Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 270

The DOI matches its Crossref record.

Coded excitation for diagnostic ultrasound: a system developer's perspective

Chiao, R.Y., Xiaohui Hao·2004·IEEE Symposium on Ultrasonics, 2003

Abstract

Resolution and penetration are primary criteria for clinical image quality. Conventionally, high bandwidth for resolution was achieved with a short pulse, which results in a tradeoff between resolution and penetration. Coded excitation extends the bounds of this tradeoff by increasing SNR through appropriate coding on transmit and decoding on receive. Although used for about 50 years in radar, coded excitation was successfully introduced into commercial ultrasound scanners only within the last 5 years. This delay is at least partly due to practical implementation issues particular to diagnostic ultrasound, which are the focus of this paper. After reviewing the basics of bi-phase and chirp coding, we present simulation results to quantify tradeoffs between penetration and resolution under frequency-dependent attenuation, dynamic focusing, and nonlinear propagation. Next we compare chirp and Golay code performance with respect to image quality and system requirements, and finally show clinical images that illustrate the current applications of coded excitation in B-mode, harmonic, and flow imaging.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/ultsym.2003.1293440

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cited by

See all 270

Metadata

Authors
Chiao, R. Y., & Hao, X.
Venue
IEEE Symposium on Ultrasonics, 2003
Publisher
IEEE
Published
2004-08-13
Pages
437-448
OpenAlex
W4251346909
Cited by
270
References
0
FWCI
58.98
Type
conference paper
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Coded excitation for diagnostic ultrasound: a system developer's perspective. https://app.cochat.ai/papers/10.1109/ultsym.2003.1293440
The paper
Chiao, R. Y., & Hao, X. (2004). Coded excitation for diagnostic ultrasound: a system developer's perspective. IEEE Symposium on Ultrasonics, 2003. https://doi.org/10.1109/ultsym.2003.1293440

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.