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3t scanner

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The 3T scanner is a high-field magnetic resonance imaging (MRI) system developed by Philips. It operates at a magnetic field strength of 3 Tesla, which enables the acquisition of high-resolution images of the human body.

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Lab products found in correlation

32 channel head coil, by Philips (6 mentions) Prisma, by GE Healthcare (4 mentions) 8 channel head coil, by Philips (3 mentions) 8 channel sense head coil, by Philips (3 mentions) 32 channel receive only head coil, by Philips (2 mentions) Hydrocel geodesic sensor net, by Electrical Geodesics (2 mentions) 1.5t system, by GE Healthcare (2 mentions) Eight channel phase array head coil, by Philips (2 mentions) Eight channel phased array head coil, by Philips (2 mentions) Eight channel radio frequency coil, by Philips (2 mentions) 8 channel sense phased array head coil, by Philips (2 mentions) Intera achieva, by Siemens (1 mentions) Boldscreen, by Cambridge Research Systems (1 mentions) Colorcal mkii colorimeter, by Cambridge Research Systems (1 mentions) Eight channel head coil, by Philips (1 mentions) Achieva 1.5t, by Philips (1 mentions) Brainsight, by Rogue Research (1 mentions) 8 channel phased array head coil, by Philips (1 mentions) Tomoscan sr7000, by Philips (1 mentions) 32 channel radiofrequency coil, by Philips (1 mentions)

120 protocols using 3t scanner

1

Diffusion MRI Acquisition Protocols

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Different scanners were utilized at the four clinical sites, including the General Electric 3T scanner (Columbia University), the Siemens 3T scanner (Massachusetts General Hospital), and the Philips 3T scanner (University of Texas Southwestern Medical Center and University of Michigan). All four clinical sites acquired diffusion MRI data with a voxel size of 1 × 1 × 1 mm3. The four clinical sites collected diffusion MRI data with different parameters. Specifically, the acquisition parameters were: Columbia University: 6 msec repetition time, 2.4 msec echo time, 9° flip angle, 5 min duration; Massachusetts General Hospital: 2.3 msec repetition time, 2.54 msec echo time, 9° flip angle, 4.3 min duration; University of Texas Southwestern Medical Center: 8 msec repetition time, 3.7 msec echo time, 12° flip angle, 4.24 min duration; University of Michigan: 8.1 msec repetition time, 3.7 msec echo time, 12° flip angle, 5.29 min duration.
Tong X., Zhao K., Fonzo G.A., Xie H., Carlisle N.B., Keller C.J., Oathes D.J., Sheline Y., Nemeroff C.B., Williams L.M., Trivedi M., Etkin A, & Zhang Y. (2024). Optimizing Antidepressant Efficacy: Multimodal Neuroimaging Biomarkers for Prediction of Treatment Response. medRxiv.
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2

Synchronous EEG-fMRI Acquisition Protocol

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The experiment with synchronous EEG-fMRI was conducted in Changzhou Second People's Hospital. Participants were scanned with a 3-T scanner (Philips Medical Systems) while wearing an EEG-Cap (HydroCel Geodesic Sensor Net; Electrical Geodesics, Inc., Eugene, OR). EEG data were collected by using Net Station software from 64 channels in 10-10 montages at the sampling rate of 250 Hz. Cz served as the reference. The impedance of all electrodes was kept below 50 kΩ. Functional MRI data acquisition (3-T scanner, Philips Medical System) was performed using a gradient echo EchoPlanar Imaging sequence [repetition time (TR) = 2,000 ms, echo time (TE) = 35 ms, flip angle = 90°, and voxel size = 3 × 3 × 3 mm]. A total of 24 continuous slices parallel to the anterior commissure-posterior commissure line were acquired per volume (field of view of 230 × 182 mm and matrix of 96 × 74). A structural MRI image was also collected from each participant with a voxel size of 1 × 1 × 1 mm. The EEG and fMRI data were synchronized using a synchronization box.
Li W., Zhang W., Jiang Z., Zhou T., Xu S, & Zou L. (2022). Source localization and functional network analysis in emotion cognitive reappraisal with EEG-fMRI integration. Frontiers in Human Neuroscience, 16, 960784.
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3

