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Sep 28: " Macromolecular Mapping with Magnetic Resonance Imaging—Beyond Free Water!" by Prof. Richard Spencer

By Christine Marie-Therese Darve posted 09-06-2023 18:23


LIVE from the USA, Prof. Richard Spencer, NIH, will map macromolecular stucture !

Title:" Macromolecular Mapping with Magnetic Resonance Imaging—Beyond Free Water!" by Prof. Richard Spencer

When: Thursday September 28th at 4 pm CET

 Register here

Now available: YouTube Recording


Richard Spencer, (NIH/NIA/IRP)

Richard Spencer is Chief of the Magnetic Resonance Imaging and Spectroscopy Section of the National Institute on Aging (NIA) of the National Institutes of Health, with a laboratory in Baltimore, Maryland, USA.  His group performs biophysical and physiological studies of human subjects, experimental animals, and tissue and cellular preparations. The current focus is on studies of brain and muscle in normative aging and in the setting of specific pathologies of particular interest in the aging population. The techniques applied range from conventional MRI protocols to specialized mathematical approaches to extract tissue information from the magnetic resonance signal. 
One of the ultimate goals of the work on the central nervous system is to identify potential therapeutic targets for cognitive decline. This involves evaluation of myelination patterns, cerebral blood flow, and spectroscopic markers of neuronal mass and biochemical status. Muscle studies center on quantifying diffusion, perfusion, and relaxation times in response to exercise, with an emphasis on normative aging and sarcopenia. Bioenergetic studies using phosphorus-31 magnetic resonance spectroscopy also form a substantial component of the muscle work.
Throughout, extensive use is made of the massive data set on normative aging available through the Baltimore Longitudinal Study on Aging, a major ongoing research initiative at the NIA.
Dr. Spencer is a Fellow of the American Physical Society, the American College of Physicians, the Norbert Wiener Center for Harmonic Analysis of the Department of Mathematics, University of Maryland, College Park, MD, and the American Institute for Medical and Biological Engineering (AIMBE).


Macromolecular mapping plays an essential role in biomedical and clinical research.  A prime example of this is the determination of myelination patterns in the central nervous system (CNS). Myelin is a protein- and lipid-rich substance that potentiates electrical impulse transmission along axons; disorders of myelin are central to certain pathologies, including multiple sclerosis, and implicated in many others, including Alzheimer’s disease. However, conventional MRI provides maps only of water, with adjustable contrast provided by relaxation or diffusion characteristics of the water within an imaging pixel. This can provide indirect information about macromolecular content, but these measurements are notoriously non-specific. A more direct measure of macromolecular content can be achieved by a multi-component mathematical analysis of the MRI signal to distinguish between relatively unbound water and water that is to a greater extent motion-constrained.  This is a much more complicated mathematical problem. Conventional MRI is a Fourier technique, with image reconstruction having the attractive property of being mathematically well-conditioned; noise in the data is transmitted to the image, but not magnified. In contrast, extraction of the macromolecular signal in MR relaxometry and related techniques is performed most often via the inverse Laplace transform, a form of the classically ill-posed inverse problem of solving the Fredholm equation of the first kind. This results in parameter estimates that can be extremely sensitive to noise. As a result, specialized methods must be undertaken to produce useful results. The inverse problems perspective has proven to be enormously fruitful in this setting. We will discuss this framework, and our applications to myelin mapping in the CNS and proteoglycan mapping in cartilage. The goals of our work are twofold: to improve the capacity of MR to evaluate tissue pathology, and to develop methods for application to inverse problems more generally.

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