Automatic time-of-flight selection for ultrasound tomography

Abstract

Ultrasound sound-speed tomography requires accurate picks of time-of-flights (TOFs) of transmitted ultrasound signals, however, manual picking on large datasets is time-consuming. An improved automatic TOF picker is taught based on the Akaike Information Criterion (AIC) and multi-model inference (model averaging), based on the calculated AIC values, to improve the accuracy of TOF picks. The automatic TOF picker of the present invention can accurately pick TOFs in the presence of random noise with average absolute amplitude of up to 80% of the maximum absolute synthetic signal amplitude. The inventive method is applied to clinical ultrasound breast data, and compared with manual picks and amplitude threshold picking. Test results indicate that the inventive TOF picker is much less sensitive to data signal-to-noise ratios (SNRs), and performs more consistently for different datasets in relation to manual picking. The technique provides noticeably improved image reconstruction accuracy.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims priority from U.S. provisional patent application Ser. No. 60 / 901,903 filed on Feb. 16, 2007, incorporated herein by reference in its entirety.STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT[0002]This invention was made with Government support under Contract No.[0003]DE-AC52-06NA25396, awarded by the Department of Energy. The Government has certain rights in this invention.INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC[0004]Not ApplicableNOTICE OF MATERIAL SUBJECT TO COPYRIGHT PROTECTION[0005]A portion of the material in this patent document is subject to copyright protection under the copyright laws of the United States and of other countries. The owner of the copyright rights has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the United States Patent and Trademark Office publicly available file or records, ...

AI Technical Summary

Benefits of technology

[0012]A method and system is described for operator-independent selection of time-of-flight (TOF) ultrasound signals, such as for a clinical imaging system using ultrasound sound-speed tomography. The invention provides a robust and computationally efficient solution which can be applied to replacing operator selected time-of-flights in various ultrasound systems. The automatic TOF “picker” is based on the Akaike Information Criterion (AIC). A preferred embodiment of the invention method utilizes an approach termed multi-model inference (model averaging), which is based on calculating AIC values across range of weighted models toward improving the accuracy of TOF picks. In one aspect a median filter is afterward utilized to eliminate outliers in the TOF picks. In another aspect of the invention, if the sensor system is symmetrical, such as a ring, then the reciprocal nature of signals is compared with TOFs being adjusted accordingly, such as averaging the reciprocal signals which exceed certain boundary conditions.

[0013]The method and apparatus provides an operator-independent, computationally efficient, and robust picker, which can accurately and reliably pick time-of-flights for clinical ultrasound signals, even for those with low signal-to-noise ratios.

[0029]Another aspect of the invention is a time-of-flight selection method which uses a median filter to eliminate outliers in the TOF picks.

Problems solved by technology

These multi-path reflections interfere with proper image formation.

For example, a prolongation of time-of-flight can lead to overestimation of the target object depth within the body.

In addition, changes to the angle of incidence of the incoming sound signals cause aliasing in the calculated target object position.

However, this operator dependent process is very time-consuming when processing a large amount of ultrasound data, such as in the case of medical ultrasound tomography which may involve many thousand signals to be resolved.

Inaccurate time-of-fight picks can result in noisy reconstructed sound-speed images with erroneous information about tumors, leading to wrong cancer detection and diagnosis.

Images