4D alignment of bidirectional dynamic MRI sequences
Bibliographic record
Abstract
Over the past decade, research in medical imaging and visualization has seen a progression from 2D to 3D and now 4D representation of medical data. 4D (3D +time) representation allows one to not only visualize a 3D structure, but also to study its functional movement. However, due to technological limitations no current acquisition modality acquires data in 4D. Instead, 4D data is created from multiple 3D or 2D acquisitions. In this paper we describe a method that spatio-temporally aligns dynamic 2D MRI sequences acquired in bidirectional, orthogonal planes (sagittal and coronal) for 4D visualization. As a proof of concept, we focus on the assessment of swallowing as a representative application of our work. MRI sequences with unknown temporal offsets are acquired through repeated instances of swallowing. We present a method that identifies common regions of interest (ROI) in the orthogonal sequences and combines image intensity profiles from the orthogonal ROIs, via registration to a fiducial volume, to align the MRI sequences. An interesting consequence of using bidirectional acquisition planes is that video sequences in the same acquisition view (e.g all sagittal sequences) automatically get aligned with respect to each other. The bidirectional alignment results are validated by comparing the sum of squared difference (SSD) values over all possible alignments of the sagittal sequences. Experimentally it is found that a minima in the SSD values corresponds to the alignment computed by our proposed method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".