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Title: Community action on FAIR data will fuel a revolution in materials research
Graphical abstract  more » « less
Award ID(s):
2039380 2022040 1931298
NSF-PAR ID:
10403853
Author(s) / Creator(s):
; ; ; ; ; ;
Publisher / Repository:
Cambridge University Press (CUP)
Date Published:
Journal Name:
MRS Bulletin
Volume:
49
Issue:
1
ISSN:
0883-7694
Format(s):
Medium: X Size: p. 12-16
Size(s):
["p. 12-16"]
Sponsoring Org:
National Science Foundation
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  1. Background

    Water content is a key parameter for simulating tissue swelling and nutrient diffusion. Accurately measuring water content throughout the intervertebral disc (NP = nucleus pulposus; AF = annulus fibrosus) is important for developing patient‐specific models. Water content is measured using destructive techniques, Quantitative MRI has been used to estimate water content and detect early degeneration, but it is dependent on scan parameters, concentration of free water molecules, and fiber architecture.

    Purpose

    To directly measure disc‐tissue water content using quantitative MRI and compare MRI‐based measurements with biochemical assays, and to quantify changes in disc geometry due to compression.

    Study Type

    Basic science, controlled.

    Specimen

    Twenty bone‐disc‐bone motion segments from skeletally mature bovines.

    Field Strength/Sequence

    7T/3D fast low angle shot (FLASH) pulse sequence and a T2rapid imaging with refocused echoes (RARE) sequence.

    Assessment

    Disc volumes, NP and AF volumetric water content, and T2relaxation times were measured through MRI; NP and AF tissue gravimetric water content, mass density, and glycosaminoglycan content were measured through a biochemical assay.

    Statistical Tests

    Correlations between MRI‐based measurement and biochemical composition were evaluated using Pearson's linear regression.

    Results

    Mechanical dehydration resulted in a decrease in disc volume by up to 20% and a decrease in disc height by up to 35%. Direct water content measurements for the NP was achieved by normalizing MRI‐based spin density by NP mass density (1.10 ± 0.03 g/cm3). However, the same approach underestimated water content in the AF by ~10%, which may be due to a higher concentration of collagen fibers and bound water molecules.

    Data Conclusion

    Spin density or spin density normalized by mass density to estimate NP and AF water content was more accurate than correlations between water content and relaxation times. Mechanical dehydration decreased disc volume and disc height, and increased maximum cross‐sectional area.

    Level of Evidence

    Technical Efficacy Stage

      J. Magn. Reson. Imaging 2020;52:1152–1162.

     
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  2. Background

    Clinical management of boys with Duchenne muscular dystrophy (DMD) relies on in‐depth understanding of cardiac involvement, but right ventricular (RV) structural and functional remodeling remains understudied.

    Purpose

    To evaluate several analysis methods and identify the most reliable one to measure RV pre‐ and postcontrast T1 (RV‐T1) and to characterize myocardial remodeling in the RV of boys with DMD.

    Study Type

    Prospective.

    Population

    Boys with DMD (N = 27) and age‐/sex‐matched healthy controls (N = 17) from two sites.

    Field Strength/Sequence

    3.0 T using balanced steady state free precession, motion‐corrected phase sensitive inversion recovery and modified Look‐Locker inversion recovery sequences.

    Assessment

    Biventricular mass (Mi), end‐diastolic volume (EDVi) and ejection fraction (EF) assessment, tricuspid annular excursion (TAE), late gadolinium enhancement (LGE), pre‐ and postcontrast myocardial T1 maps. The RV‐T1 reliability was assessed by three observers in four different RV regions of interest (ROI) using intraclass correlation (ICC).

    Statistical Tests

    The Wilcoxon rank sum test was used to compare RV‐T1 differences between DMD boys with negative LGE(−) or positive LGE(+) and healthy controls. Additionally, correlation of precontrast RV‐T1 with functional measures was performed. AP‐value <0.05 was considered statistically significant.

    Results

    A 1‐pixel thick RV circumferential ROI proved most reliable (ICC > 0.91) for assessing RV‐T1. Precontrast RV‐T1 was significantly higher in boys with DMD compared to controls. Both LGE(−) and LGE(+) boys had significantly elevated precontrast RV‐T1 compared to controls (1543 [1489–1597] msec and 1550 [1402–1699] msec vs. 1436 [1399–1473] msec, respectively). Compared to healthy controls, boys with DMD had preserved RVEF (51.8 [9.9]% vs. 54.2 [7.2]%,P = 0.31) and significantly reduced RVMi (29.8 [9.7] g vs. 48.0 [15.7] g), RVEDVi (69.8 [29.7] mL/m2vs. 89.1 [21.9] mL/m2), and TAE (22.0 [3.2] cm vs. 26.0 [4.7] cm). Significant correlations were found between precontrast RV‐T1 and RVEF (β = −0.48%/msec) and between LV‐T1 and LVEF (β = −0.51%/msec).

    Data Conclusion

    Precontrast RV‐T1 is elevated in boys with DMD compared to healthy controls and is negatively correlated with RVEF.

    Level of Evidence

    1

    Technical Efficacy

    Stage 2

     
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  3. Background

    Healthy articular cartilage presents structural gradients defined by distinct zonal patterns through the thickness, which may be disrupted in the pathogenesis of several disorders. Analysis of textural patterns using quantitative MRI data may identify structural gradients of healthy or degenerating tissue that correlate with early osteoarthritis (OA).

    Purpose

    To quantify spatial gradients and patterns in MRI data, and to probe new candidate biomarkers for early severity of OA.

    Study Type

    Retrospective study.

