Characterization of the ultra-short echo time magnetic resonance (UTE MR) collagen signal associated with myocardial fibrosis
Bibliographic record
Abstract
The homogeneous distribution of collagen in diffuse myocardial fibrosis renders the disease unsuitable for imaging using late gadolinium enhancement (LGE) [ 1 ]. More recently, the estimation of extracellular volume from T 1 maps involving gadolinium agents has shown promise; however, these methods are not specific to collagen and are governed by gadolinium kinetics [ 2 ]. The diagnosis of diffuse myocardial fibrosis would benefit from an imaging method that can directly detect collagen. Notably, ultra-short echo time magnetic resonance (UTE MR) is a technique that can be used to detect short T 2 * species, including collagen [ 3 ]. Our objective is to characterize the UTE signal of protons in the collagen molecule, including their T 2 * and chemical shift. Direct isolation of a collagen signal could aid in the diagnosis of myocardial fibrosis, especially for diffuse distributions, and the assessment of disease extent. Collagen solutions of concentrations ranging from 0 % m/v to 50 % m/v were prepared by dissolving hydrolyzed type I and III collagen powder in 0.125 mM MnCl 2 , where the signal decay of MnCl 2 mimicked that of cardiac muscle. Each solution was scanned using a 3D UTE pulse sequence at 7 T, acquiring TEs from 0.02 ms to 25 ms, at a resolution of 0.781 mm isotropic. Upon fitting with a model of bi-exponential T 2 * with oscillation, the UTE collagen signal fraction was determined and calibrated against the collagen concentration. The T 2 * and resonance frequency (arising from the chemical shift) of collagen were assessed in collagen solutions. Validation of the collagen signal properties was also performed in formalin-fixed canine heart tissue, imaged with TEs from 0.02 ms to 25 ms, at a resolution of 0.156 mm isotropic. For collagen concentrations of 10 % to 50 %, the mean collagen T 2 * was 0.75 ± 0.05 ms, and the mean collagen frequency was 1.061 ± 0.004 kHz. A linear relationship (slope = 0.40 ± 0.01, R = 0.99696) was determined between the UTE collagen signal fraction associated with these characteristics and the measured collagen concentration (Figure 1 ). Similarly in canine heart tissue, a signal with T 2 * of 1.1 ± 0.3 ms and resonance frequency of 1.11 ± 0.02 kHz upfield of water was determined, consistent with collagen (Figure 2 ). The UTE collagen signal fraction of 1.2 ± 0.2 % in tissue corresponded to a collagen concentration of 2.3 ± 0.9 %, which was within the uncertainty of the collagen area fraction determined from histology (4 ± 2 %). UTE results in collagen solutions. (a) Collagen solution calibration plot, demonstrating a linear relationship between the UTE collagen signal fraction and the collagen concentration. m = slope, b = y-intercept, R = correlation coefficient, s r = standard deviation about the regression. (b) T 2 * decay of the 50 % collagen solution, fitted using a bi-exponential T 2 * model with oscillation. T 2 * long denotes the long T 2 * of MnCl 2 (mimicking cardiac muscle). Although not all long TEs were fitted, the focus was in the characterization of the short TEs ≤ 2 ms, where the T 2 * model is accurate. Histology and UTE results in canine heart tissue. (a) Histological slice of heart tissue, stained with Picrosirius Red. The 781.2 μm x 781.2 μm region-of-interest (ROI) used for analysis is delineated. The collagen area fraction in the ROI was determined to be 4 ± 2 %, based on a pixel threshold algorithm. (b) Corresponding UTE MR image at TE = 0.02 ms, with the ROI delineated. (c) T 2 * decay within the ROI. T 2 * long denotes the long T 2 * of cardiac muscle. TEs ≤ 2 ms were finely sampled to determine the collagen T 2 * and resonance frequency, where the T 2 * model is accurate. Based on the calibration plot in Figure 1a , the collagen signal fraction of 1.2 ± 0.2 % was equivalent to a collagen concentration of 2.3 ± 0.9 %. Hence, there was agreement between the collagen area fraction determined from histology (4 ± 2 %) and the collagen concentration. The results suggest that collagen associated with myocardial fibrosis can be endogenously detected and quantified using UTE MRI. This signal is specific to protons in collagen, characterized by a T 2 * of ~ 0.8 ms and a resonance frequency of ~ 1.1 kHz upfield of water at 7 T. Such properties would be beneficial in the determination of collagen content due to disease.
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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 | 0.001 |
| 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 | 0.001 |
| 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".