A new high definition zoom method based on compounded direct pixel beamforming for medical ultrasound imaging: preliminary results
Bibliographic record
Abstract
Conventional ultrasound imaging based on scan conversion suffers from blurring artifacts caused by interpolation[1]. Especially, when zooming an image for enlarging lesions during scan conversion (i.e., read-zoom), this blurring artifact becomes severe. To reduce blurring artifacts, a write-zoom method was previously proposed. However, it still presents blurring artifacts and lowers the frame rate due to increased line density. In this paper, a new high definition zoom method based on compounded direct pixel beamforming (CDPB) capable enhancing the detail and boundary of lesions is presented. The performance of the proposed method was evaluated with phantom and in vivo experiments by measuring the information entropy contrast (IEC). The radio-frequency channel data were acquired by using a 3.5-MHz convex array transducer with the SonixTouch research platform (Ultrasonix Medical Corp., Vancouver, BC, Canada). The enlarged images using a new high-definition zoom method based on CDPB (i.e., HDZ-CDPB) with 128 transmit scanlines were reconstructed along with read- and write zoom (RZ and WZ) images based on scan conversion by using 128 and 256 transmit scanlines, respectively. From the phantom experiments, the IEC value with the proposed HDZCDPB method was enhanced by maximally 42% and 29% compared to the RZ and WZ methods, respectively. This preliminary results indicate that the proposed HDZ-CDPB method would be useful for generating a high definition ultrasound zoom image with improved image quality compared to the conventional scan conversion based methods (i.e., RZ and WZ) while achieving the high frame rate.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".