Prediction and Reduction of Motion Artifacts in Free-Breathing Dynamic Contrast Enhanced CT Perfusion Imaging of Primary and Metastatic Intrahepatic Tumors
Bibliographic record
Abstract
RATIONALE AND OBJECTIVE: To develop and evaluate a method for predicting and reducing motion artifacts in free-breathing liver perfusion computed tomography (CT) scanning with couch shuttling and to compare tumor and liver parenchyma perfusion values. MATERIALS AND METHODS: Thirty patients (23 males, 7 females, median age of 74 years) with primary or metastatic intrahepatic tumors underwent dynamic contrast enhanced CT scans with axial shuttling. A semiautomatic respiratory motion correction algorithm was applied to align the acquired images along the z-axis. Perfusion maps were generated using the dual-input Johnson-Wilson model. Root mean squared deviation (RMSD) maps of the model fit to the pixel time-density curves were calculated. RESULTS: Precorrection RMSD correlated positively with magnitude of change in functional values resulting from motion. Blood flow, arterial blood flow, and permeability surface product were significantly increased in tumor compared to normal tissue (P < .05), blood volume was significantly reduced in tumor compared to normal tissue (P < .05). In a subgroup of patients with high-amplitude motion significant difference was observed between uncorrected and motion correction blood flow maps. CONCLUSIONS: Patients can breathe freely during hepatic perfusion imaging if retrospective motion correction is applied to reduce motion artifacts. RMSD provides a regional assessment of motion induced artifacts in liver perfusion maps.
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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 | 0.005 |
| 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) | 0.000 | 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 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".