Landmark based compensation of patient motion artifacts in computed tomography
Bibliographic record
Abstract
We propose a new method for compensating patient motion in computed tomography. The method consists of analyzing the frequency spectrum of tracked landmark points in the acquired sinogram. These landmarks are either physically attached to the patient prior scanning, or coincide with anatomical points traceable in the sinogram. Without motion present, these extracted landmark curves represent one period of a sine wave in full-rotation tomography. Motion compensation is achieved by calculating the fundamental frequency component of the extracted landmark curves in order to obtain the motion compensated landmark curve. The extracted and compensated landmark curve pairs serve for constructing a motion map, and these are utilized to re-sort the acquired sinogram to obtain a motion compensated sinogram. Reconstructing the motion compensated sinogram using a standard reconstruction algorithm yields the motion compensated image. The proposed method is compared to a previously published motion artifact reduction method. The results show equal or improved motion compensation capabilities of the proposed method for three different types of computer simulated in-plane motion in synthetic 2D parallel-beam sinograms. The influence of inaccurate curve extraction has been simulated and results are included. Experiments aimed at demonstrating the compensation of motion artifacts in micro-computed tomography patient images are currently under way. In addition to compensation, future work will explore detection and quantification of patient motion based on frequency spectrum analysis of landmark curves, potentially providing a comprehensive tool for identifying, quantifying and correcting motion artifacts.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".