Training in Quantitative MRI

Since its inception four decades ago, magnetic resonance imaging (MRI) has continued to evolve and is far from having reached its ultimate potential. MRI is unquestionably the most complex but also the richest and most versatile imaging method, therefore requiring systematic 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 limitation being the subjective nature of the result, i.e. sensitivity to reader experience and judgment.  An increasing number of problems in medicine require a quantitative assessment of tissue structure, physiology and function. Moreover, for many diagnostic or staging problems quantification 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, the assessment of non-focal systemic disorders such as degenerative neurologic or metabolic bone disease where a quantitative measurement of some structural or functional parameter has to be made.

Over the years MRI has become ever more complex with the ongoing emergence of new methodologies, providing increasingly detailed insight into tissue function. Many of these new methods are conceived and reduced to practice years before being implemented by equipment manufacturers. Successful participation in these developments demands in-depth, modality-specific training to enable future scientists to effectively deploy the myriad of mathematical tools for pulse sequence design and data reconstruction. Translation of new methods from the bench to the clinic is equally important and highlighted as one of NIH’s key priorities. Basic science trainees often understand the medical problem incompletely and typically have difficulties in translating abstract technical concepts to the practicing physician. The training process therefore needs to be multidisciplinary, requiring close cooperation among MR physicists, engineers, computer scientists and physicians in the various subspecialties.

Training in Structural, Physiological and Functional Imaging 

Supported by NRSA T32 EB020087

This program will train four predoctoral candidates in MRI physics and engineering, with particular focus on structural, physiologic and functional applications, for a period of two years.  Training modalities involve a combination of colloquia, structured teaching and hands-on laboratory training, and emphasis on preceptor-directed research. The training faculty consists of MR imaging and physician scientists with a record of successful multidisciplinary research training as well as basic and translational research excellence.

Predoctoral trainee candidates must have been accepted into a biomedical or bioengineering graduate program at the University of Pennsylvania. 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 US citizens or have permanent resident status.

The University of Pennsylvania is an equal opportunity employer and candidates of underrepresented minorities are particularly encouraged to apply. Interested candidates should contact the program director, Felix W. Wehrli, PhD. Applicants are requested to submit their curriculum vitae along with three letters of recommendation as email attachments to: felix.wehrli@pennmedicine.upenn.edu

Postdoctoral Position in Medical Imaging

The Laboratory for Structural, Physiologic and Functional Imaging (LSPFI) in the Department of Radiology at the University of Pennsylvania is seeking a highly qualified and ambitious candidate with a strong interest in magnetic resonance imaging to join our team. LSPFI’s research is aimed at quantitatively characterizing tissue properties and their relationship to physiology and function by means of spatially resolved magnetic resonance in humans.

Activities for this opportunity center on the further development of methods, and their translation to studies in patients by means of quantitative MRI procedures. Specific areas of concentration include design, implementation and translation to the clinic of methods for the study of 1) oxygen metabolism in the brain and other tissues; 2) solid-state MRI techniques for the study of skeletal mechanical competence and providing 3D renditions of skull and facial bones for surgical evaluation; 3) studies of the systemic vascular system in response to lifestyle and exposure to environmental toxins.

Candidates must have, or be near conferral of, an advanced degree (PhD or dual MD/PhD) in physics, physical chemistry, biomedical engineering, or electrical/electronic engineering and possess a strong research record. Hands-on MRI laboratory experience including pulse-sequence design, image reconstruction, and processing and analysis is preferred, but experience in other fields of imaging may be considered. Demonstrated professional level verbal and written English language skills are essential, as are significant interpersonal skills and the ability to assume a leading role in translational patient studies. 

Penn’s Department of Radiology, ranked #2 by the Blue Ridge Institute for Medical Research in overall NIH funding awarded to US medical school clinical science departments, derives its overall strength from a multidisciplinary focus, a close integration between basic science researchers and clinician-investigators, and blending of disease-oriented and basic science and technology programs. Radiology's Center for Advanced MR Imaging and Spectroscopy (CAMRIS) accommodates multiple whole-body MRI research-dedicated scanners operating at 1.5, 3 and 7T field strengths, designed for experimental and patient related studies.  

Stipend level will be at minimum commensurate with the post-graduate year NIH National Research Service Award (NRSA) policy levels. The University offers a generous postdoctoral benefits package including health insurance, sick leave and paid vacation. Please note that foreign nationals appointed to a postdoctoral trainee position should come to the University of Pennsylvania in an exchange visitor (J-1) status. 

For consideration, please submit the following to Professor Felix W. Wehrli (felix.wehrli@pennmedicine.upenn.edu):

  1. curriculum vitae with publication record
  2. brief description of research interests and future goals
  3. three references with contact information

 

 

Danielle S. Bassett, PhD

Complex systems, network science, computational neuroscience 

Christos Davatzikos, PhD

Biomedical image analysis, machine learning in imaging, imaging biomarkers of neurologic and neuropsychiatric diseases 

Edward J. Delikatny, PhD

Magnetic resonance imaging and spectroscopy, Cerenkov and near-infrared optical imaging, intraoperative imaging 

John A. Detre, MD

Physiological brain imaging, MRI/optical methods, clinical applications

Terence P. Gade, MD, PhD

Molecular imaging, image guided therapy, cancer biology

James C. Gee, PhD

Magnetic resonance image analysis and computing

Sarah Gullbrand, PhD

Musculoskeletal degeneration and repair, quantitative MRI, T1 and T2 mapping

Hao Huang, PhD

MRI-based brain parcellation, diffusion, multi-modal and perfusion MRI, modeling, animal MRI, clinical translation

Despina Kontos, PhD

Computational biomarker imaging 

Chamith S. Rajapakse, PhD

MRI-based image reconstruction, MSK imaging, computational biomechanics, deep learning, machine learning

Ravinder Reddy, PhD

Metabolic imaging, multinuclear MRI, CEST MRI and MRS, neurodegenerative and neuropsychiatric disorders, NAD+

Rahim Rizi, PhD

Functional, structural, and metabolic imaging tools for regional assessment of the lung, use of imaging for early detection and characterization of pulmonary disorders, lung transplant

Andrew Tsourkas, PhD

Molecular imaging, nanoparticles 

Jayaram Udupa, PhD

Image processing and analysis, artifical and hybrid intelligence and their applications

Ragini Verma, PhD

Diffusion tensor imaging, computational brain connectomics

Felix W. Wehrli, PhD

Quantitative characterization of tissue properties and their relationship to physiology and function via spatially resolved MRI in humans

Walter R.T. Witschey, PhD

Cardiac MRI, imaging technologies, artificial intelligence

Paul A. Yushkevich, PhD

Biomedical image analysis, neurodegenerative disease