Estimation of multipath transmission parameters for quantitative ultrasound measurements of bone.

When applying quantitative ultrasound (QUS) measurements to bone for predicting osteoporotic fracture risk, the multipath transmission of sound waves frequently occurs. In the last 10 years, the interest in separating multipath QUS signals for their analysis awoke, and led to the introduction of several approaches. Here, we compare the performances of the two fastest algorithms proposed for QUS measurements of bone: the modified least-squares Prony method (MLSP), and the space alternating generalized expectation maximization algorithm (SAGE) applied in the frequency domain. In both approaches, the parameters of the transfer functions of the sound propagation paths are estimated. To provide an objective measure, we also analytically derive the Cramér-Rao lower bound of variances for any estimator and arbitrary transmit signals. In comparison with results of Monte Carlo simulations, this measure is used to evaluate both approaches regarding their accuracy and precision. Additionally, with simulations using typical QUS measurement settings, we illustrate the limitations of separating two superimposed waves for varying parameters with focus on their temporal separation. It is shown that for good SNRs around 100 dB, MLSP yields better results when two waves are very close. Additionally, the parameters of the smaller wave are more reliably estimated. If the SNR decreases, the parameter estimation with MLSP becomes biased and inefficient. Then, the robustness to noise of the SAGE clearly prevails. Because a clear influence of the interrelation between the wavelength of the ultrasound signals and their temporal separation is observable on the results, these findings can be transferred to QUS measurements at other sites. The choice of the suitable algorithm thus depends on the measurement conditions.