The overall goal of this project is to incorporate advanced real-time feedback techniques into MRI protocols in order to increase reproducibility of MR-derived parameters and improve the accuracy and efficiency of a variety of clinical procedures at the University of Pennsylvania. While this proposal focuses on a few specific applications in the areas of structural micro-MRI of trabecular bone, real-time cardiac imaging, and functional neuroimaging, the candidate anticipates that the real-time feedback programming framework developed during the project will have a significant impact on other NIH funded research projects at Penn and elsewhere. The candidate, a mathematician with a background in computer science, has developed a graphical pulse sequence programming environment which has been fully interfaced to the Siemens whole-body scanners. This software, which has been used by students and researchers to develop dozens of research pulse sequences, will be the starting point for the proposed real-time feedback programming framework. Through collaborations with clinical and biomedical researchers at Penn, the candidate expects this technology to fill gaps in research techniques and ultimately have a significant positive impact on health care. Medical imaging is becoming increasingly important for the development and validation of new drugs, medical devices and procedures. Magnetic resonance imaging (MRI) is particularly suitable for these purposes because it is non-invasive and sensitive to a wide range of physiological processes. However, the modality also has a number of inherent challenges that often reduce the accuracy or reproduciblity of quantitative MRI-derived parameters (imaging biomarkers). Subject motion during a scan, inconsistent patient positioning, and other MRI-related problems can hinder the ability to detect small changes in biomarkers in response to treatment or disease progression. Real-time feedback techniques (including prospective motion compensation and automatic section prescription) can significantly improve the accuracy and precision of such measurements. However, due to the technical difficulty of implementing these advanced scanning techniques, real-time feedback capabilities are not available for many of the clinical procedures that could most benefit.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Mentored Quantitative Research Career Development Award (K25)
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Special Emphasis Panel (ZEB1-OSR-B (J1))
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Erim, Zeynep
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University of Pennsylvania
Schools of Medicine
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Zhang, Ning; Magland, Jeremy F; Song, Hee Kwon et al. (2015) Registration-based autofocusing technique for automatic correction of motion artifacts in time-series studies of high-resolution bone MRI. J Magn Reson Imaging 41:954-63
Li, Cheng; Magland, Jeremy F; Seifert, Alan C et al. (2014) Correction of excitation profile in Zero Echo Time (ZTE) imaging using quadratic phase-modulated RF pulse excitation and iterative reconstruction. IEEE Trans Med Imaging 33:961-9
Magland, Jeremy F; Childress, Anna Rose (2014) Task-correlated facial and head movements in classifier-based real-time FMRI. J Neuroimaging 24:371-8
Zhang, Ning; Magland, Jeremy F; Rajapakse, Chamith S et al. (2013) Assessment of trabecular bone yield and post-yield behavior from high-resolution MRI-based nonlinear finite element analysis at the distal radius of premenopausal and postmenopausal women susceptible to osteoporosis. Acad Radiol 20:1584-91
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Wright, Alexander C; Lemdiasov, Rostislav; Connick, Thomas J et al. (2011) Helmholtz-pair transmit coil with integrated receive array for high-resolution MRI of trabecular bone in the distal tibia at 7T. J Magn Reson 210:113-22
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Magland, J F; Wald, M J; Wehrli, F W (2009) Spin-echo micro-MRI of trabecular bone using improved 3D fast large-angle spin-echo (FLASE). Magn Reson Med 61:1114-21
Magland, Jeremy F; Wehrli, Felix W (2008) Trabecular bone structure analysis in the limited spatial resolution regime of in vivo MRI. Acad Radiol 15:1482-93