Robust universal nonrigid motion correction framework for first‐pass cardiac MR perfusion imaging
Bibliographic record
Abstract
Purpose To present and assess an automatic nonrigid image registration framework that compensates motion in cardiac magnetic resonance imaging (MRI) perfusion series and auxiliary images acquired under a wide range of conditions to facilitate myocardial perfusion quantification. Materials and Methods Our framework combines discrete feature matching for large displacement estimation with a dense variational optical flow formulation in a multithreaded architecture. This framework was evaluated on 291 clinical subjects to register 1.5T and 3.0T steady‐state free‐precession (FISP) and fast low‐angle shot (FLASH) dynamic contrast myocardial perfusion images, arterial input function (AIF) images, and proton density (PD)‐weighted images acquired under breath‐hold (BH) and free‐breath (FB) settings. Results Our method significantly improved frame‐to‐frame appearance consistency compared to raw series, expressed in correlation coefficient (R 2 = 0.996 ± 3.735E‐3 vs. 0.978 ± 2.024E‐2, P < 0.0001) and mutual information (3.823 ± 4.098E‐1 vs. 2.967 ± 4.697E‐1, P < 0.0001). It is applicable to both BH (R 2 = 0.998 ± 3.217E‐3 vs. 0.990 ± 7.527E‐3) and FB (R 2 = 0.995 ± 3.410E‐3 vs. 0.968 ± 2.257E‐3) paradigms as well as FISP and FLASH sequences. The method registers PD images to perfusion T 1 series (9.70% max increase in R 2 vs. no registration, P < 0.001) and also corrects motion in low‐resolution AIF series (R 2 = 0.987 ± 1.180E‐2 vs. 0.964 ± 3.860E‐2, P < 0.001). Finally, we showed the myocardial perfusion contrast dynamic was preserved in the motion‐corrected images compared to the raw series (R 2 = 0.995 ± 6.420E‐3). Conclusion The critical step of motion correction prior to pixel‐wise cardiac MR perfusion quantification can be performed with the proposed universal system. It is applicable to a wide range of perfusion series and auxiliary images with different acquisition settings. Level of Evidence: 3 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1060–1072.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".