Bubble‐echo based deconvolution of contrast‐enhanced ultrasound imaging: Simulation and experimental validations
Bibliographic record
Abstract
PURPOSE: Improvement of both the imaging resolution and the contrast-to-tissue ratio (CTR) is a current emphasis of contrast-enhanced ultrasound (CEUS) for microvascular perfusion imaging. Based on the strong nonlinear characteristics of contrast agents, the CTRs have been significantly enhanced using various advanced CEUS methods. However, the imaging resolution of these methods is limited by the finite bandwidth of the imaging process. This study aimed to propose a bubble-echo based deconvolution (BED) method for CEUS with both improved resolution and CTR. METHOD: The method is built on a modified convolution model and uses novel bubble-echo based point-spread-functions to reconstruct the images by regularized inverse Wiener filtering. Performances of the proposed BED for three CEUS modes are investigated through simulations and in vivo perfusion experiments. RESULTS: BED of fundamental imaging was found to have the highest improvement in imaging resolution with the resolution gain up to 2.0 ± 0.2 times, which was comparable to the approved cepstrum-based deconvolution (CED). BED of second-harmonic imaging had the best performance in CTR with an enhancement of 9.8 ± 2.3 dB, which was much higher than CED. Pulse inversion BED had both a better resolution and a higher CTR. CONCLUSION: All results indicate that BED could obtain CEUS image with both an improved resolution and a high CTR, which has important significance to microvascular perfusion evaluation in deep tissue.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".