3.0 tesla allegra mri scanner

Thirty-one subjects were scanned with a Siemens 3.0 Tesla Allegra MRI scanner. Structural images for atlas transformation and lesion segmentation were acquired using a T1-weighted MP-RAGE (1x1x1.25 mm voxels; TE=3.93ms, TR=1810ms, TI=1200ms, flip angle=12 deg) and T2-weighted fast spin echo sequence (1.1x1.1x3.0 mm voxels; TE=96ms, TR=8430ms). Thirty-nine subjects were scanned with a Siemens 3.0 Trio MRI scanner. Structural images for atlas transformation and lesion segmentation were acquired using a T1-weighted MP-RAGE (1x1x1 mm voxels; TE=2.26ms, TR=1950ms, TI=900ms, flip angle=9 deg) and T2-weighted fast spin echo sequence (1x1x1 mm voxels; TE=441ms, TR=2500ms).

Structural MRI was obtained using a Siemens 3.0 Tesla Allegra MRI scanner at the Hoglund Brain Imaging Center. High-resolution T1-weighted (MPRAGE) images were obtained and a T1-weighted axial slice-based structural scan was acquired (repetition time/echo time [TR/TE] = 23/4 ms, flip angle = 90°, field of view [FOV] = 192 mm, matrix = 256 × 256, slice thickness = 3 mm, 0.5 skip, in-plane resolution = 1 × 1 mm). One individual in the PD group was not able to complete the scan sequence, thus our imaging data reflect data from 21 PD subjects.

Structural MRI data were obtained using a Siemens 3.0 Tesla Allegra MRI Scanner. Fluid attenuated inversion recovery (FLAIR) images were used for white matter lesion assessments (Ti = 2500, TR = 10,000, TE = 81.0, flip angle = 180°, slice thickness = 4 mm with 0 gap). WMLs (PVWMLs (contiguous with the ventricle) and DWMLs) were manually traced on FLAIR images as a measure of vascular related brain injury using Medical Image Processing, Analysis, and Visualization (MIPAV, Johns Hopkins University) expressed as volume in mm³. A single rater (LH) was trained to a threshold of <10% error and reliability of white matter assessments were assessed with 10 blinded repeat assessments inserted randomly into the rater’s workflow. The training set used to learn this method was randomly sampled drawn from a larger pool of 191 images, given a random identifier at each rating during training, and provided in batches of 20 (including 10 previously rated images) until repeat absolute error was below 10%. Intra-rater reliability was excellent, absolute-agreement ICC(3,2) = 0.88 (F = 9.9, df = 51, p < 0.001).