A low cost open source high frame-rate high-frequency imaging system
Bibliographic record
Abstract
In this paper a low cost open source approach to high-frequency ultrasound imaging is described. This complete imaging system is based around four core components: A single-element geometrically focused imaging transducer, a low cost high frame-rate mechanical scanner, a field programmable gate array (FPGA) controlled pulser-receiver unit, and a data acquisition system running open source interface software. The single-element imaging transducer is spherically curved composite based on Lithium Niobate that has a centre frequency of 45 MHz, a bandwidth of 65%, and an insertion loss of −19dB. The mechanical scanning mechanism is based on a 45 mm long PZT bimorph attached to an extension arm. The mechanism can scan up to a 10 mm displacements at 100 Hz and is driven with a low cost Arduino microcontroller. The mechanism is mounted in an enclosed probe holder filled with deionized water. The FPGA accurately controlling the variable timing of the pulser-receiver unit is a Xilinx Virtex V and the data acquisition hardware consists of an off the shelf AlazarTech PCIe digitizing card and a PC. The hardware communication, GUI/plotting libraries, and data collection is all controlled with an open source Python application we have named OpenHiFUS.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.013 |
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".