New multi-modal ultrasound imaging methods for longitudinal analysis of tissue healing and early prognosis
- Institution
- TEXAS ENGINEERING EXPERIMENT STATION, TX
- Principal investigator
- Raffaella Righetti
- Funding
- $396K
- Duration
- 2025–2029
Moonbase score
Still being scored
Abstract
ABSTRACT This project aims at developing novel multi-modal imaging methods that can be used as predictive makers of tissue healing and to non-invasively monitor the tissue healing response. The proposed imaging methods are based on the use of safe, cost-effective and non-invasive ultrasound imaging methods. The acquisition of ultrasound signals from the tissue area of interest is typically fast and does not require anesthesia, radiations, or imaging contrast. Theoretical models and software techniques are then used to obtain images and measurements of the healing tissues. Ultrasound scanners are available worldwide, and the availability of small portable ultrasonic devices allows for prompt assessment “in the field” where other imaging methods may not be readily available. In this study, we propose to investigate new prognostic markers of tissue healing related to vascular and interstitial fluid transport mechanisms. In addition to biomechanical understanding of tissue regenerative mechanisms, these properties are important to assess the propensity of tissue healing and likely outcome of tissue repair. These prognostic markers are used in conjunction with novel 3D imaging methods to track spatial and temporal changes occurring in the healing tissues over time. Availability of these non-invasive, real-time and cost-effective imaging technologies can benefit several medical fields, such as oncology, cardiology, orthopedics and regenerative medicine. By providing new information about the mechanical, physical and biological behavior of newly formed tissues in vivo, the methods included in this proposal can be used as new tools to better understand tissue repair mechanisms and may be useful for guiding and monitoring treatments that can deliver effective and personalized care.
Research team
- Catherine G AmbrosePI
- Raffaella RighettiPI
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