Clinical quantitative susceptibility mapping (QSM): biometal MRI
Abstract— QSM should be generated as a postprocessing whenever a gradient echo sequence is used in MRI. The gradient echo sequence should be included in an MRI protocol when a patient is suspected of neurodegeneration, inflammation, ischemia, hemorrhage, cancer, osteoporosis, and atherosclerosis, and whenever a contrast agent is injected.
Both phase and T2* weighted hypointensity in gradient echo MRI depend on surrounding susceptibility sources in a convoluted way, or they reflect only the “shadow” of magnetic tissue [1, 2]. Tissue susceptibility can be determined by deconvolving the MRI signal phase. The field-to-source ill-posed inverse problem can be solved using anatomic information readily available in MRI, which can guide the selection of a desired solution with zero streaking and shadow artifacts. This Bayesian approach has been developed by us to enable quantitative susceptibility mapping (QSM) [3, 4]. Current QSM technology is robust enough for routine study [5-8] of very strong isotropic magnetic susceptibility sources in tissue, which are biometals. The most abundant magnetic biometals in the body are iron, calcium, and contrast agent. We outline the following opportunities to develop these biometal QSM applications.
Iron in neurons. Iron (typically Fe2+) as a catalyst is required to participate in most cellular biochemistries in the body. Iron overload is invariably associated with neurodegenerative diseases, such as Parkinson’s disease, causing tissue damage through pathways involving oxidative stress. The vast majority of iron is stored (~99%) as Fe3+ mostly in ferritin, and only a very small fraction (~1%) is labile. The tight balance between stored iron concentration and bioactive labile iron concentration in each cell, as dictated by brain homeostasis, makes QSM iron storage measurement meaningful for studying neurodegenerative and other diseases. In particular, QSM is ideal for defining the deep brain stimulation target, subthalamic nucleus , which has rich iron due to its active generation of neurotransmitter glutamate. QSM may be critically useful in monitoring iron chelating therapy that is been developed for treating Parkinson’s disease.
Iron in hepatocytes. A major consequence for liver iron overload is fibrosis and eventual cirrhosis that inherently confound R2* and R2 measurements of liver iron. As fibrosis contributes little and linearly to magnetic susceptibility, QSM would play a critical role for accurate liver iron measurement in managing iron chelating dosage in transfusional iron overload patients.
Iron in immune cell macrophages. Iron is taken up by proinflammatory M1 macrophages as the immune system responds to pathogen invasion. In multiple sclerosis (MS) where the immune system attacks the central nerve system, M1 microglia in the MS lesion neighborhood cause chronic inflammation that can for the first time be detected on QSM as a hyperintense rim [10, 11].
Iron in red blood cells. QSM can be used to quantify deoxyheme, enabling practical mapping of cerebral metabolic rate of oxygen consumption (CMRO2). QSM provides quantitative study of hemorrhages, including microbleeds, that are involved in various pathological processes without blooming artifacts.
High concentration in calcification (hydroxyapatite) makes a robust QSM of diamagnetism. QSM promises to eliminate x-ray radiation for assessing bone strength, as well as bone marrow quality.
QSM solve contrast agent quantification problems in relaxation, enabling MRI quantification in drug delivery.
 Y. Wang, Principles of Magnetic Resonance Imaging: Physics Concepts, Pulse Sequences, & Biomedical Applications: CreateSpace Independent Publishing Platform, 2012.
 J. Li, et al., “Reducing the object orientation dependence of susceptibility effects in gradient echo MRI through quantitative susceptibility mapping,” Magn Reson Med, vol. 68, pp. 1563-9, Nov 2012.
 L. de Rochefort, et al., “Quantitative susceptibility map reconstruction from MR phase data using bayesian regularization: validation and application to brain imaging,” Magn Reson Med, vol. 63, pp. 194-206, Jan 2010.
 B. Kressler,et al, “Nonlinear regularization for per voxel estimation of magnetic susceptibility distributions from MRI field maps,” IEEE Trans Med Imaging, vol. 29, pp. 273-81, Feb 2010.
