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| Funder | NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES |
|---|---|
| Recipient Organization | New York University School of Medicine |
| Country | United States |
| Start Date | Feb 01, 2021 |
| End Date | Dec 31, 2022 |
| Duration | 698 days |
| Number of Grantees | 1 |
| Roles | Principal Investigator |
| Data Source | NIH (US) |
| Grant ID | 10104647 |
PROJECT SUMMARY Low back pain (LBP) is a major clinical and socioeconomic global health burden. In fact, LBP affects nearly all of us at least once in our lives, and in ~20% the condition becomes chronic.
The intervertebral disc (IVD) consists of a proteoglycan (PG)-rich nucleus pulposus (NP) surrounded by a collagenous annulus fibrosus (AF) that together provide support, transmit complex loads and motion of the spine. With aging, the IVD undergoes progressive and irreversible degenerative changes that often lead to LBP.
Several techniques have been utilized to characterize the IVD, using animal or human cadaver models.
While these studies can provide important data, internal disc mechanics may have different characteristics and quantifying the behavior of internal disc mechanics is technically challenging for in-vivo applications in humans.
Therefore, we hypothesize that MRI studies of the lumbar IVDs performed under different static mechanical loading states (e.g., rest, during loading and recovery), using a flexible dynamic Golden-angle Radial Sparse Parallel (GRASP) MRI technique, could be used to better quantify disc mechanics in-vivo.
The overarching goal of this R21 proposal is to develop a framework for non-invasive evaluation of in-vivo lumbar IVD mechanics (e.g., strain mapping in L1/L2-L5/S1) in response to MRI-compatible mechanical loading.
For this purpose, we will utilize a fast, flexible dynamic Golden-angle Radial Sparse Parallel (GRASP)-MRI acquisition, for quantitative assessment of biomechanical characterization of lumbar IVDs on a standard clinical 3T scanner in a clinically feasible scan time, employing compressed sensing (CS), parallel imaging (PI), golden angle radial sampling and an optical flow algorithm.
New York University School of Medicine
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