MRI to C‐arm spine registration through Pseudo‐3D CycleGANs with differentiable histograms
Bibliographic record
Abstract
PURPOSE: Image-guided spine surgery increasingly relies on diagnostic MRI for device navigation, as it allows to visualize the nerves and soft tissues during screw insertion in the pedicle region, which is not possible with preoperative CT or cone beam CT. However, registration of MRI to C-arm images remains difficult due to differences in visible tissue. METHODS: In this paper, we introduce a three-dimensional/two-dimensional (3D/2D) registration method of preoperative T2-weighted MRI of the lumbar spine to C-arm X-ray using synthetic CT images. The registration work is based on a pseudo-3D CycleGAN integrating a new cyclic loss function to ensure consistency in MRI and CT synthesis using differentiable histograms to match the multimodal distributions. The unified framework allows to improve bony tissue inference as opposed to regular 2D CycleGAN for image synthesis. A multiplanar digitally reconstructed radiograph (DRR) registration approach aligns the 3D and 2D images. RESULTS: Experiments performed on a public dataset of 18 pathological spines yielded a mean dice coefficient of 0.84 ± 0.015 on synthetic CTs. The DRR registration experiments, on the other hand, presented a target localization error of 2.1 ± 0.2mm. CONCLUSION: Intensity distributions and voxel-wise errors in Hounsfield units show encouraging results, illustrating the network's flexibility of producing qualitatively and quantitatively reasonable synthetic CT scans that can be used in a surgical 3D/2D registration framework. These promising results demonstrate the potential of the synthesis tool prior to integration in an image-guidance system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".