    Subjects

    Fourteen volunteers receiving total knee replacement surgery (eight males/two females/four unknown, average age ± standard deviation: 68.1 ± 9.6 years) and 10 patients from the OA Initiative (OAI) with radiographic OA onset (two males/eight females, average age ± standard deviation: 57.7 ± 9.4 years; initial Kellgren‐Lawrence [KL] grade: 0; final KL grade: 3 over the 10‐year study).

    Field Strength/Sequence

    3.0‐T and 14.1‐T, biomechanics‐based displacement‐encoded imaging, fast spin echo, multi‐slice multi‐echoT2mapping.

    Assessment

    We studied structure and strain in cartilage explants from volunteers receiving total knee replacement, or structure in cartilage of OAI patients with progressive OA. We calculated spatial gradients of quantitative MRI measures (eg, T2) normal to the cartilage surface to enhance zonal variations. We compared gradient values against histologically OA severity, conventional relaxometry, and/or KL grades.

    Statistical Tests

    Multiparametric linear regression for evaluation of the relationship between residuals of the mixed effects models and histologically determined OA severity scoring, with a significance threshold atα = 0.05.

    Results

    Gradients of individual relaxometry and biomechanics measures significantly correlated with OA severity, outperforming conventional relaxometry and strain metrics. In human explants, analysis of spatial gradients provided the strongest relationship to OA severity (R2 = 0.627). Spatial gradients of T2 from OAI data identified variations in radiographic (KL Grade 2) OA severity in single subjects, while conventional T2 alone did not.

    Data Conclusion

    Spatial gradients of quantitative MRI data may improve the predictive power of noninvasive imaging for early‐stage degeneration.

    Evidence Level

    1

    Technical Efficacy

    Stage 1

     
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  4. Background

    Deep learning (DL)‐based automatic segmentation models can expedite manual segmentation yet require resource‐intensive fine‐tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine‐tuning is not well characterized.

    Purpose

    Evaluate the generalizability of DL‐based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population.

    Study Type

    Retrospective based on prospectively acquired data.

    Population

    Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females).

    Field Strength/Sequence

    A 3‐T, quantitative double‐echo steady state (qDESS).

    Assessment

    Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)‐DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage.

    Statistical Tests

    Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank‐sum tests, root‐mean‐squared error‐coefficient‐of‐variation to quantify manual vs. automatic T2 and volume variations. Bland–Altman plots for manual vs. automatic T2 agreement. APvalue < 0.05 was considered statistically significant.

    Results

    DSCs for the qDESS‐trained model, 0.79–0.93, were higher than those for the OAI‐DESS‐trained model, 0.59–0.79. T2 and volume CCCs for the qDESS‐trained model, 0.75–0.98 and 0.47–0.95, were higher than respective CCCs for the OAI‐DESS‐trained model, 0.35–0.90 and 0.13–0.84. Bland–Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS‐trained model, ±2.4 msec and ±4.0 msec, than the OAI‐DESS‐trained model, ±4.4 msec and ±5.2 msec.

    Data Conclusion

    The qDESS‐trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population.

    Evidence Level

    1

    Technical Efficacy

    Stage 1

     
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  5. Background

    Carotid webs (CaWs) are fibromuscular projections in the internal carotid artery (ICA) that cause mild luminal narrowing (<50%), but may be causative in up to one‐third of seemingly cryptogenic strokes. Understanding hemodynamic alterations caused by CaWs is imperative to assessing stroke risk. Time‐Average Wall Shear Stress (TAWSS) and Oscillatory Shear Index (OSI) are hemodynamic parameters linked to vascular dysfunction and thrombosis.

    Purpose

    To test the hypothesis: “CaWs are associated with lower TAWSS and higher OSI than mild atherosclerosis or healthy carotid bifurcation.”

    Study Type

    Prospective study.

    Population

    A total of 35 subjects (N = 14 bifurcations with CaW, 11F, age: 49 ± 10, 10 mild atherosclerosis 6F, age: 72 ± 9, 11 healthy 9F, age: 42 ± 13).

    Field Strength/Sequence

    4D flow/STAR‐MATCH/3D TOF/3T MRI, CTA.

    Assessment

    4D Flow velocity data were analyzed in two ways: 1) 3D ROI in the ICA bulbar segment (complex flow patterns are expected) was used to quantify the regions with low TAWSS and high OSI. 2) 2D planes were placed perpendicular to the centerline of the carotid bifurcation for detailed analysis of TAWSS and OSI.

    Statistical Tests

    Independent‐samples Kruskal–Wallis‐H test with 0.05 used for statistical significance.

    Results

    The percent surface area where low TAWSS was present in the ICA bulb was 12.3 ± 8.0% (95% CI: 7.6–16.9) in CaW subjects, 1.6 ± 1.9% (95% CI: 0.2–2.9) in atherosclerosis, and 8.5 ± 7.7% (95% CI: 3.6–13.4) in healthy subjects, all differences were statistically significant (ƞ2 = 0.3 [95% CI: 0.05–0.5],P‐value CaW vs. healthy = 0.2). OSI had similar values in the CCA between groups (ƞ2 = 0.07 [95% CI: 0.0–0.2],P‐value = 0.5), but OSI was significantly higher downstream of the bifurcation in CaW subjects compared to atherosclerosis and normal subjects. OSI returned to similar values between groups 1.5 diameters distal to the bifurcation (ƞ2 = 0.03 [95% CI: 0.0–0.2],P‐value = 0.7).

    Conclusion

    Lower TAWSS and higher OSI are present in the ICA bulb in patients with CaW when compared to patients with atherosclerotic or healthy subjects.

    Evidence Level

    2

    Technical Efficacy

    Stage 2

     
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