Impact of temporal resolution and motion correction for dynamic contrast-enhanced MRI of the liver using an accelerated golden-angle radial sequence
Bibliographic record
Abstract
This paper presents a prospective study evaluating the impact on image quality and quantitative dynamic contrast-enhanced (DCE)-MRI perfusion parameters when varying the number of respiratory motion states when using an eXtra-Dimensional Golden-Angle Radial Sparse Parallel (XD-GRASP) MRI sequence. DCE acquisition was performed using a 3D stack-of-stars gradient-echo golden-angle radial acquisition in free-breathing with 100 spokes per motion state and temporal resolution of 6 s/volume, and using a non-rigid motion compensation to align different motion states. Parametric analysis was conducted using a dual-input single-compartment model. Nonparametric analysis was performed on the time-intensity curves. A total of 22 hepatocellular carcinomas (size: 11-52 mm) were evaluated. XD-GRASP reconstructed with increasing number of spokes for each motion state increased the signal-to-noise ratio (SNR) (p < 0.05) but decreased temporal resolution (0.04 volume/s vs 0.17 volume/s for one motion state) (p < 0.05). A visual scoring by an experienced radiologist show no change between increasing number of motion states with same number of spokes using the Likert score. The normalized maximum intensity time ratio, peak enhancement ratio and tumor arterial fraction increased with decreasing number of motion states (p < 0.05) while the transfer constant from the portal venous plasma to the surrounding tissue significantly decreased (p < 0.05). These same perfusion parameters show a significant difference in case of tumor displacement more than 1 cm (p < 0.05) whereas in the opposite case there was no significant variation. While a higher number of motion states and higher number of spokes improves SNR, the resulting lower temporal resolution can influence quantitative parameters that capture rapid signal changes. Finally, fewer displacement compensation is advantageous with lower number of motion state due to the higher temporal resolution. XD-GRASP can be used to perform quantitative perfusion measures in the liver, but the number of motion states may significantly alter some quantitative parameters.
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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 | 0.006 |
| 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".