 E. M. Haacke, S. Liu, S. Buch, W. Zheng, D. Wu, and Y. Ye, “Quantitative susceptibility mapping: current status and future directions,” Magn Reson Imaging, vol. 33, pp. 1-25, Jan 2015.
 C. Liu, et al, “Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain,” J Magn Reson Imaging, vol. 42, pp. 23-41, Jul 2015.
 J. R. Reichenbach, F. Schweser, B. Serres, and A. Deistung, “Quantitative Susceptibility Mapping: Concepts and Applications,” Clin Neuroradiol, vol. 25 Suppl 2, pp. 225-30, Oct 2015.
 Y. Wang and T. Liu, “Quantitative susceptibility mapping (QSM): Decoding MRI data for a tissue magnetic biomarker,” Magn Reson Med, vol. 73, pp. 82-101, Jan 2015.
 T. Liu, et al., “Improved subthalamic nucleus depiction with quantitative susceptibility mapping,” Radiology, vol. 269, pp. 216-23, Oct 2013.
 Y. Zhang, et al., “Magnetic Susceptibility from Quantitative Susceptibility Mapping Can Differentiate New Enhancing from Nonenhancing Multiple Sclerosis Lesions without Gadolinium Injection,” AJNR Am J Neuroradiol, Jun 30 2016.
 C. Wisnieff, S. Ramanan, J. Olesik, S. Gauthier, Y. Wang, and D. Pitt, “Quantitative susceptibility mapping (QSM) of white matter multiple sclerosis lesions: Interpreting positive susceptibility and the presence of iron,” Magn Reson Med, vol. 74, pp. 564-70, Aug 2015.
*Research supported in part by NIH R01CA181566, R01NS072370, R01NS090464, and R01NS095562.
Yi Wang is with the Mennig School of Biomedical Engineering and Department of Radiology, Cornell University, New York, USA (corresponding author: 646-962-2640; e-mail: yiwang@ med.cornell.edu).
Professor Yi Wang (PhD 1994, University of Wisconsin-Madison) is the Faculty Distinguished Professor of Radiology of Professor of Biomedical Engineering at Cornell University. He is a Fellow of IEEE (Institute of Electrical and Electronics Engineers), ISMRM (International Society of Magnetic Resonance in Medicine), and AIMBE (American Institute for Medical and Biological Engineering). He has been elected to the Council of Distinguished Investigators of the Academy of Radiology Research. He has served as a scientific reviewer of grant applications for many agencies, including the National Institutes of Health (NIH), European Research Council, Research Grants Council of Hong Kong, Swiss National Science Foundation, and the Wellcome Trust of the United Kingdom. As a Principal Investigator, he has led many NIH projects on research and education, including researches on brain function, cancer, heart disease, multiple sclerosis, Parkinson’s disease, stroke, and vascular diseases. He has published 195 peer-reviewed journal papers, authored a textbook “Principles of Magnetic Resonance Imaging” and a monograph book “Quantitative susceptibility mapping: magnetic resonance imaging of tissue magnetism”, edited a book “Introductory medicine for engineers”, and co-authored a book “Electro-Magnetic Tissue Properties MRI”.
Professor Wang’s research interest has been in developing MRI technology for clinical applications. He has developed navigator motion compensation for cardiac MRI, which has become widely adopted in scientific research and clinical practice for cardiac and other MRI. He has pioneered time resolved contrast enhanced MRA, which has become a major tool for scientific and clinical investigations of cardiovascular diseases. He has pioneered multiple station stepping table platform for large field-of-view imaging as exemplified by bolus chase acquisition; the multi-coil array for multi-station as depicted in his patent has become Siemens’ flagship “Tim – Total image matrix” product. Recently, his group has introduced the quantitative susceptibility mapping (QSM) technique that has solved for the first time the field-to-susceptibility inverse problem using the Bayesian approach. QSM has broken ground for a new field in MRI for studying tissue magnetism, including iron, calcification, myelin, and contrast agents. QSM has potential applications for a wide range of inflammatory, ischemic and neurodegenerative diseases.