Least-error projection sorting to optimize retrospectively gated cardiac micro-CT of free-breathing mice
Bibliographic record
Abstract
PURPOSE: To develop and characterize a technique for optimizing image quality by eliminating streaking artifacts in retrospectively gated microcomputed tomography (micro-CT) images of mice caused by insufficient and irregular angular sampling. METHODS: A least-error sorting technique was developed to minimize streak artifacts in retrospectively gated cardiac micro-CT images. To ensure complete filling of projection space, for each angular position, the projection acquired closest to the desired cardiac phase is used to reconstruct a volumetric image. An acrylic slanted-edge phantom undergoing cyclic motion was used to characterize the system's resolution. The phantom was scanned using a volumetric micro-CT scanner equipped with a flat-panel detector mounted on a slip-ring gantry. Projection images of the moving phantom were collected over a period of 60 s using a variety of acquisition protocols with the rotation period of the gantry ranging from 1 to 5 s. The modulation transfer function (MTF) of the reconstructed images was measured for many combinations of acquisition and reconstruction parameters. The use of the least-error technique was also demonstrated in vivo. RESULTS: The motion blurring introduced into the images at physiologically significant velocities of 6 cm∕s agreed well with predicted values; limiting resolution (frequency at 10% MTF) degraded from 2.5 to 1.0 mm(-1) for a velocity of 6 cm∕s and 5 s∕rotation gantry speed. Faster gantry rotation speeds led to improved temporal resolution but the scanner's data storage and transfer rates and field of view limitations made scanning at gantry speeds faster than 2 s∕rotation impractical. CONCLUSIONS: The least-error technique effectively eliminates streaking artifact caused by missing views and allows for optimization of image quality in retrospectively gated micro-CT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".