Data Quality Analyzer-Towards Optimal Radio-Frequency Frame Pair Selection for Ultrasound Elastography.

Quasi-static ultrasound elastography (USE) is a promising imaging technique for detecting malignancies by assessing tissue stiffness, but its accuracy heavily depends on the quality of radio-frequency (RF) frame pairs used for displacement estimation. A major challenge in quasi-static USE is signal decorrelation, which is primarily caused by out-of-plane motion during manual probe compression, leading to unreliable displacement fields and degraded elastography images. This paper introduces a novel, displacement estimator-agnostic method for assessing RF frame pair quality by measuring the similarity between the measured post-compression RF frame and a warped version of the pre-compression frame generated using the estimated displacement field. The proposed approach employs computationally efficient metrics such as mean squared error (MSE) and correlation, demonstrating robustness against signal decorrelation in both synthetic and clinical datasets. Additionally, we present a method to simulate realistic RF data corruption via controlled out-of-plane displacements, facilitating the development of robust motion-tracking algorithms. Validation using in silico phantoms, tissue-mimicking phantoms and clinical breast cancer and liver cancer cases confirm the method's efficacy in identifying high-quality frame pairs, significantly improving strain image accuracy. Threshold values of 1.4 and 0.5 were determined for MSE and correlation, respectively, as being effective to differentiate between good vs. bad RF data frame pairs. This work lays the foundation for automated frame selection in USE, enhancing its diagnostic reliability and clinical utility.