Training in Quantitative Magnetic Resonance Imaging
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Institutional Training Grant (T32)
Rajapakse CS, Leonard MB, Bhagat YA, Sun W, Magland JF, Wehrli FW. Micro–MR Imaging–based Computational Biomechanics Demonstrates Reduction in Cortical and Trabecular Bone Strength after Renal Transplantation. Radiology 2012 Mar; 262(3):912-20.
Magnetic resonance imaging (MRI) has, since its inception over three decades ago, been by far the most complex but also the most versatile imaging technique since the possibilities by which the spin system can be manipulated are almost limitless. The technique’s complexity and extraordinary richness therefore require rigorous training. Although inherently quantitative, MRI has been used largely as a qualitative imaging technique practiced by radiologists utilizing predominantly qualitative criteria for establishing a diagnosis or excluding disease. This approach is fraught with problems, its main disadvantage being the subjective nature of the result, i.e. sensitivity to reader experience and judgment. Many problems in diagnostic medicine require a quantitative assessment. Moreover, for many diagnostic or staging problems quantitation of an observation is not merely a better option but the qualitative approach is entirely unsuited. Examples are measurement of tissue perfusion, quantification of metabolite concentration by spectroscopic imaging or the assessment of non-focal systemic disorders such as Alzheimer’s disease or metabolic bone disease where a quantitative measurement of some structural or functional parameter has to be made. A thorough understanding and working knowledge of physics and engineering principles is a paramount requirement to be successful as a biomedical MRI scientist.
Over the years the modality has become ever more complex with the ongoing emergence of new methodologies, providing increasingly detailed insight into tissue function, nanostructure and physiology. Many of these new methods are conceived and reduced to practice years before being implemented by equipment manufacturers. Active participation in these developments demands training at the forefront to be able to effectively handle the mathematical tools for pulse sequence design and data reconstruction. Quantitative approaches further require post-processing methods of arrays of images, typically performed off-line on workstations. Translation of new methods from the bench to the clinic is equally important and highlighted as one of NIH’s key priorities. The training process therefore needs to be multidisciplinary, requiring close cooperation among MR physicists, engineers, computer scientists and physicians in the various subspecialties. Basic science trainees typically lack an understanding of the medical problem and often have difficulties in translating abstract concepts to the practicing physician. This program trains two predoctoral and two postdoctoral basic science trainees in quantitative MRI methodology for a period of two years. Training modalities involve a combination of colloquia, structured teaching and hands-on laboratory training, with particular emphasis on preceptor-directed research. The training faculty consists of imaging scientists who have a record of successful multidisciplinary research training.
Felix W. Wehrli, Ph.D. Development of MRI-based methods for the visualization and analysis of tissue microarchitecture, physiology and function and their translation to the clinic
- Christos Davatzikos, Ph.D. Computational medical image analysis
- Jim Delikatny, Ph.D. New methods and imaging probes to study treatment in cancer by MRS and MRI
- John Detre, M.D. Imaging regional brain function by MR and other imaging modalities
- Charles L. Epstein, Ph.D. Pulse design based on inverse scattering
- James C. Gee, Ph.D. Development of methods for biomedical image analysis
- Jerry D. Glickson, Ph.D. In vivo NMR spectroscopy and imaging of tumors
- Ravinder Reddy, Ph.D. High-field MRI and MRS methods development
- Rahim Rizi, Ph.D. Functional and metabolic imaging of the lungs
- Mitchell Schnall, M.D., Ph.D. Development and evaluation of new breast cancer imaging techniques
- Hee Kwon Song, Ph.D. Development of dynamic imaging strategies in MRI
- Ragini Verma, Ph.D. Computational neuroanatomy and image analysis
- Rong Zhou, Ph.D. Biology-driven research that employs imaging technologies
The University of Pennsylvania has developed programs for underrepresented (UR) undergraduate and graduate students to promote interest and competence in biomedical science through hands-on laboratory experience, basic science instruction and other activities. These programs are fully integrated with University-wide programs for UR student education and with training programs within the School of Medicine (SOM) that support research training for all students.
Predoctoral trainee candidates must have been accepted into a biomedical or bioengineering graduate program at the University of Pennsylvania.
Postdoctoral candidates must have an advanced degree (Ph.D. or M.D./Ph.D.) in bioengineering, biophysics, physics, chemistry, or electrical engineering. Applicants with hands-on experience in MR imaging or spectroscopy data acquisition and processing methods, MRI hardware, or advanced applications are given preference but such prior training is not a requirement for being considered. NIH Training Grantee candidates must be U.S. citizens or have permanent resident status ("green card" holders).
The University of Pennsylvania values diversity and seeks talented students, faculty, and staff from diverse backgrounds. Women and candidates of underrepresented minorities are particularly encouraged to apply. Interested candidates should contact the Program Director.
Applicants are requested to submit their curriculum vitae along with three letters of recommendation as email attachments to:
Felix W. Wehrli, Ph.D.
Director, Laboratory for Structural NMR Imaging
Department of Radiology
University of Pennsylvania Medical Center
3400 Spruce Street
Philadelphia, PA 19104
Contact email: email@example.com