MRI of the Pulmonary Veins: Comparison Between 3D MR Angiography and T1-Weighted Spin Echo
Bibliographic record
Abstract
OBJECTIVE: The purpose was to determine the ability of three-dimensional (3D) magnetic resonance (MR) angiography to depict normal pulmonary veins in comparison with spin-echo MR imaging. MATERIALS AND METHODS: MR imaging of 40 patients with cardiovascular disease were reviewed. Patients with known pulmonary venous abnormalities were excluded. Using a standard GE 1.5-T magnet, axial T1-weighted spin-echo 5-mm-thick contiguous slices and 3D MR angiography (contiguous slice thickness of 2.5-3.5 mm, 20-30 c.c. of gadolinium bolus at 1-1.5 c.c./sec, 32-43-second breath-hold, coronal and sagittal plane acquisition) were evaluated retrospectively on separate occasions by two experienced radiologists. Multiplanar imaging projection was used for the identification of pulmonary veins. Each lung was considered to have two drainage veins: a superior vein and an inferior vein. Identification of a pulmonary vein was made by visualizing a connection with the left atrium. RESULTS: 143 pulmonary veins (87.5% +/-5.2) were identified at the level of the left atrium on T1-weighted spin-echo images, and 157 (98.1% +/-1.9) were identified on 3D MR angiography (p<0.01). Overall we identified by T -weighted spin-echo imaging 36 right upper, 38 right lower, 27 left upper, and 38 left lower pulmonary veins. By 3D MR angiography, we identified 38 right upper, 40 right lower, 39 left upper, and 40 left lower pulmonary veins. All four pulmonary veins were detected in 22 patients on spin-echo imaging (55%) and in 37 patients (92.5%) on 3D MR angiography (chi = 3.81, p<0.05). CONCLUSION: A significant difference is demonstrated between 3D MR angiography and spin-echo MR imaging in identifying normal pulmonary veins. MR angiography provides a complete view of normal pulmonary venous anatomy and could be a valuable tool for the assessment of abnormal pulmonary venous drainage.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".