The Neuroscience Section is focused on the development of novel MRI technologies and their application to basic science problems and clinical disease, especially in the brain. Methods have and are being developed for MRS and MRS imaging (MRSI); diffusion tensor imaging (DTI) and axonal mapping; physiological imaging (blood flow, blood volume, and blood oxygenation); and for the study of biochemical interactions using magnetization transfer processes.
Other research to improve the understanding of the blood oxygen level-dependent (BOLD) mechanism that underlies functional MRI, and the theory of MR relaxation in blood that underlies it is underway. Several faculty members of this large section are engaged in the development of new technologies for high-field MRI, including novel biodegradable contrast agents (sugar and proteins), molecular imaging markers, and new endogenous contrast agents for distinguishing tumors from healthy tissue.
Our Team
Division Chief
Kazi Akhter, B.S.
kakhter1@jhmi.edu
Adnan Bibic, Ph.D.
Bibic@kennedykrieger.org
Joe Gillen, B.S.
Research Associate
jgillen@mri.jhu.edu
Wenbo Li, Ph.D.
Assistant Professor
Deng Mao, Ph.D.
Research Associate
Feng Xu, Ph.D.
fxu6@jhmi.edu
Fellows
Students
Blake Dewey
Lin Chen
Kaihua Zhang
Caigui Lin
Chongxue Bie
MRI at Kirby Center
Terri Brawner
MRI Chief Tech at Kirby Center
Kathleen Kahl
MRI Tech
Ivana Kusevic
MRI Tech
Our Research Labs
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Peter van Zijl Laboratory
The Peter van Zijl Laboratory focuses on developing new methodologies for using MRI and magnetic resonance spectroscopy (MRS) to study brain function and physiology. In addition, we are working to understand the basic mechanisms of the MRI signal changes measured during functional MRI (fMRI) tests of the brain. We are also mapping the wiring of the brain (axonal connections between the brains functional regions) and designing new technologies for MRI to follow where cells are migrating and when genes are expressed. A more recent interest is the development of bioorganic biodegradable MRI contrast agents. Our ultimate goal is to transform these technologies into fast methods that are compatible with the time available for multi-modal clinical diagnosis using MRI.