Parallel imaging enhanced MR colonography using a phantom model
Bibliographic record
Abstract
PURPOSE: To compare various Array Spatial and Sensitivity Encoding Technique (ASSET)-enhanced T2W SSFSE (single shot fast spin echo) and T1-weighted (T1W) 3D SPGR (spoiled gradient recalled echo) sequences for polyp detection and image quality at MR colonography (MRC) in a phantom model. Limitations of MRC using standard 3D SPGR T1W imaging include the long breath-hold required to cover the entire colon within one acquisition and the relatively low spatial resolution due to the long acquisition time. Parallel imaging using ASSET-enhanced T2W SSFSE and 3D T1W SPGR imaging results in much shorter imaging times, which allows for increased spatial resolution. MATERIALS AND METHODS: Using two porcine colon phantoms each with eight simulated 3-10-mm "polyps," baseline reference sequences acquired without ASSET (6-mm slices and readout bandwidth [BW] 62 kHz) were compared with 11 SSFSE and 8 SPGR sequences acquired with 2-fold ASSET acceleration. ASSET-enhanced SSFSE and SPGR sequences comprised BW/matrix combinations ranging from 20-62 kHz/256-352x256, respectively, with slice thicknesses adjusted from 3.0 to 4.5 mm to maintain a 23-26-second acquisition time and 30 cm slab thickness. Two experienced radiologists viewed the datasets in a randomized, blinded fashion. RESULTS: Compared to reference sequences, ASSET-enhanced SSFSE and SPGR sequences facilitated better polyp detection and had similar overall image quality and per-phantom specificity. The two best ASSET-enhanced SSFSE (3 and 4.5 mm slices, each with BW of 62.5 kHz and 352x256 matrices) and three best ASSET-enhanced SPGR BW/slice thickness/matrix combinations of 31 kHz/4.4 msec/192x256; 62/3.4/192x256; and 62/4.0/192x256, respectively, permitted detection of all polyps>or=5 mm. CONCLUSION: Parallel imaging using ASSET-enhanced T2W SSFSE and T1W 3D SPGR improves the ability to detect significant colon polyps in an MRC phantom model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".