Lesion Imaging and Target Detection in Multiple Scattering (LITMUS) Media.


Journal

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
ISSN: 1525-8955
Titre abrégé: IEEE Trans Ultrason Ferroelectr Freq Control
Pays: United States
ID NLM: 9882735

Informations de publication

Date de publication:
11 2020
Historique:
pubmed: 2 5 2020
medline: 29 6 2021
entrez: 2 5 2020
Statut: ppublish

Résumé

We present an ultrasound algorithm [lesion imaging and target detection in multiple scattering (LITMUS)] suited to image lesions (hypoechoic) or targets (hyperechoic) in highly complex structures. In such media, standard ultrasound imaging techniques fail to detect lesions or targets due to aberrations and the loss of linearity between propagation distance and propagation time, caused by multiple scattering of ultrasound waves. The present algorithm (LITMUS) has the capability to predict the location as well as the size of such lesions/targets by using the multiple scattered ultrasound signals to its advantage. In this experimental and computational study, we use an ultrasound linear array. Lesions/targets are embedded at varying depths inside multiple scattering media with varying density of scatterers. In the simulations, plastic scatterers are used as the source of multiple scattering in a propagation medium (water). In the experiments, melamine sponges are used, with air alveoli as the scattering source. For multiple locations along the transducer, the incoherent backscattered intensity of the backscattered signals is extracted and the linear growth of the diffusive halo over time is tracked. Sudden changes in this growth indicate the presence of a region with reduced heterogeneity, indicative of the presence of a lesion/target. This methodology is combined with a depression detection algorithm to predict the size and location of the lesion/targets with high fidelity, despite the presence of strong heterogeneity and multiple scattering.

Identifiants

pubmed: 32356743
doi: 10.1109/TUFFC.2020.2990704
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

2281-2290

Subventions

Organisme : NCI NIH HHS
ID : R21 CA231503
Pays : United States

Auteurs

Articles similaires

Selecting optimal software code descriptors-The case of Java.

Yegor Bugayenko, Zamira Kholmatova, Artem Kruglov et al.
1.00
Software Algorithms Programming Languages
Humans Magnetic Resonance Imaging Phantoms, Imaging Infant, Newborn Signal-To-Noise Ratio
Humans Male Female Health Knowledge, Attitudes, Practice Middle Aged
Humans Female Male Retrospective Studies Middle Aged

Classifications MeSH