In Vivo Super Resolution Ultrasound Imaging using the Erythrocytes - SURE
Bibliographic record
Abstract
Current super resolution imaging is conducted using ultrasound contrast agents, where a sparse distribution of bubbles must be employed to separate individual targets. The sparse targets make the acquisition time long in the range of 1 to 10 minutes, and therefore demands an accurate motion correction over a long time. The employment of a contrast agent also lowers MI to below 0.2 to not disrupt the bubbles, with a corresponding lower signal-to-noise ratio in the images. A new method, SURE (SUper Resolution ultrasound imaging using Erythrocytes), where erythrocytes are used as targets, is suggested to alleviate these problems. Perfused tissues contain an abundance of targets, and the full clinical pressure range can be used. It is hypothesized that super resolution imaging below the diffraction limit can be attained in seconds using SURE imaging. A SURE processing pipeline was developed with modules for beamforming, tissue motion estimation, alignment, singular value decomposition for echo canceling, and subsequent peak detection in the speckle pattern. The detected peaks were summed in a high-resolution image for yielding the SURE image. Data were acquired using a 10 MHz linear array GE L10-18i probe (150 µm wavelength) and a Verasonics Vantage 256 scanner. A synthetic aperture scan sequence with 12 emissions was employed at a pulse repetition frequency of 5 kHz for a 417 Hz frame rate. Kidneys of Sprague-Dawley rats were scanned for 24 seconds and RF data stored for off-line processing. The excised kidneys were micro-CT scanned for 11 hours for generating reference maps of the vasculature with a voxel size of 21 µm. SURE images revealed vessels with sizes down to 50 µm. Fourier ring correlations between independent images measured for 12 s revealed a resolution between 25 to 49 µm, demonstrating the super resolution capability of the method. The SURE images are obtained in 1 to 12 seconds, demand no injection of intravenous contrast agents, and can use the full pressure and intensity range allowed in medical ultrasound, making the method easily adaptable to clinical use.
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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 | 0.000 |
| 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 | 0.000 | 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".