A diffusion weighted data set of a healthy volunteer was acquired using a 3T Siemens MAGNETOM Prisma
Fit 57 . Next, Gibbs ringing correction based on local interpolation in k‐space was applied58 . The “Topup” 59 and “Eddy” 60 tools in FSL were used to correct for susceptibility, eddy current, and subject motion distortions.
Magnetom prisma fit system
Manufactured by Siemens
The MAGNETOM Prisma Fit system is a magnetic resonance imaging (MRI) scanner designed and manufactured by Siemens. It is a high-field MRI system capable of generating a strong magnetic field for the purpose of acquiring detailed images of the human body. The core function of the MAGNETOM Prisma Fit is to produce high-quality MRI scans that can be used for medical diagnosis and research purposes.
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Lab products found in correlation
3 protocols using magnetom prisma fit system
1
Diffusion-Weighted Imaging Protocol for Healthy Brain
2
Diffusion-Weighted MRI Preprocessing for Healthy Brain
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For the real data experiments in the subsections below, a diffusion weighted MRI data set of a healthy, 27‐year‐old male volunteer was acquired using a 3T Siemens MAGNETOM PrismaFit system. An EPI/spin echo (SE) diffusion weighted pulse sequence was used with a 96 × 96 acquisition matrix which resulted in an image with an isotropic voxel size of 2.5 mm. We acquired 36 slices with an inter slice gap of 30%, an echo time (TE) of
73 ms
ms / μ m 2 56 , 57 . Reversed phase encoded b = 0 images were acquired to allow for correction of susceptibility distortions. The total acquisition time was 12:56 min.
The first step in the post processing pipe‐line was the denoising of the dMRI data by exploiting its inherent redundancy using random matrix theory58 . Next, Gibbs ringing correction based on local interpolation in k‐space was applied 59 . Finally, the data was corrected for susceptibility, eddy current distortions, and subject motion using the “Topup” 60 and “Eddy” 61 tools in FSL.
The first step in the post processing pipe‐line was the denoising of the dMRI data by exploiting its inherent redundancy using random matrix theory
3
Optimized MRI Acquisition Sequences for Block Design and Resting-State Experiments
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We recorded data employed the block design visual stimulus paradigm using two sequences varying in resolution: Acquisition sequence 1 used the 3 T HCP protocol parameters (72 slices, TR = 0.8 s, MB = 8, no in-plane undersampling 2 mm isotropic, TE = 37 ms, flip angle = 52°, bandwidth = 2290 Hz/pixel). Acquisition sequence 2 parameters were 100 slices, TR = 2.1 s, MB = 4, in-plane undersampling factor = 2, 7/8 partial Fourier, 1.2 mm isotropic, TE = 32.6 ms, flip angle = 78°, bandwidth = 1595 Hz/pixel.
For the resting state data we used the acquisition sequence 1 detailed above (i.e. the 3 T HCP protocol).
For all acquisitions, flip angles were optimized to maximize the signal across the brain for the given TR. For each participant, shimming to improve B0 homogeneity over occipital regions was conducted manually.
T1-weighted anatomical images were obtained on a 3 T Siemens Magnetom Prismafit system using an MPRAGE sequence (192 slices; TR, 1900 ms; FOV, 256 × 256 mm; flip angle 9°; TE, 2.52 ms; 0.8 mm isotropic voxels). Anatomical images were used for visualization purposes and to define the cortical gray matter ribbon. This was done in BrainVoyager via automatic segmentation based on T1 intensity values and subsequent manual corrections. All analyses were subsequently confined within the gray matter.
For the resting state data we used the acquisition sequence 1 detailed above (i.e. the 3 T HCP protocol).
For all acquisitions, flip angles were optimized to maximize the signal across the brain for the given TR. For each participant, shimming to improve B0 homogeneity over occipital regions was conducted manually.
T1-weighted anatomical images were obtained on a 3 T Siemens Magnetom Prismafit system using an MPRAGE sequence (192 slices; TR, 1900 ms; FOV, 256 × 256 mm; flip angle 9°; TE, 2.52 ms; 0.8 mm isotropic voxels). Anatomical images were used for visualization purposes and to define the cortical gray matter ribbon. This was done in BrainVoyager via automatic segmentation based on T1 intensity values and subsequent manual corrections. All analyses were subsequently confined within the gray matter.
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