Adaptive clutter filter design for micro-ultrasound color flow imaging of small blood vessels

摘要

In micro-ultrasound, which uses imaging frequencies above 20 MHz, obtainingcolor flow images (CFI) of small blood vessels using is not a trivial taskbecause it is more challenging to suppress tissue clutter properly given thestronger blood signal power at high imaging frequencies and the slow bloodvelocity inside the microcirculation. To improve clutter suppression inmicro-ultrasound CFI, this paper presents an adaptive clutter filtering approachthat is based on a two-stage eigen-analysis of slow-time ensemblecharacteristics. The approach first identifies tissue pixels in the imaging viewby examining whether high-frequency contents are absent in the principalslow-time eigen-components for each pixel as computed from single-ensembleeigen-decomposition. It then computes the filtered slow-time ensemble for eachpixel by finding the least-squares projection residual between the pixel'sslow-time ensemble and the clutter eigen-components estimated from amulti-ensemble eigen-decomposition of tissue slow-time ensembles within aspatial window. In this filtering approach, the clutter eigen-components arechosen based on whether their mean frequency lies within a spectral band. Toanalyze the efficacy of the proposed adaptive filter, both in-vitro experimentsand Field II simulations were carried out. For the experiments, raw CFI datawere acquired using a 64-element, 33 MHz linear array prototype (pulse duration:2 cycles, PRF: 1 kHz, transmit focus: 8mm, F-number: 5). Their imaging viewcorresponded to the cross-section of a 0.9mm-diameter tube that was placed ontop of an unsuspended table where ambient vibrations may appear; flow velocity(5, 7, 10, 15 mm/s) within the tube was controlled using a syringe pump. For thesimulations, raw CFI data was computed for both plug and parabolic flowprofiles, and tissue motion was modeled as 0.5 mm/s sinusoidal vibrations. Forall flow velocities tested in our in-vitro study, the proposed adaptive filterimproved the flow detection sensitivity as compared to existing ones. In theslow-flow case (5 mm/s), we observed over 70% increase in flow detectionsensitivity (assuming a 5% false alarm rate). This effectively reduced flashingartifacts in the resulting CFIs and gave a more consistent visualization of theflow tube. © 2010 IEEE.