Prisma fit mri system

Manufactured by Siemens

Sourced in Germany

The Prisma-Fit MRI system is a magnetic resonance imaging (MRI) device manufactured by Siemens. It is designed to capture detailed images of the human body for diagnostic purposes. The Prisma-Fit MRI system utilizes strong magnetic fields and radio waves to generate clear, high-resolution images that can be used by healthcare professionals to assess various medical conditions.

Automatically generated - may contain errors

Lab products found in correlation

Trio mri system, by Siemens (1 mentions)

Close spelling variants of this product

Prisma MRI system Prisma Fit Prisma system Prisma

4 protocols using prisma fit mri system

MRI data preprocessing protocol for fMRI analysis

Check if the same lab product or an alternative is used in the 5 most similar protocols

The MRI data were collected on 3 T MRI systems (Experiment 1: Trio MRI system, Experiment 2: Prisma-Fit MRI system; Siemens) using 12-channel (Experiment 1) and 32-channel (Experiment 2) head coils. Anatomical images were acquired using T1-weighted sequences (Experiment 1: MPRAGE sequence, FoV = 256 mm, TR = 2530 ms, TE = 1.74 ms, 176 slices, flip angle = 7˚, voxel size = 1.0 × 1.0 × 1.0 mm³; Experiment 2: MEMPRAGE sequence, FoV = 256 mm; TR = 2530 ms; TE = 1.69 ms; 176 slices; flip angle = 7˚; voxel size = 1.0 × 1.0 × 1.0 mm³). Functional images were acquired using T2*-weighted gradient echo-planar imaging sequences (Experiment 1: FoV = 220 mm, TR = 1780 ms, TE = 24 ms, 37 descending slices, flip angle = 70˚, voxel size = 3.0 × 3.0 × 3.5 mm³; Experiment 2: FoV = 220 mm; TR = 1200 ms; TE = 30 ms; 51 slices; flip angle = 65˚; voxel size = 2.5 × 2.5 × 2.5 mm³).
We used SPM12 (Wellcome Department of Imaging Neuroscience, London, UK) to analyze the MRI data. The first two volumes of each run were removed to allow for scanner equilibration. Functional images were first converted from DICOM to NIFTI and then preprocessed with the following steps: de-spiking, slice-timing correction, realignment, segmentation, coregistration, and normalization. The functional images were smoothened with 6 mm full-width-half-maximum (FWHM) Gaussian kernels.

Yeon J., Shekhar M, & Rahnev D. (2020). Overlapping and unique neural circuits are activated during perceptual decision making and confidence. Scientific Reports, 10, 20761.

+ Open protocol

+ Expand

BOLD Imaging Preprocessing in SPM12

Check if the same lab product or an alternative is used in the 5 most similar protocols

We collected the BOLD signal data on a 3T MRI system (Prisma-Fit MRI system; Siemens) using a 32-channel head coil. We acquired anatomical images using T1-weighted sequences (MEMPRAGE sequence, FoV = 256 mm; TR = 2530 ms; TE = 1.69 ms; 176 slices; flip angle = 7°; voxel size = 1.0 × 1.0 × 1.0). We acquired functional images using T2*-weighted gradient echo-planar imaging sequences (FoV = 220 mm; slice thickness = 2.5, TR = 1200 ms; TE = 30 ms; 51 slices; flip angle = 65°; voxel size = 2.5 × 2.5 × 2.5, multi band factor = 3, interleaved slices).
We used SPM12 (Wellcome Department of Imaging Neuroscience, London, UK) to analyze the MRI data. Functional images were first converted from DICOM to NIFTI and then preprocessed with the following steps: de-spiking, slice-timing correction, realignment, segmentation, coregistration, and normalization. The functional images were smoothened with a 6 mm full-width-half-maximum (FWHM) Gaussian kernel.

Haddara N, & Rahnev D. (2024). Threat Expectation Does Not Improve Perceptual Discrimination despite Causing Heightened Priority Processing in the Frontoparietal Network. The Journal of neuroscience : the official journal of the Society for Neuroscience, 44(15).

+ Open protocol

+ Expand

Multimodal MRI Acquisition Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols

MRI scans were conducted on a 3T Prisma^fit MRI system (Siemens Healthineers, Erlangen, Germany) with the following sequences: (1) T1-weighted 3D imaging using a magnetization prepared rapid gradient echo (MPRAGE) sequence with TR/TI/TE = 2300/900/2.26 ms, FOV = 256 × 256 mm², parallel imaging factor = 2, 1 × 1 × 1 mm³ voxels. (2) T2-weighted fluid attenuated inversion recovery (T2-FLAIR) sequence with TR/TI/TE = 9000/2500/92.0 ms, FOV = 220 × 220 mm², parallel imaging factor = 2, voxel size 0.9 × 0.9 × 2 mm³. (3) DKI using bipolar diffusion weighted echo planar imaging (EPI) with 3 b-values (0, 1000, 2000 s/mm²) along 64 diffusion encoding directions, voxel size 2.5 × 2.5 × 2.5 mm³, TR/TE = 4600/85 ms, FOV = 220 × 220 mm².

Benitez A., Jensen J.H., Thorn K., Dhiman S., Fountain-Zaragoza S., Rieter W.J., Spampinato M.V., Hamlett E.D., Nietert P.J., de Fatima Falangola M, & Helpern J.A. (2022). Greater diffusion restriction in white matter in Preclinical Alzheimer’s disease. Annals of neurology, 91(6), 864-877.

+ Open protocol

+ Expand

3T MRI Acquisition and Preprocessing Protocol

Check if the same lab product or an alternative is used in the 5 most similar protocols

Haddara N, & Rahnev D. (2023). Threat expectation does not improve perceptual discrimination despite causing heightened priority processing in the frontoparietal network. bioRxiv.

+ Open protocol

+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!