Multimodal Neuroimaging of Aging and Cognition

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We considered 23 participants from Vanderbilt Memory and Aging Project (VMAP) [12 (link)] and 25 from Baltimore Longitudinal Study of Aging (BLSA) [13 (link)]. The data covers 48 participants aged 55 to 86 years with 18 female and 22 with mild cognitive impairment. Both sites used single shell acquisitions but varied the gradient schemes. VMAP acquired 32 directions at a b-value of 700 s/mm2, and BLSA acquired 64 samples at a b-value of 1000 s/mm2, respectively. BLSA used a Philips 3T scanner at a resolution of 2.2 × 2.2 × 2.2 mm3 and resampled to 0.81 × 0.81 × 2.2 mm3. VMAP used a Philips 3T scanner at a resolution of 2 × 2 × 2 mm3.
DWI from all participants underwent preprocessing to remove eddy current, motion, and echo-planar imaging (EPI) distortions prior to any harmonization or model fitting [14 (link)].
Davis J, & Van Nostrand W.E. (1996). Enhanced pathologic properties of Dutch-type mutant amyloid beta-protein.. Proceedings of the National Academy of Sciences of the United States of America, 93(7), 2996-3000.
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4

MidRISH Harmonization of Multisite Diffusion Data

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To validate MidRISH at harmonizing data from the same study but different scanners, we analyzed 74 Alzheimer’s Disease Neuroimaging Initiative (ADNI) (adni.loni.usc.edu) patients split between GE 3T and SIEMENS 3T scanners, matched for sex (22 females from each) and age (72.7±6.7 years). Both had b-values of 1000 s/mm2 and were sampled at 48 directions and stored with 2 × 2 × 2 mm3 resolution. This cohort was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD [23 (link)].
For the cross-study analysis, we used sex-age-diagnosis matched data from Vanderbilt Memory and Aging Project (VMAP) and Baltimore Longitudinal Study of Aging (BLSA). We considered 117 participants free of cognitive impairment (total of 234) with 65 females and ages 71.9±7.43. Both sites used single shell acquisitions but varied gradient schemes. VMAP acquired 32 directions at a b-value of 1000 s/mm2, and BLSA acquired 64 samples at a b-value of 700 s/mm2, respectively. BLSA used a Philips 3T scanner at a resolution of 2.2 × 2.2 × 2.2 mm3 and resampled to 0.81 × 0.81 × 2.2 mm3. VMAP used a Philips 3T scanner at a resolution of 2 × 2 × 2 mm3.
Newlin N.R., Kim M.E., Kanakaraj P., Yao T., Hohman T., Pechman K.R., Beason-Held L.L., Resnick S.M., Archer D., Jefferson A., Landman B.A, & Moyer D. (2023). MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal. bioRxiv.
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5

Longitudinal Structural Brain Imaging Atlas

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We analyzed structural data from two cohorts of participants. We first constructed a sulcal curve atlas dataset using 21 subjects from the publicly available KIRBY21 database (11 males; 10 females; age range=22-61) [49 (link)]. Structural images in the atlas dataset were acquired with (3T Philips MPRAGE sequence with a 1×1×1.2 mm3 resolution and an FOV of 240×204×256 mm3). Our primary analyses were carried out using data from 784 participants in the Baltimore Longitudinal Study of Aging (BLSA) (349 males; 435 females; mean age=72 years; range = 25–99) [43 ]. Participants in this study were scanned 1–7 times over a period of 8.5 years on a Philips 3T scanner using a 3D “magnetization prepared rapid gradient echo” (MPRAGE) sequence. Each image had 170×256×256 voxels with 1.2×1×1 mm3 resolution. The local Institutional Review Boards approved the study, and all participants provided written informed consent at each visit. From the BLSA dataset, we created 3 samples of participants. The training set consisted of 1–7 longitudinal scans from 759 individuals for a total of 1374 separate MRI sessions. The validation dataset was constructed from single sessions from 28 individuals that are not included as part of the training set. Single session data from 22 participants not included in the training or validation datasets were used to create a test dataset.
Parvathaneni P., Nath V., McHugo M., Huo Y., Resnick S.M., Woodward N.D., Landman B.A, & Lyu I. (2019). Improving Human Cortical Sulcal Curve Labeling in Large Scale Cross-Sectional MRI using Deep Neural Networks. Journal of neuroscience methods, 324, 108311.
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6

Brain MRI Image Synthesis and Denoising

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The image synthesis GAN and the denoising autoencoder experiments were conducted on 528 T1-weighted brain MRI images from healthy controls (ages 29–94 years old, mean of 67.9 years old) as part of the Baltimore Longitudinal Study of Aging (BLSA) study, which is a study of aging operated by the National Institute of Aging [15 (link)]. MR images were acquired on a Phillips 3T scanner using an MPRAGE sequence and with 1mm isotropic voxel resolution. All subjects were affine-registered to MNIs-space and intensity-normalized. For computation efficiency, the single midline axial slice was chosen for each subject to train the model. The dimensions of all slices were 220 × 172 voxels.
Bermudez C., Plassard A.J., Davis T.L., Newton A.T., Resnick S.M, & Landman B.A. (2018). Learning Implicit Brain MRI Manifolds with Deep Learning. Proceedings of SPIE--the International Society for Optical Engineering, 10574, 105741L.
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7

