Two structural images were acquired for each participant on a 3-Tesla MR750 GE scanner with a 32-channel head coil. For each participant, a high-resolution T1-weighted magnetization-prepared rapid acquisition gradient-echo sequence (MPRAGE; sagittal acquisition; TI/TE= 425/min; flip angle = 7°; FOV = 25.6; Matrix 256x256; Slice thickness = 1mm; bandwidth = 25HTz) and a T2-weighted fast relaxation fast spin-echo sequence was acquired (FRFSE-XL; sagittal acquisition; TR/TE 15000/80 ms; FOV = 25.6; Matrix 256x256; Slice thickness = 1mm; bandwidth = 31.25HTz) was acquired. Critically, both the T1 and T2 images were acquired with an FOV that allowed for imaging the neck of the participant. As a result, regions of the neck could also be incorporated into the head model used for source localization (see below).
Ge mr750 scanner
Manufactured by GE Healthcare
Sourced in United States
The GE MR750 scanner is a magnetic resonance imaging (MRI) system designed for clinical use. It provides high-quality imaging capabilities to aid in medical diagnosis and treatment. The MR750 scanner utilizes a superconducting magnet and advanced radio frequency (RF) technology to generate detailed images of the body's internal structures.
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9 protocols using ge mr750 scanner
1
Multimodal MRI Neuroimaging Protocol
2
Identifying Common Lesion Networks Using LNM
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To identify a common lesion network using the LNM method, we included a normal resting‐state functional imaging dataset that included 358 healthy young Chinese subjects (183 males; mean age = 19.39 ± 1.09 years; age range = 17.00–24.00 years; mean education years = 12.34 ± 0.81 years). The dataset was acquired with a 3.0 T MR750 GE Scanner using a gradient‐echo echo‐planar imaging (GRE‐EPI) sequence: TR = 2000 ms, TE = 30 ms, FA = 90°, FOV = 240 × 240 mm2, matrix = 64 × 64, voxel size = 3.75 × 3.75 × 4 mm3, and slices = 39. All the subjects were told to stay awake, close their eyes, and avoid movement during the scanning. More detailed data acquisition and preprocessing details can be found in our previous study (Liu et al., 2020 (link)).
Briefly, the following steps were applied: (1) removal of the first 10 timepoints and head motion correction; (2) rigid‐body registration of the T1 image to the EPI mean image; (3) normalization of the EPI images to MNI standard space using the T1 image and subsequent resampling to 3 × 3 × 3 mm3; (4) removal of noise, including the whole brain signals, head motions, and linear trends; (5) temporal filter (.01–.08 Hz); and (6) spatial smoothing using a 6‐mm full‐width at half‐maximum (FWHM) isotropic Gaussian kernel.
Briefly, the following steps were applied: (1) removal of the first 10 timepoints and head motion correction; (2) rigid‐body registration of the T1 image to the EPI mean image; (3) normalization of the EPI images to MNI standard space using the T1 image and subsequent resampling to 3 × 3 × 3 mm3; (4) removal of noise, including the whole brain signals, head motions, and linear trends; (5) temporal filter (.01–.08 Hz); and (6) spatial smoothing using a 6‐mm full‐width at half‐maximum (FWHM) isotropic Gaussian kernel.
3
Multimodal Neuroimaging Protocol
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Imaging was acquired during each visit with a 3-T MR750 GE scanner using a 32channel head coil. Each fMRI scan included five consecutive runs in the following order: anatomical scan (∼ 5 min); resting state (∼ 6 min), task (2 runs, ∼ 10 min each), and resting state (∼ 5 min). The full parameters are provided in the Supplemental Material.
for use under a CC0 license.
for use under a CC0 license.
4
Multimodal Neuroimaging Analysis Pipeline
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T1-weighted MR images, acquired on a 3T GE MR750 scanner (GE Medical Systems, Milwaukee, WI), were realigned, co-registered with PET images, and segmented into gray matter, white matter, and cerebrospinal fluid using SPM12 (Wellcome Department of Cognitive Neurology, University College London). Inverted co-registration parameters were obtained to transform ROIs from MR to PET space. The preselected regions of interest (ROIs) were hippocampus, VST, ACC, and Dorsal BrainStem (DBS). ROIs were defined using Freesurfer (5.0.0, http://surfer.nmr.mgh.harvard.edu/ ), except for the DBS ROI. The DBS ROI was defined using [11C]AZ10419369 template data in the standard reference space of the Montréal Neurological Institute35 and was transformed into individual space using FSL (FSL 5.0, Oxford)36 (link).
5
3T MRI Neuroimaging Protocol
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Images were acquired using a 3T GE-MR750 scanner with an 8-channel head coil at Peking University Sixth Hospital (Beijing, China). T2-weighted echoplanar images (EPI) were obtained with blood oxygenation level-dependent (BOLD) contrast. Thirty-three transverse slices of 4.2 mm thickness that covered the whole brain except the cerebellum were acquired using multiband EPI sequence in an interleaved order (repetition time = 2000 ms, echo time = 30 ms, field of view = 224 × 224 mm2, flip angle = 90°). A high-resolution T1-weighted image was acquired using an MPRAGE sequence (192 sagittal slices; voxel size 1 × 1 × 1 mm).
6
Multiparametric Breast MRI Protocol
7
Deep Learning MRI Image Reconstruction
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With IRB approval, multi-b-value DWI data were acquired from ten healthy subjects on a 3T GE MR750 scanner (GE HealthCare, Waukesha, WI, USA) using a single-shot EPI pulse sequence. The key acquisition parameters were: slice thickness = 5 mm, FOV = 22 cm × 22 cm, matrix = 256 × 256, slice number = 25, 14 b-values from 0 to 4000 s/mm2, and an acquisition time of ~6′30″. The acquired images were then transformed back to pseudo k-space and undersampled before being fed into the neural network. The undersampling mask pattern was a variable density pattern with the pseudo k-space center (24 lines) fully sampled. Seven datasets were used for training, two for validation, and one for testing. The datasets were also reconstructed with zero-padding and 3D-CNN for comparison. The experiment was performed with undersampling rates (R) of 4 and 6, respectively. The network was trained on a NVIDIA Titan Xp 64 GB graphics card.
8
High-Resolution Structural MRI Acquisition
9
Multi-site MRI Data Acquisition Protocol
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