessing tumor tissue microstructures SEMINAR Bioengineering Engineering FOR LIFE
Transcript of Seminar Muge Karaman Mar2018Muge Karaman received her Ph.D. in Computational Sciences from Marquette...
Muge Karaman received her Ph.D. in Computational Sciences from Marquette University. She is a post-doc-toral research associate in the Center for Magnetic Resonance Research at the University of Illinois College of Medicine at Chicago. Her current research interests include development of non-Gaussian di�usion MRI methods and their applica-tion for characterizing tissue complexity and heterogene-ity, particularly for cancer diagnosis and treatment evaluation. She has published 11 peer-reviewed papers and 28 conference abstracts.
Muge KaramanPost-doctoral research associateCenter for Magnetic Resonance ResearchUniversity of Illinois College of Medicine at Chicago
Tuesday, March 13, 201811:00 a.m. - 12:00 p.m. 2240 DCL, 1304 W. Springfield Ave., Urbana
Tumor tissues exhibit a high degree of structural heterogeneity and complexity, which reflect the underlying biology. Encoding the di�usion-driven displacements of water molecules, di�usion-weighted MRI (DWI) has the potential to provide quantitative information related to the structural changes due to the physiologic and/or pathologic transformations in tissue. The assumptions made to characterize the distribution of di�usion displacements, however, have a direct impact on the tissue-characteristic information extracted from the DWI data. Given the heterogeneous nature of biological tissues, particularly the tumors, water di�usion does not follow the classical Gaussian distribution. Recognizing this complexity, several non-Gaussian di�usion models have been suggested to probe the underlying tissue microstructures and environment. Among these, the continuous-time random walk model, its predecessor fractional-order calculus model, and the fractional motion model were developed to examine the spatiotemporal and/or stochastic characteristics of biological tissue through non-Gaussian di�usion dynamics. This talk will present theory, implementation, and validation of these new non-Gaussian DWI models; and discuss their clinical applications in diagnosis and prognosis of di�erent types of cancer, such as brain tumor, gastric cancer, and breast cancer.
Non-Gaussian di�usion MRI for assessingtumor tissue microstructures
S E M I N A RBioengineering
Engineering FOR LIFE
