Signa twinspeed hdxt mr scanner

Manufactured by GE Healthcare

The Signa Twinspeed HDxT MR Scanner is a magnetic resonance imaging (MRI) system developed by GE Healthcare. The core function of this equipment is to generate high-quality images of the human body using strong magnetic fields and radio waves. It is designed to provide efficient and reliable performance for clinical diagnostic applications.

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Signa HDxt MR Signa Twinspeed scanner Signa HDxt scanner Signa MR scanner Signa Twinspeed Signa HDxt Signa scanner Twinspeed Scanners

2 protocols using signa twinspeed hdxt mr scanner

Functional MRI Task-Derived Intrinsic Connectivity

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Functional MRI data were acquired on a single 3T GE Signa Twinspeed HDxT MR Scanner (GE Healthcare) using an 8-channel phased-array head coil. All participants completed 5 fMRI tasks, of which task-derived intrinsic connectivity data were estimated using a previously validated method.^{21 (link),22 (link)} The details of the acquisition, preprocessing, method for task-derived intrinsic connectivity, and handling of motion has been described previously^{23 (link)} and in the eMethods in Supplement 1.

Korgaonkar M.S., Breukelaar I.A., Felmingham K., Williams L.M, & Bryant R.A. (2023). Association of Neural Connectome With Early Experiences of Abuse in Adults. JAMA Network Open, 6(1), e2253082.

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3D T1-Weighted Imaging of Brain

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MR Imaging for this cohort was performed using an 8‐channel phased‐array head coil on a 3T GE Signa Twinspeed HDxT MR Scanner (GE Healthcare, Milwaukee, Wisconsin) at the Department of Radiology, Westmead Hospital. T1‐weighted images were acquired in the sagittal plane using a 3D SPGR sequence (FOV = 256mm, TR = 8.3 ms; TE = 3.2 ms; Flip Angle = 11°; TI = 500 ms; NEX = 1 and ASSET = 1.5; Frequency direction: S/I). A total of 180 contiguous 1 mm slices was acquired with a 256 × 256 matrix, with an in‐plane resolution of 1 mm × 1mm resulting in isotropic voxels in a total scan time of 7mins 12sec.

Breukelaar I.A., Antees C., Grieve S.M., Foster S.L., Gomes L., Williams L.M, & Korgaonkar M.S. (2016). Cognitive control network anatomy correlates with neurocognitive behavior: A longitudinal study. Human Brain Mapping, 38(2), 631-643.

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