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Mr 750

Manufactured by Philips
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The Philips MR 750 is a magnetic resonance imaging (MRI) system designed for medical imaging applications. It produces high-quality images of the body's internal structures using strong magnetic fields and radio waves. The MR 750 is capable of generating detailed images to aid in the diagnosis and monitoring of various medical conditions.

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Lab products found in correlation

Prisma, by Cytiva (3 mentions) Prisma fit, by GE Healthcare (1 mentions) Ingenia, by Philips (1 mentions)

3 protocols using mr 750

1

Structural MRI Data Processing Protocol

Cited 2 times
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T1-weighted structural MRI data were gathered on 3-T MRI systems (Siemens Prisma, General Electric MR 750, Philips). On the basis of standardized processing pipelines [23 (link)], structural MRI data processing was collected using FreeSurfer version 5.3.070. All scan sessions completed radiological review whereby scans with incidental results were identified. Participants were removed who could not pass the visual inspection of T1 images and FreeSurfer quality control [55 (link)] (imgincl_t1w_include =  = 1). According to the Desikan-Killiany Atlas, the current analysis used post-processed SA and CT data which were mapped to 34 cortical parcellations per hemisphere (68 brain regions in total) [56 (link)].
Kuang N., Liu Z., Yu G., Wu X., Becker B., Fan H., Peng S., Zhang K., Zhao J., Kang J., Dong G., Zhao X., Sahakian B.J., Robbins T.W., Cheng W., Feng J., Schumann G., Palaniyappan L, & Zhang J. (2023). Neurodevelopmental risk and adaptation as a model for comorbidity among internalizing and externalizing disorders: genomics and cell-specific expression enriched morphometric study. BMC Medicine, 21, 291.
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2

Structural MRI Processing and Brain Parcellation

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Participants completed a high-resolution T1-weighted structural MRI scan (1 mm isotropic voxels) on 3 T scanners (Siemens Prisma, General Electric MR 750, Philips). Structural MRI data processing was completed using FreeSurfer version 5.3.0 according to standardized processing pipelines. 25 (link) All scans underwent radiological review to identify incidental findings. We excluded 644 youth who did not pass visual inspection of T1 images and FreeSurfer quality control26 (link) (imgincl_t1w_include = = 1) from the following analyses. The present study used post-processed CT data mapped to 34 cortical parcellations per hemisphere based on the Desikan–Killiany Atlas.27 (link)
Yu G., Liu Z., Wu X., Becker B., Zhang K., Fan H., Peng S., Kuang N., Kang J., Dong G., Zhao X.M., Schumann G., Feng J., Sahakian B.J., Robbins T.W., Palaniyappan L, & Zhang J. (2023). Common and disorder-specific cortical thickness alterations in internalizing, externalizing and thought disorders during early adolescence: an Adolescent Brain and Cognitive Development study. Journal of Psychiatry & Neuroscience : JPN, 48(5), E345-E356.
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3

Structural MRI Data Processing Pipeline

Cited 1 time
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MRI acquisition and scanning parameters are described elsewhere (Casey et al., 2018 (link)) (see the Supporting Information Appendix S1 for details). Brain data were collected on 3T scanners (Siemens Prisma and Prisma Fit, General Electric MR 750, Philips Achieva dStream and Ingenia). The T1 images were corrected for gradient nonlinearity distortions using scanner-specific, nonlinear transformations. Cortical reconstruction and volumètric segmentation were performed by the Data Analysis, Informatics, and Resources Centre (DAIRC) of ABCD using FreeSurfer v5.3.0 (Dale, Fischl, & Sereno, 1999 (link)). The present study used post-processed structural (i.e., cortical volume, thickness, and surface area) data mapped to 34 cortical parcellations per hemisphere (68 total regions of interest) based on the Desikan-Killiany brain registration atlas (Desikan et al., 2006 (link)). For cortical volume, 8 subcortical segmentations per hemisphere were also investigated (16 regions in total) (Fischl et al., 2002 (link)). DAIRC used a combination of automated and manual methods to review the datasets for quality control prior to sharing data via the NDA database.
Mewton L., Lees B., Squeglia L.M., Forbes M.K., Sunderland M., Krueger R., Koch F.C., Baillie A., Slade T., Hoy N, & Teesson M. (2021). The relationship between brain structure and general psychopathology in preadolescents. Journal of child psychology and psychiatry, and allied disciplines, 63(7), 734-744.
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