LAVA HyperSense and deep-learning reconstruction for near-isotropic (3D) enhanced magnetic resonance enterography in patients with Crohn’s disease: utility in noise reduction and image quality improvement
Bibliographic record
Abstract
This study aimed to compare near-isotropic contrast-enhanced T1-weighted (CE-T1W) magnetic resonance enterography (MRE) images reconstructed with vendor-supplied deep-learning reconstruction (DLR) with those reconstructed conventionally in terms of image quality. METHODSA total of 35 patients who underwent MRE for Crohn's disease between August 2021 and February 2022 were included in this retrospective study.The enteric phase CE-T1W MRE images of each patient were reconstructed with conventional reconstruction and no image filter (original), with conventional reconstruction and image filter (filtered), and with a prototype version of AIR TM Recon DL 3D (DLR), which were then reformatted into the axial plane to generate six image sets per patient.Two radiologists independently assessed the images for overall image quality, contrast, sharpness, presence of motion artifacts, blurring, and synthetic appearance for qualitative analysis, and the signal-to-noise ratio (SNR) was measured for quantitative analysis. RESULTSThe mean scores of the DLR image set with respect to overall image quality, contrast, sharpness, motion artifacts, and blurring in the coronal and axial images were significantly superior to those of both the filtered and original images (P < 0.001).However, the DLR images showed a significantly more synthetic appearance than the other two images (P < 0.05).There was no statistically significant difference in all scores between the original and filtered images (P > 0.05).In the quantitative analysis, the SNR was significantly increased in the order of original, filtered, and DLR images (P < 0.001). CONCLUSION Using DLR for near-isotropic CE-T1W MRE improved the image quality and increased the SNR. KEYWORDSCrohn's disease, MR enterography, image quality, deep learning, noise reduction C rohn's disease (CD) is a chronic bowel inflammatory disease characterized by transmural discontinuous asymmetric inflammation that affects the bowel wall and is frequently accompanied by extramural complications.1,2 Cross-sectional imaging plays an important role in CD diagnosis and monitoring.As CD often presents in young populations who require repeat imaging during their lifetimes, 3,4 magnetic resonance enterography (MRE) is preferred because of its high-contrast resolution, multiple imaging parameters, and lack of ionizing radiation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".