Performance Evaluation of a 3D Ultrasound Imaging and Needle Tip Tracking System: A Comparative Study on Tone-Burst and Chirp Excitation
Bibliographic record
Abstract
Ultrasound is widely used in minimally invasive procedures to visualize anatomy and guide needle placement, which is critical for patient safety and surgical success. However, accurately visualizing the needle, particularly its tip, remains challenging. In this work, we present a novel system that combines interleaved 3D needle tip tracking and volumetric imaging using a fiber-optic hydrophone integrated within the needle cannula and a sparse spiral 2D array. The array probe was programmed to transmit a plane wave sequence for imaging and a subsequent sequence for single-element transmission for tracking, with the tracking data collected via the fiber-optic hydrophone. With each tracking sequence, an image of the needle tip was reconstructed using the hydrophone data. To enhance tracking performance, chirp excitation and pulse-compression were employed. Performance of the system was evaluated with a water phantom and chicken tissue ex-vivo experiments. The results demonstrate a spatial average tracking accuracy of 1.35 ± 0.38 (mean ± standard deviation) in water with chirp excitation. In chicken tissue insertion at a steep insertion angle, chirp excitation-based tracking reconstructed images achieved Contrast-to-Noise Ratio (CNR) of 18.98 ± 3.15 dB, outperforming tone-burst excitation (9.26 ± 3.40 dB). This paper presents a performance study of an advanced approach that integrates 3D volumetric ultrasound imaging with needle tip tracking. This combined embodiment holds promise for enabling real-time 3D visualization of patient anatomy and precise needle tip localization, potentially enhancing minimally invasive procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".