|Nancy Newlin||Vanderbilt University, Nashville, TN|
|Neda Jahanshad||Keck School of Medicine of USC, Los Angeles, CA|
|Kurt Schilling||Vanderbilt University, Nashville, TN|
|Daniel Moyer||Vanderbilt University, Nashville, TN|
|Eleftherios Garyfallidis||Indiana University, Indianapolis, IN|
|Bennett Landman||Vanderbilt University, Nashville, TN|
Diffusion weighted MRI of the same subject, with the same diffusion weighting, ideally results in the same derived quantitative indices. However, application of diffusion MRI in the research or clinic is cofounded by site and acquisition effects. Here, we make available a large dataset (N=114) of volunteers scanned from two scanners, with two different acquisition protocols, and ask participants to preprocess and/or harmonize the data (testing dataset of N=25) in order to minimize scanner differences while retaining biological variation. We will assess not only tissue microstructure, but features of white matter bundles, and also connectome generation.
CLICK HERE to access the data.The data is organized as follows. There are 25 subjects in the "Testing" folder. This is the subset of data to harmonize and submit. We provided 77 additional subjects to be used for training, if needed (in "Training" folder). All subjects have three sub-folders: Diffusion data from site A ("A"), Diffusion data from site B ("B"), and a T1-weighted image in "anat" folder. Scanning was performed at the QIMR Berghofer Medical Research Institute on a 4 tesla Siemens Bruker Medspec scanner. T1-weighted images were acquired with an inversion recovery rapid gradient-echo sequence (inversion/repetition/echo times, 700/1500/3.35 ms; flip angle, 8°; slice thickness, 0.9 mm; 256 × 256 acquisition matrix). Site A DW images were acquired using single-shot echo-planar imaging with a twice-refocused spin echo sequence to reduce eddy current-induced distortions. A 3-min, 30-volume acquisition was designed to optimize signal-to-noise ratio for diffusion tensor estimation (Jones 1999). Imaging parameters were repetition/echo times of 6090/91.7 ms, field of view of 23 cm, and 128 × 128 acquisition matrix. Each 3D volume consisted of 21 axial slices 5 mm thick with a 0.5-mm gap and 1.8 × 1.8 mm2 in-plane resolution. Thirty images were acquired per subject: three with no diffusion sensitization (i.e., T2-weighted b0 images) and 27 DW images (b = 1146 s/mm2) with gradient directions uniformly distributed on the hemisphere. Site B DW images were acquired using single-shot echo planar imaging (EPI) with a twice-refocused spin echo sequence to reduce eddy-current induced distortions. Acquisition parameters were optimized to improve the signal-to-noise ratio for estimating diffusion tensors (Jones 1999). Imaging parameters were: 23 cm FOV, TR/TE 6090/91.7 ms, with a 128 × 128 acquisition matrix. Each 3D volume consisted of 55 2-mm thick axial slices with no gap and a 1.79 × 1.79 mm2 in-plane resolution. 105 images were acquired per subject: 11 with no diffusion sensitization (i.e., T2-weighted b0 images) and 94 DWI (b = 1159 s/mm2) with gradient directions distributed on the hemisphere. HARDI scan time was 14.2 minutes.
Upload your harmonized DW images, bvecs, and bvals to Box (https://account.box.com/) or an equivalent sharable source. You only need to process the 25 subjects in the "Testing" folder. Send a link to the data to email@example.com along with your team's report! Please title the email with ‘"MICCAI 2023 Challenge Submission – [YOUR TEAM NAME]". We provided two example submissions in the correct format and associated report ("TesSubmission_1" and "TestSubmission_2"). Please keep the same directory organization as the data provided.CLICK HERE to access the report template.