title: Dynamic frame pairing in real-time freehand elastography.
abstract: Quasi-static ultrasound (US) elastography is now a well-established technique that involves acquiring US (RF/envelope) signals from an imaging plane before and after a small quasi-static compression to form axial strain elastograms (ASE). The image quality of the ASEs is a function of the applied axial strain. This relationship was extensively investigated and formalized in terms of strain filter in the literature. Most of the work in elastography formed elastograms by choosing pre- and post-compression frames separated by a desired compression strain. Although this approach is feasible in simulations and in vitro/in vivo experiments that involve controlled compression, it has been a challenge to do this during freehand compression in real time. In this work, we describe a one-prediction-one- correction method that dynamically selects pre- and post- compression frames to form an elastogram, based on the applied axial strain level. We validate the method using controlled compression experiments on phantoms and compare the performance of the dynamic frame pairing method against successive-frame pairing method in terms of the contrast-to-noise ratio (CNRe). Further, we demonstrate the advantages of the new method with the help of freehand acquired data from phantom experiments and in vivo breast data. The results demonstrate that the frame-pairing identified by the dynamic method matched the frame pairing that was designed to yield an applied axial strain of ~1%. The CNRe obtained by the traditional approach varied from as low as ~5 to as high as ~25, depending on the choice of skip number and compression rate. However, the dynamic frame pairing method provided elastograms with a CNRe that was consistently around ~20, irrespective of the compression rate. The results from analysis of 22 in vivo breast data demonstrated that the dynamic pairing method generated elastograms such that the frame-average axial strain (FAAS) of each frame in the cine-loop is consistently ~1% (0.011 ± 0.001).
authors.forename: Rongmin
authors.initials: R
authors.lastname: Xia
authors.forename: Guozhi
authors.initials: G
authors.lastname: Tao
authors.forename: Arun Kumar
authors.initials: AK
authors.lastname: Thittai
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003199:Computer Systems; D054459:Elasticity Imaging Techniques / Q000295:instrumentation / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D016217:Ultrasonography, Mammary / Q000295:instrumentation / Q000379:methods*