title: Overdetermined least-squares aberration estimates using common-midpoint signals.
abstract: As medical ultrasound imaging moves to larger apertures and higher frequencies, tissue sound-speed variations continue to limit resolution. In geophysical imaging, a standard approach for estimating near-surface aberrating delays is to analyze the time shifts between common-midpoint signals. This requires complete data-echoes from every source/receiver pair in the array. Unfocused common-midpoint signals remain highly correlated in the presence of delay aberrations; there is also tremendous redundancy in the data. In medical ultrasound, this technique has been impaired by the wide-angle, random-scattering nature of tissue. This has made it difficult to estimate azimuth-dependent aberration profiles or to harness the full redundancy in the complete data. Prefiltering the data with two-dimensional fan filters mitigates these problems, permitting highly overdetermined, least-squares solutions for the aberration profiles at many steering angles. In experiments with a tissue-mimicking phantom target and silicone rubber aberrators at nonzero stand-off distances from a one-dimensional phased array, this overdetermined, fan-filtering algorithm significantly outperformed other phase-screen algorithms based on nearest-neighbor cross-correlation, speckle brightness maximization, and common-midpoint signal analysis. Our results imply that there is still progress to be made in imaging with single-valued focusing operators. It also appears that the signal-to-noise penalty for using complete data sets is partially compensated by the overdetermined nature of the problem.
authors.affiliation: Philips Research, Briarcliff Manor, NY 10510, USA. haunma@keteu.org
authors.forename: Mark A
authors.initials: MA
authors.lastname: Haun
authors.forename: Douglas L
authors.initials: DL
authors.lastname: Jones
authors.forename: William D
authors.initials: WD
authors.lastname: O'Brien
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001185:Artificial Intelligence; D003198:Computer Simulation; D003238:Connective Tissue / Q000000981:diagnostic imaging; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D016018:Least-Squares Analysis; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012542:Scattering, Radiation; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D014463:Ultrasonography / Q000295:instrumentation / Q000379:methods*