Competitive Revision to P41 EB017183 The Center for Advanced Imaging Innovation and Research (CAI2R) pursues a mission of bringing people together to create new ways of seeing. The work of our Center has been focused on creating new paradigms for the acquisition, reconstruction, and interpretation of biomedical images, and on implementing new collaboration models in order to translate these developments rapidly into clinical practice. In the proposed Competitive Revision, we will apply our experience in working with biomedical images and other signals to a new collaboration, aimed at the urgent need for COVID-19 testing. Parent Grant Summary The world of biomedical imaging is changing, and CAI2R has been at the forefront of that change. Tasks that were once the sole domain of meticulously-engineered imaging hardware are now beginning to be accomplished in software, increasingly informed by diverse arrays of inexpensive auxiliary sensors. Information once pursued through the laborious acquisition of carefully separated image datasets is now being derived from newly integrated, and richly quantitative, data streams. In keeping with these themes, our Center will be organized around the following four Technology Research and Development (TR&D) projects going forward: 1. Reimagining the Future of Scanning: Intelligent image acquisition, reconstruction, and analysis. 2. Unshackling the Scanners of the Future: Flexible, self-correcting, multisensor machines. 3. Enriching the Data Stream: MRI and PET in concert. 4. Revealing Microstructure: Biophysical modeling and validation for discovery and clinical care. Competitive Revision Summary With the appearance of COVID-19, the world changed suddenly. The need for definitive but also broadly available COVID-19 testing is clear, and is identified as a top priority in the Notice of Special Interest (NOT-EB- 20-008) to which this proposal responds. In this project, we will partner with colleagues in chemical engineering and virology to develop, evaluate, and deploy a new electrochemical device for multifaceted point- of-care or home-based COVID-19 testing. The device will use molecular surface imprinting to create a gold surface sensitive to SARS-CoV-2 spike proteins and other analytes of interest. Sensitive solid-state electronics will then detect the presence of these analytes in patient samples, ultimately allowing rapid and simultaneous assessment of COVID-19 infection, immunity and severity.
Specific Aims of the Competitive Revision are as follows: 1. Prototype. We will test whether a COVID-19 signal may already be obtained using our best current imprinting methods and electronic detection circuitry. 2. Characterize. We will use biobanked patient samples to establish sensitivity, specificity, and limits of detection (LOD) of our initial prototype for COVID-19, as opposed to other common viruses. 3. Optimize and iterate. Informed by Aims 1 and 2, we will develop optimized electronics, surface imprinting protocols, and measurement strategies to improve sensitivity and specificity. 4. Evaluate and distribute. We will test designs with promising performance prospectively in a cohort of subjects presenting for testing at NYU, and will compare results with standard RT-PCR COVID-19 testing, with an eye towards FDA approval, commercialization, and broader distribution.

Public Health Relevance

Competitive Revision to P41 EB017183 The Center for Advanced Imaging Innovation and Research (CAI2R) develops novel imaging techniques and technologies for the improved diagnosis and management of cancer, musculoskeletal disease, neurological disease and other disorders with a profound impact on human health. Our team of engineers, physicists, and clinicians has a track record of developing and disseminating new tools for the rapid, continuous, and comprehensive monitoring of health and disease. In this Competitive Revision, we will expand the scope of our Center, partnering with chemical engineers and virologists to develop, evaluate, and deploy a new electrochemical device for multifaceted point-of-care or home-based COVID-19 testing.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Biotechnology Resource Grants (P41)
Project #
3P41EB017183-06S1
Application #
10167922
Study Section
Program Officer
Liu, Guoying
Project Start
2020-07-20
Project End
2021-07-19
Budget Start
2020-07-20
Budget End
2021-07-19
Support Year
6
Fiscal Year
2020
Total Cost
Indirect Cost
Name
New York University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
121911077
City
New York
State
NY
Country
United States
Zip Code
10016
Feng, Li; Coppo, Simone; Piccini, Davide et al. (2018) 5D whole-heart sparse MRI. Magn Reson Med 79:826-838
Benkert, Thomas; Tian, Ye; Huang, Chenchan et al. (2018) Optimization and validation of accelerated golden-angle radial sparse MRI reconstruction with self-calibrating GRAPPA operator gridding. Magn Reson Med 80:286-293
Wake, Nicole; Chandarana, Hersh; Rusinek, Henry et al. (2018) Accuracy and precision of quantitative DCE-MRI parameters: How should one estimate contrast concentration? Magn Reson Imaging 52:16-23
Lee, Hong-Hsi; Sodickson, Daniel K; Lattanzi, Riccardo (2018) An analytic expression for the ultimate intrinsic SNR in a uniform sphere. Magn Reson Med 80:2256-2266
Lattanzi, Riccardo; Zhang, Bei; Knoll, Florian et al. (2018) Phase unwinding for dictionary compression with multiple channel transmission in magnetic resonance fingerprinting. Magn Reson Imaging 49:32-38
Madelin, Guillaume; Xia, Ding; Brown, Ryan et al. (2018) Longitudinal study of sodium MRI of articular cartilage in patients with knee osteoarthritis: initial experience with 16-month follow-up. Eur Radiol 28:133-142
Sbrizzi, Alessandro; Heide, Oscar van der; Cloos, Martijn et al. (2018) Fast quantitative MRI as a nonlinear tomography problem. Magn Reson Imaging 46:56-63
Lakshmanan, Karthik; Brown, Ryan; Madelin, Guillaume et al. (2018) An eight-channel sodium/proton coil for brain MRI at 3 T. NMR Biomed 31:
Winters, Kerryanne V; Reynaud, Olivier; Novikov, Dmitry S et al. (2018) Quantifying myofiber integrity using diffusion MRI and random permeable barrier modeling in skeletal muscle growth and Duchenne muscular dystrophy model in mice. Magn Reson Med 80:2094-2108
Hammernik, Kerstin; Klatzer, Teresa; Kobler, Erich et al. (2018) Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med 79:3055-3071

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