Correcting organ motion artifacts in x‐ray CT medical imaging systems by adaptive processing. I. Theory
Bibliographic record
Abstract
X-ray CT scanners provide images of transverse cross sections of the human body from a large number of projections. During the data acquisition process, which usually takes about 1 s, motion effects such as respiration, cardiac motion, and patient restlessness produce artifacts that appear as blurring, doubling, and distortion in the reconstructed images, and may lead to inaccurate diagnosis. To address this problem several processing techniques have been proposed that require a priori knowledge of the motion characteristics. This paper proposes a method, which makes no assumptions about the properties of the motion, to eliminate the motion artifacts. The approach in this paper uses a spatial overlap correlator scheme to accurately track organ motion in computed tomography imaging systems. Then, it is shown that as optimum processing scheme to remove organ motion effects is to apply adaptive interference cancellation (AIC) methods, which treat the output of the spatial overlap correlator as noise interference at the input of the AIC process. Furthermore, an AIC method does not require any kind of periodicity of the motion effects. Synthetic data tests demonstrate the validity of this approach and show that hardware modifications are essential for its implementation in x-ray CT medical imaging systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".