# Novel Quantitative Ultrasound methods for in-vivo histomorphology to grade early cartilage degeneration

> **NIH NIH R21** · RIVERSIDE RESEARCH INSTITUTE · 2020 · $152,835

## Abstract

Project Summary/Abstract
The overall goal of this proposed research is to develop and test new ultrasound backscattering models for future
quantitative-ultrasound (QUS) -based in-vivo screening and monitoring of early osteoarthritis (EOA).
Osteoarthritis (OA) is a joint disease that degenerates articular cartilage (AC) and is the most-prevalent joint
disease in the United States causing more than $60 billion in health-care costs each year. Currently, none of the
available non-invasive modalities is capable of assessing the early, symptomless stages of OA. By the time
symptoms become apparent, OA is usually advanced and cannot be reversed or halted, which limits effective
treatment. Therefore, a non-invasive tool that is capable of detecting early signs of cartilage degradation, such
as chondrocyte apoptosis, before the patient recognizes symptoms would lead to a paradigm shift in managing
osteoarthritis. Studies performed during the past decade indicate that QUS has great potential as such a tool.
Several spectral parameters from backscattered ultrasound have been shown to be sensitive to morphological
properties of articular cartilage that are related to early developmental stages of OA including cartilage-matrix
and cell-morphology parameters. In particular, our most-recent studies indicate that the structural organization
of hyaline cartilage in the knee joint is causing coherent scattering that may have a significant impact on QUS-
parameter estimation.
We will develop a novel and accurate model of the ultrasound backscatter coefficient (BSC) in human articular
cartilage. The model will be used to develop a QUS-based, multi-feature approach for classifying articular
cartilage and detecting EOA stages and can be implemented in current clinical scanners. We will use a
combination of numerical ultrasound simulations and ex vivo measurements to develop the new BSC models
and to define the optimal set of QUS parameters suitable as features for classifying cartilage-degradation stages.
The basis for the numerical ultrasound simulations will be a novel 3D acoustical and morphological model of
human hyaline cartilage. The model will allow us to test various OA-related cartilage properties independent from
each other and will help us to optimize the QUS estimates derived from ex vivo QUS measurements. We will
combine well established and novel QUS estimates and will develop new signal-processing approaches to
compute these parameters. Nonlinear classifiers for cartilage degeneration will be developed and optimized
using ROC methods, and will be tested on an existing data base of ultrasound data from histologically evaluated
OA patients. If this project is successful, subsequent projects will refine the developed tools and implement them
in a clinical scanner.

## Key facts

- **NIH application ID:** 9854890
- **Project number:** 5R21AR074668-02
- **Recipient organization:** RIVERSIDE RESEARCH INSTITUTE
- **Principal Investigator:** JONATHAN MAMOU
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $152,835
- **Award type:** 5
- **Project period:** 2019-01-25 → 2021-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9854890

## Citation

> US National Institutes of Health, RePORTER application 9854890, Novel Quantitative Ultrasound methods for in-vivo histomorphology to grade early cartilage degeneration (5R21AR074668-02). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/9854890. Licensed CC0.

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