Alignment and calibration of high frequency ultrasound (HFUS) and optical coherence tomography (OCT) 1D transducers using a dual wedge-tri step phantom
Bibliographic record
Abstract
This paper introduces a novel alignment and calibration method for high frequency ultrasound (HFUS) and optical coherence tomography (OCT) 1D transducers. 2D images are constructed by means of translation of the transducers using a linear motor stage. Physical alignment of the transducers is needed in order to capture images of the same crosssectional plane, and calibration is needed to determine the relative coordinates of the images, including the image skew. A dual wedge-tri step phantom is created for both alignment and calibration. This phantom includes two symmetrical wedges and three steps that provide the user with visual feedback on how well the scan plane is aligned with the midplane of the phantom. The phantom image consists of five line segments, each of which corresponds to one of the wedges or steps. The slopes and positions of the lines are extracted from the image and compared with the phantom model. The scan plane parameters are found so that the difference between the model and extracted features is minimized. The main advantage of this phantom is that only one frame is required to determine translations, orientations, and skew parameters of the scan plane with respect to the phantom. Experimental results with ocular imaging show the ability to achieve alignment based on this method and its potential for medical applications.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".