Resting-state fMRI Acquisition and Processing

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Whole-brain images were acquired on a Phillips 3T scanner equipped with an 8-channel head-coil. Resting-state fMRI data were obtained using a single-shot echo-planar imaging (EPI) gradient echo sequence, repetition time (TR) = 2000 ms, echo time (TE) = 30ms, flip angle = 90, field of view (FOV) = 240 mm, voxel size = 3x3mm with 33 slices of 3.5 mm. A total of 150 volumes were acquired during a 5-min scan. Participants were instructed to keep their eyes closed without falling asleep or thinking of anything in particular during scanning. A sagittal T1-weighted gradient-echo sequence (MPRAGE: 172 contiguous slice, 1 mm3 voxel, TR = 7.5 ms, TE = 3.5ms, FOV = 210 mm) was acquired for brain segmentation and functional image registration. Head movements during scanning were minimized by cushioning the participant’s head in the coil; all subjects were monitored for any movements while in the scanner.
Battistella G., Fuertinger S., Fleysher L., Ozelius L.J, & Simonyan K. (2016). Cortical sensorimotor alterations classify clinical phenotype and putative genotype of spasmodic dysphonia. European journal of neurology, 23(10), 1517-1527.
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8

Multimodal MRI Acquisition Protocol

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MRI scans were collected from multiple scanners including a GE 3T, Philips Intera Achieva, Siemens 1.5T, Siemens 3T, and Philips 3T scanner. Details on imaging procedures are available in the Supplementary Material.
Patel V.S., Kelly S., Wright C., Gupta C.N., Arias-Vasquez A., Perrone-Bizzozero N., Ehrlich S., Wang L., Bustillo J.R., Morris D., Corvin A., Cannon D.M., McDonald C., Donohoe G., Calhoun V.D, & Turner J.A. (2015). MIR137HG risk variant rs1625579 genotype is related to corpus callosum volume in schizophrenia. Neuroscience letters, 602, 44-49.
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9

Multimodal MRI Acquisition Protocol

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Images were acquired in a Philips Achieva 3T scanner (Amsterdam, The Netherlands) using an 8-channel SENSE head coil. The MP-RAGE and the DTI sequence were acquired during the same scanning session. No single group was scanned on a schedule or time different than the other groups, thereby avoiding bias related to temporally dependent scanner calibration. The MP-RAGE volume was collected in sagittal orientation with in-plane resolution of 256 × 256 and 1 mm slice thickness (isotropic voxels of 1 mm3; TR = 650 ms, TE = 3.2 ms, FOV 256 mm, flip angle 8°, SENSE factor = 1).
The diffusion data were acquired using a single-shot spin-echo EPI pulse sequence (TE = 90 ms, TR = 8609 ms, SENSE factor = 2.5, 5 min acquisition) with 32 diffusion weighted (b-factor = 850 s/mm2, anterior–posterior fold-over direction) and three non-diffusion volumes (b-factor = 0 s/mm2; averaged in scanner to a single B0 volume). Each volume contained 66 slices (thickness = 2 mm, gap = 0 mm) acquired in the axial plane, with a reconstructed matrix size of 128 × 128 and FOV 230 mm, resulting in voxel size 1.8 × 1.8 × 2 mm. Fat suppression was achieved using a standard SPIR (spectral pre-saturation with inversion recovery) technique. The sequence was repeated twice for each subject and the datasets were averaged to increase signal to noise ratio.
Pustina D., Avants B., Sperling M., Gorniak R., He X., Doucet G., Barnett P., Mintzer S., Sharan A, & Tracy J. (2015). Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: A multimodal study. NeuroImage : Clinical, 9, 20-31.
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10

MRI Neuroimaging Protocol for WM Lesion Segmentation

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All subjects underwent MR scanning at a 3T scanner (Philips Achieva) using the same protocol. We acquired a 3D gradient echo T1w sequence using magnetization-prepared 180 degrees radiofrequency pulses and rapid gradient-echo sampling with a spatial resolution of 1.0 x 1.0 x 1.0 mm3, a repetition time (TR) of 9 ms, and an echo time (TE) of 4 ms. For the segmentation of WM lesions, we also acquired a 3D FLAIR sequence with a spatial resolution of 1.0 x 1.0 x 1.5 mm3, a TR of 104 ms, a TE of 140 ms, and a time to inversion of 2750 ms.
Righart R., Schmidt P., Dahnke R., Biberacher V., Beer A., Buck D., Hemmer B., Kirschke J.S., Zimmer C., Gaser C, & Mühlau M. (2017). Volume versus surface-based cortical thickness measurements: A comparative study with healthy controls and multiple sclerosis patients. PLoS ONE, 12(7), e0179590.
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