MRI acquisition and scanning parameters are described elsewhere (Casey et al., 2018 (link)) (see the Supporting Information Appendix S1 for details). Brain data were collected on 3T scanners (Siemens Prisma and Prisma Fit, General Electric MR 750, Philips Achieva dStream and Ingenia). The T1 images were corrected for gradient nonlinearity distortions using scanner-specific, nonlinear transformations. Cortical reconstruction and volumètric segmentation were performed by the Data Analysis, Informatics, and Resources Centre (DAIRC) of ABCD using FreeSurfer v5.3.0 (Dale, Fischl, & Sereno, 1999 (link)). The present study used post-processed structural (i.e., cortical volume, thickness, and surface area) data mapped to 34 cortical parcellations per hemisphere (68 total regions of interest) based on the Desikan-Killiany brain registration atlas (Desikan et al., 2006 (link)). For cortical volume, 8 subcortical segmentations per hemisphere were also investigated (16 regions in total) (Fischl et al., 2002 (link)). DAIRC used a combination of automated and manual methods to review the datasets for quality control prior to sharing data via the NDA database.
Prisma fit
Manufactured by GE Healthcare
Prisma Fit is a compact and versatile laboratory centrifuge from GE Healthcare. It is designed for a wide range of applications, including sample preparation, cell separation, and protein purification. The centrifuge features a high-performance motor and can achieve speeds up to 14,000 RPM, with a maximum RCF of 20,000 x g. The unit is equipped with a user-friendly control panel and can accommodate various rotor types to accommodate different sample volumes and tube sizes.
Automatically generated - may contain errors
Lab products found in correlation
Ingenia, by Philips (2 mentions)
Mr 750, by Philips (1 mentions)
Prisma, by GE Healthcare (1 mentions)
Discovery mr750, by Philips (1 mentions)
Achieva dstream, by Philips (1 mentions)
Prisma, by Siemens (1 mentions)
Signa architect 3t, by GE Healthcare (1 mentions)
Ingenia 3t, by Siemens (1 mentions)
3 protocols using prisma fit
1
Structural MRI Data Processing Pipeline
2
Harmonized Multisite MRI Imaging Protocol
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The ABCD Data Analysis and Informatics Center (DAIC) and the ABCD Imaging Acquisition Workgroup developed the imaging protocol used in the present study. These groups harmonized the imaging protocol for all scanner platforms. Imaging data was acquired across 21 sites using the following 3 tesla (3 T) scanner models: Siemens Prisma, Siemens Prisma Fit, General Electric Discovery MR750, Philips Achieva dStream, and Philips Ingenia. Imaging data collection occurred across one to two sessions. Further detail on these methods is documented elsewhere (Casey et al., 2018 (link); Hagler et al., 2019 (link)). Each session included T1‐ and T2‐weighted images of brain structure. Details on the imaging parameters include the following: TR (repetition time) 2400–2500 ms; TE (echo time) 2–2.9 ms; FOV (field of view) 256 × 240 to 256; FOV phase of 93.75%–100%; matrix 256 × 256; 176–225 slices; TI (inversion delay) 1060 ms; flip angle of 8°; voxel resolution of 1 × 1 × 1 mm; total acquisition time from 5 min 38 s to 7 min 12 s.
3
Radiomics Analysis of Flow Profiles
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Our analyses use data sets from three independent studies (Table 1 ). For the reproducibility assessment of the feature calculation, we consider eight healthy subjects from the travelling volunteer study presented by Demir et al. (19 (link)). Each volunteer was scanned on Philips (Ingenia 3T), Siemens (Prisma Fit) and GE (SIGNA Architect 3T) systems with the scan parameters as summarized in Table 1 .
The applicability of radiomics analysis to characterize flow profiles is tested with data from two additional independent studies. A dataset of patients with aortic stenosis (22 (link)) represents a cohort of pathological cases, while a dataset from a population study in the city of Freiburg, Germany (23 (link)) serves as control. We were able to include six additional cases in the latter dataset, which could not be analyzed in the initial study. None of the control subjects were diagnosed with valve diseases.
The applicability of radiomics analysis to characterize flow profiles is tested with data from two additional independent studies. A dataset of patients with aortic stenosis (22 (link)) represents a cohort of pathological cases, while a dataset from a population study in the city of Freiburg, Germany (23 (link)) serves as control. We were able to include six additional cases in the latter dataset, which could not be analyzed in the initial study. None of the control subjects were diagnosed with valve diseases.
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