Imaging contrast agent concentration and extracellular volume fraction in the right ventricle
Bibliographic record
Abstract
Globally increased myocardial extracellular volume fraction (ECVF) has been associated with diffuse myocardial fibrosis. ECVF can be estimated using blood and tissue concentrations of gadolinium contrast agent, [Gd], which are calculated using baseline and post-contrast T 1 values [ 1 ]. To date, T 1 quantification has been limited to the left ventricle (LV) with moderate spatial resolution (~2 mm) and long imaging windows (>200 ms) to accommodate breath-hold acquisitions. These methods have insufficient spatial resolution to image the relatively thin-walled right ventricle (RV). A new cine-imaging approach for the measurement of contrast agent concentration and ECVF using saturation-recovery preparation is evaluated in the LV and RV. A saturation-recovery gated-segmented cine SSFP sequence, similar to the multi-contrast late enhancement imaging method [ 2 ], provides a short acquisition window (< 50 ms) enabling end-systolic imaging and higher spatial resolution (~1 mm). Bloch equation simulations of the sequence were used to generate a look-up table to relate the measured ratio of post- to pre-contrast image intensity to the tissue concentration of contrast agent (CLAIR - Contrast Level Assessment using Intensity Ratios). Short axis images were acquired in 9 subjects from an ongoing study of heart failure (Alberta HEART), with contrast-enhanced images at 15 min post 0.15mmol/kg Gadovist. Typical CLAIR pulse sequence parameters: FOV=300mm, 256 matrix, 8 mm slice, flip angle=73°, TE=1.66ms, TR=3.32ms, VPS=14, TI=300ms. Average LV [Gd] in subjects was compared to values obtained using a saturation-recovery SSFP T 1 -mapping sequence [ 3 ] calculated using [Gd] = ΔR1/r (ΔR1 = change in 1/T 1 with contrast, r = relaxivity). For both methods ECVF = (1-Hct)*[Gd]Tissue/[Gd]Blood, with [Gd]Blood obtained via the T 1 mapping sequence and an assumed Hct of 0.4. Data are presented as mean±SD and differences compared with the two-tailed paired Student’s t-test. Subject age was 60.7±14.3yrs, with 5 males. Images from an individual using CLAIR (end-systole) and conventional T 1 -mapping (end-diastole) are shown in Fig. 1 . LV [Gd] is not statistically different between CLAIR and T 1 mapping (0.188±0.042 vs. 0.198±0.029 mM, p=0.151) and is significantly correlated between methods (p<0.01) (Fig. 2 left). LV ECVF is not statistically different between CLAIR and T 1 mapping (0.216±0.027 vs. 0.231±0.027, p=0.120) with negligible bias (Fig. 2 right) between the two methods. CLAIR RV [Gd] (0.212±0.066 mM) and ECVF (0.245±0.054) were not statistically different from LV values (p=0.134 and p=0.093). CLAIR image (left) at end-systole (1.17 mm resolution, 46 ms temporal resolution) and conventional T 1 -mapping image at end-diastole (right) (1.88 mm resolution, 225 ms temporal resolution) from the same subject (15 minutes post contrast). Comparison of CLAIR and conventional T 1 -mapping methods: LV contrast concentration (left) and Bland-Altman plot of extracellular volume fraction (right). CLAIR yields similar LV myocardial contrast concentration and ECVF in the LV to T 1 -mapping and provides sufficient temporal and spatial resolution for end-systolic RV imaging.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".