Adaptive attenuation correction during H-scan ultrasound imaging using K-means clustering

Haowei Tai, Mawia Khairalseed, Kenneth Hoyt

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

H-scan ultrasound (US) imaging (where the ‘H’ stands for Hermite) is a novel non-invasive, low cost and real-time technology. Like traditional US, H-scan US suffers from frequency-dependent attenuation that must be corrected to have acceptable image quality for tissue characterization. The goal of this research was to develop a novel attenuation correction method based on adaptive K-means clustering. To properly isolate these signals, a lateral moving window approach applied to adaptively adjust GH filters based on the changing of RF vector spectrums. Then the signal isolated via the same filter will be combined together via overlap-add technology to keep the information loss minimum. Experimental data was collected using a Verasonics 256 US scanner equipped with a L11-4v linear array transducer. In vivo data indicates that H-scan US imaging after adaptive attenuation correction can optimally re-scale the GH kernels and match to the changing spectrum undergoing attenuation (i.e. high frequency shift). This approach produces H-scan US images with more uniform spatial intensity and outperforms global attenuation correction strategies. Overall, this approach will improve the ability of H-scan US imaging to estimate acoustic scatterer size and will improve its clinical use for tissue characterization when imaging complex tissues.

Original languageEnglish (US)
Article number105987
JournalUltrasonics
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

attenuation
filters
linear arrays
scanners
frequency shift
transducers
acoustics
estimates
scattering

Keywords

  • Adaptive K-means clustering
  • Attenuation correction
  • H-scan US imaging
  • Overlap-add
  • Tissue characterization
  • Ultrasound imaging

ASJC Scopus subject areas

  • Acoustics and Ultrasonics

Cite this

Adaptive attenuation correction during H-scan ultrasound imaging using K-means clustering. / Tai, Haowei; Khairalseed, Mawia; Hoyt, Kenneth.

In: Ultrasonics, 01.01.2019.

Research output: Contribution to journalArticle

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