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 the modality 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. 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 often understand the medical problem incompletely and typically have difficulties in translating abstract technical concepts to the practicing physician. The proposed Training program builds on the director's earlier program and its record in terms of achieved training outcome, showing the large majority of former trainees having attained academic faculty or senior research positions in industry. The new program builds on this successful formula by proposing to train predoctoral and postdoctoral candidates in MRI physics and engineering, with particular focus on structural, physiologic and functional applications, for 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.

Public Health Relevance

The complexity of MRI, its multiparametric nature, and its myriad embodiments for the study of tissue structure, physiology and function, require systematic, modality-specific training. The proposed Program entails all aspects of training physics and engineering concepts for MRI image acquisition, reconstruction and processing, in combination with preceptor-directed research.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Institutional National Research Service Award (T32)
Project #
5T32EB020087-03
Application #
9459881
Study Section
Special Emphasis Panel (ZEB1)
Program Officer
Erim, Zeynep
Project Start
2016-04-01
Project End
2021-03-31
Budget Start
2018-04-01
Budget End
2019-03-31
Support Year
3
Fiscal Year
2018
Total Cost
Indirect Cost
Name
University of Pennsylvania
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
042250712
City
Philadelphia
State
PA
Country
United States
Zip Code
19104
Kim, Jason Z; Soffer, Jonathan M; Kahn, Ari E et al. (2018) Role of Graph Architecture in Controlling Dynamical Networks with Applications to Neural Systems. Nat Phys 14:91-98
Papadopoulos, Lia; Kim, Jason Z; Kurths, Jürgen et al. (2017) Development of structural correlations and synchronization from adaptive rewiring in networks of Kuramoto oscillators. Chaos 27:073115
DeBrosse, Catherine; Nanga, Ravi Prakash Reddy; Wilson, Neil et al. (2016) Muscle oxidative phosphorylation quantitation using creatine chemical exchange saturation transfer (CrCEST) MRI in mitochondrial disorders. JCI Insight 1:e88207
Wilson, Neil E; D'Aquilla, Kevin; Debrosse, Catherine et al. (2016) Localized, gradient-reversed ultrafast z-spectroscopy in vivo at 7T. Magn Reson Med 76:1039-1046