Quantitative Ultrasound Assessment of Early Osteoarthritis in Human Articular Cartilage Using a High-Frequency Linear Array Transducer

Characterization of the in vivo transient responses of the femoral cartilage by means of quantitative ultrasound imaging techniques

BACKGROUND

Quantitative ultrasound (QUS) techniques are new diagnostic tools able to identify changes in structural and material properties of the investigated tissue. For the first time, we evaluated the capability of QUS techniques in determining the in vivo transient changes in knee joint cartilage after a stressful task.

METHODS

An ultrasound scanner collecting B-mode and radiofrequency data simultaneously was used to collect data from the femoral cartilage of the right knee in 15 participants. Cartilage thickness (CTK), ultrasound roughness index (URI), average magnitude ratio (AMR), and Nakagami parameters (NA) were evaluated before, immediately after and every 5 min up to 45 min a stressful task (30 min of running on a treadmill with a negative slope of 5%).

RESULTS

CTK was affected by time (main effect: p < 0.001). Post hoc test showed significant differences with CTK at rest, which were observed up to 30 min after the run. AMR and NA were affected by time (p < 0.01 for both variables), while URI was unaffected by it. For AMR, post hoc test showed significant differences with rest values in the first 35 min of recovery, while NA was increased compared to rest values in all time points.

CONCLUSION

Data suggest that a single running trial is not able to modify the integrity of the femoral cartilage, as reported by URI data. In vivo evaluation of QUS parameters of the femoral cartilage (NA, AMR, and URI) are able to characterize changes in cartilage properties over time.

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator

The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose parameters are of physical meaning. To estimate the HK parameters is an inverse problem, and is quite complicated. Previously, we proposed an artificial neural network (ANN) estimator and an improved ANN (iANN) estimator for estimating the HK parameters, which are fast and flexible. However, a drawback of the conventional ANN and iANN estimators consists in that they use Monte Carlo simulations under known values of HK parameters to generate training samples, and thus the ANN and iANN models have to be re-trained when the size of the test sets (or of the envelope samples to be estimated) varies. In addition, conventional ultrasound HK imaging uses a sliding window technique, which is non-vectorized and does not support parallel computation, so HK image resolution is usually sacrificed to ensure a reasonable computation cost. To this end, we proposed a generalized ANN (gANN) estimator in this paper, which took the theoretical derivations of feature vectors for network training, and thus it is independent from the size of the test sets. Further, we proposed a parallelized HK imaging method that is based on the gANN estimator, which used a block-based parallel computation method, rather than the conventional sliding window technique. The gANN-based parallelized HK imaging method allowed a higher image resolution and a faster computation at the same time. Computer simulation experiments showed that the gANN estimator was generally comparable to the conventional ANN estimator in terms of HK parameter estimation performance. Clinical experiments of hepatic steatosis showed that the gANN-based parallelized HK imaging could be used to visually and quantitatively characterize hepatic steatosis, with similar performance to the conventional ANN-based HK imaging that used the sliding window technique, but the gANN-based parallelized HK imaging was over 3 times faster than the conventional ANN-based HK imaging. The parallelized computation method presented in this work can be easily extended to other quantitative ultrasound imaging applications.