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. 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. In each of these projects, we aim to push medical imaging technology to the next level, both in hardware and in software. Having made great strides in developing rapid, continuous imaging data streams, we will next aim to add key new information to those streams, both from physics-driven microstructural modeling and from data- driven machine learning. Having focused on the development of robust tools for image acquisition and reconstruction, we will extend the pipeline to image interpretation, using the results of human- or machine- derived evaluations of image content as feedback for the further improvement of acquisition strategies and sensor designs. We will also aim to close the loop between diagnostic sensing and therapeutic intervention, exploring new ways to guide therapy with continuously-acquired information about tissue bioeffects. Our Center has an explicit translational focus, which is reflected in the day-to-day operation of TR&D projects as well as in the topics of Collaborative Projects (CPs) and Service Projects (SPs), which are focused on three general areas of high public health impact: cancer, musculoskeletal disease, and neurologic disease. In keeping with this translational emphasis, CAI2R is also be driven by an embedded collaboration model in which basic scientists, clinicians, and industry developers sit down together regularly at the scanners for interactive technology development and assessment. With early involvement of clinical stakeholders and industry partners, we aim to make CAI2R technologies widely available, for the advancement of biomedical knowledge and for the benefit of patients and the physicians who care for them.

Public Health Relevance

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. By exploiting connections between imaging modalities such as MRI and PET, we aim to advance the fundamental capabilities of each, so as to expand biomedical knowledge and improve the care of patients.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Biotechnology Resource Grants (P41)
Project #
5P41EB017183-07
Application #
9996604
Study Section
Special Emphasis Panel (ZEB1)
Program Officer
Liu, Guoying
Project Start
2014-09-30
Project End
2024-07-31
Budget Start
2020-08-01
Budget End
2021-07-31
Support Year
7
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
Benkert, Thomas; Mugler 3rd, John P; Rigie, David S et al. (2018) Hybrid T2 - and T1 -weighted radial acquisition for free-breathing abdominal examination. Magn Reson Med 80:1935-1948
Ianniello, Carlotta; de Zwart, Jacco A; Duan, Qi et al. (2018) Synthesized tissue-equivalent dielectric phantoms using salt and polyvinylpyrrolidone solutions. Magn Reson Med 80:413-419
Paška, Jan; Cloos, Martijn A; Wiggins, Graham C (2018) A rigid, stand-off hybrid dipole, and birdcage coil array for 7 T body imaging. Magn Reson Med 80:822-832
Chen, Gang; Collins, Christopher M; Sodickson, Daniel K et al. (2018) A method to assess the loss of a dipole antenna for ultra-high-field MRI. Magn Reson Med 79:1773-1780
Piekarski, Eve; Chitiboi, Teodora; Ramb, Rebecca et al. (2018) Two-dimensional XD-GRASP provides better image quality than conventional 2D cardiac cine MRI for patients who cannot suspend respiration. MAGMA 31:49-59
Lattanzi, Riccardo; Wiggins, Graham C; Zhang, Bei et al. (2018) Approaching ultimate intrinsic signal-to-noise ratio with loop and dipole antennas. Magn Reson Med 79:1789-1803
Storey, Pippa; Gonen, Oded; Rosenkrantz, Andrew B et al. (2018) Quantitative Proton Spectroscopy of the Testes at 3 T: Toward a Noninvasive Biomarker of Spermatogenesis. Invest Radiol 53:87-95
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

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