Image quality improvement based on inter-frame motion compensation for photoacoustic imaging: A preliminary study
Bibliographic record
Abstract
Photoacoustic imaging (PAI) is capable of providing real-time anatomical and functional information of pathological changes. In PAI, the frame averaging (FA) technique is commonly used for enhancing signal-to-noise ratio (SNR) instead of increasing the power of a pulsed laser to avoid unwanted adverse effects. However, the FA technique suffers from motion artifacts due to the limited pulse repetition frequencies (PRFs) (e.g., 10-20 Hz) of a commonly-used Nd:Yag laser. In this paper, to improve the image quality of PAI, a new FA technique combined with inter-frame motion compensation (i.e., FA-IFMC) is proposed. The RF data acquisition was conducted with a commercial ultrasound imaging system (Ultrasonix Inc., Vancouver, BC, Canada) for the 0.9-mm diameter graphite lead-injected chicken breast. For the laser excitation, a Surelite Nd:YAG OPO system (Continuum Inc., Santa Clara, CA, USA) was used at the rate of 10 Hz and at the wavelength of 700nm with a bifurcated fiber bundle. From the phantom experiment, peak signal-to-noise ratio (PSNR) and the axial resolution were improved by 15.8 dB and 1.06 mm compared to the conventional FA method, respectively. These results indicate that the suggested FA-IFMC method can improve image quality of PAI by enhancing PSNR and axial resolution due to the lowerd blurring artifacts from motions.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".