Philips Ingenia 3T (Netherlands) and Siemens Skyra 3T (Germany) MRI systems were used in this study. The subjects were in the supine position in the scanner, wearing earplugs and headphones, and were indicated to keep quiet and avoid head movement during the scan. They were further required to keep their eyes closed and remain awake during the resting-state scanning. T1-weighted sequence was used for structural image scanning (Philips: TR = 8.2 ms, TE = 3.8 ms, FA = 8°, FOV = 240 × 240, voxel size = 1 × 1 × 1 mm3, slice number = 170; Siemens: TR = 1,900 ms, TE = 3.4 ms, FA = 9°, FOV = 250 × 250, voxel size = 1 × 1 × 1 mm3, slice number = 192). Resting-state functional imaging adopts gradient-echo single-shot echo-planar imaging (EPI) sequence (Philips: TR = 2,000 ms, TE = 25 ms, FA = 75°, FOV = 224 × 224, slice number = 35, slice thickness = 4 mm, number of volumes = 240; Siemens: TR = 2,500 ms, TE = 30 ms, FA = 75°, FOV = 225 × 225, slice number = 41, slice thickness = 3.5 mm, number of volumes = 240). In addition, conventional T2WI, FLAIR, and DWI imaging data were collected to evaluate whether abnormal signals or organic lesions were present in the brain. Two subjects were excluded due to head motion and metal artifacts in structural image scanning.
Ingenia 3t
Manufactured by Siemens
Sourced in Netherlands
The Ingenia 3T is a magnetic resonance imaging (MRI) system designed and manufactured by Siemens. It is a high-field MRI scanner that operates at a magnetic field strength of 3 Tesla. The Ingenia 3T is primarily used for diagnostic imaging and research purposes in the medical field.
Automatically generated - may contain errors
3 protocols using ingenia 3t
1
Resting-State fMRI Acquisition with 3T MRI
2
MICCAI 2016 MS Lesion Segmentation Challenge
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The MICCAI 2016 MS lesion segmentation challenge (Commowick et al., 2018 (link)) was composed of 15 training scans acquired in three different scanner vendors: 5 scans (Philips Ingenia 3 T), 5 scans (Siemens Aera 1.5 T) and 5 scans (Siemens Verio 3 T). For each subject, 3D T1-w MPRAGE, 3D FLAIR, 3D T1-w gadolinium enhanced and 2D T2-w/Proton Density (PD) images were provided, with voxel sizes ranging from (0.74 × 0.74 × 0.7 mm3) to (0.72 × 0.72 × 4.0mm3). Please refer to the original publication for more details for the exact details of the acquisition parameters and image resolutions (Commowick et al., 2018 (link)). Manual lesion annotations for each training subject were provided as a consensus mask among 7 different human raters.
Pre-processed images were already provided. The pre-processing pipeline consisted of a denoising step with the NL-means algorithm (Coupé et al., 2008 (link)) and a rigid registration (Commowick et al., 2012 ) of all of the modalities against the FLAIR image. Then, each of the modalities were skull-stripped using the volBrain platform (Manjón and Coupé, 2016 ) and bias corrected using the N4 algorithm (Tustison et al., 2010 (link)). Finally, all the training images were also interpolated to (1 × 1 × 1 mm3) using the FSL-FLIRT utility (Greve and Fischl, 2009 (link)) in order to match the same voxel spacing between all the experiment datasets.
Pre-processed images were already provided. The pre-processing pipeline consisted of a denoising step with the NL-means algorithm (Coupé et al., 2008 (link)) and a rigid registration (Commowick et al., 2012 ) of all of the modalities against the FLAIR image. Then, each of the modalities were skull-stripped using the volBrain platform (Manjón and Coupé, 2016 ) and bias corrected using the N4 algorithm (Tustison et al., 2010 (link)). Finally, all the training images were also interpolated to (1 × 1 × 1 mm3) using the FSL-FLIRT utility (Greve and Fischl, 2009 (link)) in order to match the same voxel spacing between all the experiment datasets.
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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