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ZENODO
Dataset . 2010
License: CC 0
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2010
License: CC 0
Data sources: ZENODO
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2010 Ismrm Recon Challenge, Stockholm, Sweden

Authors: Pipe, James;

2010 Ismrm Recon Challenge, Stockholm, Sweden

Abstract

This data was presented at the ISMRM-ESMRMB Joint Annual Meeting in Stockholm, Sweden, 2010. It can be found in the program under: PLENARY SESSION Room AI The Eye of the Beholder: An Image Reconstruction Challenge Organizers: Margaret A. Hall-Craggs, M.D., Douglas C. Noll, Ph.D., and James G. Pipe, Ph.D. 08:15 If I Am So Good at This, Why Do I Miss So Much? Jeremy M. Wolfe, Ph.D. 08:40 Reconstruction Challenge: So Many Algorithms, So Few Data Award presentations, panel discussion --- Please read the instructions for each data set before downloading. The first three data sets are from the 2010 ISMRM Recon Challenge. The available poster (2010RCPoster.pdf) gives examples of the entries. Registrants to the 2010 ISMRM Annual Meeting can hear radiologist’s comments and see the results by visiting here (http://www.ismrm.org/PRES_W_A1_0840/). 1. Need for Speed. Data were simulated using collected projection X-ray of an arterial bolus injection in a patient with an AVM. X-ray data were collected 3 frames per second, for a total of 10 seconds (31 collected frames) which span wash-in to wash-out. These were linearly interpolated in time between frames to create a total of 200 temporal images, each with 512x512 resolution. B1-maps (provided with data) derived from an axial slice through a water phantom using an 8-channel head coil were superimposed on the image to create 8 “coil” images. Independent noise was added to each channel. The data were synthesized over 200 trajectories, each with 2000 points. Data for each trajectory are synthesized from one temporal frame of the time series. NFS_materials.zip: descriptions of trajectories (the pdf files), read/write code for C and Matlab, and B1 maps. NFS_truth.dat: for the NFS truth data set, which has no header, is floating point (4 byte) real data, and arranged as a 3D array of 512x512x37. You may then choose from any of the following sets: A. NFS_NZWA_09AUG06.zip: a Spiral trajectory coordinates and data. (trajectory designed by Nick Zwart, Barrow Neurological Institute) B. NFS_ASAM_09AUG04.zip: a PR trajectory coordinates and data. (trajectory designed by Alexey Samsonov, University of Wisconsin) 2. Double Vision. Data originate from 12 axial images in the abdomen (respiratory gated T2 FSE with Fat Sat) collected using a torso phase-array coil, collected at 320x320 matrix (40cm FOV). Field maps (breatheld, end-exhilation, with Fat Sat) were collected using gradient echo images at TE = 3 and 5, with a 96x96 collected matrix. Synthesized data were corrupted by off-resonance phase. Data from each of the 8 coils were generated. B0 and B1 maps are provided with the data. (B0 maps are in units of Hz). The data were synthesized over 8 trajectories, each with 20,000 points. The dwell time was 1usec, e.g. the time for each acquisition was 20msec. DV_materials.zip: descriptions of trajectories (the pdf files), read/write code for C and Matlab, B0 maps, and B1 maps. DV_truth.dat: the DV truth data set, which has no header, is floating point (4 byte) real data, and arranged as a 3D array of 320x320x12. You may choose from any one of the following sets: A. DV_CMEY_09JUL06.zip: a Spiral trajectory coordinates and data. (trajectory designed by Craig Meyer, University of Virginia) B. DV_JPIP_09SEP08.zip: an EPI trajectory coordinates and data. (trajectory designed by Jim Pipe, Barrow Neurological Institute) 3. Piece of the Puzzle. Data are from axially collected 3D unspoiled Gradient-Echo images of the knee, 320(X) x 320(Y) x 220(Z), with 0.5mm resolution in each direction (FOV = 160mm x 160mm x 110mm). Data are from an 8-channel phased-array, with coils in the x-y plane, i.e. very little coil orthogonality in Z. Contestants will get separate data for each coil. Independent noise was added to each channel. The data were synthesized over 4,000 trajectories, each with 2,000 points, which results in total undersampling of rough R=5, depending on the trajectory set. POP_materials.zip: to get descriptions of trajectories (the pdf files), read/write code for C and Matlab, and B1 maps. POP_truth.dat: for the POP truth data set, which has no header, is floating point (4 byte) real data, and arranged as a 3D array of 320x320x220. You may choose from any one of the following sets: A. POP_MLUS_09AUG03.zip: a Random - Poisson Disk trajectory coordinates and data. (trajectory designed Miki Lustig, Stanford University) B. POP_ADEV_09AUG07.zip: a Spiral Projection (low resolution) trajectory coordinates and data. (trajectory designed Ajit Devaraj, Barrow Neurological Institute) C. POP_ADEV_09AUG10.zip: a Spiral Projection (high resolution) trajectory coordinates and data. (trajectory designed Ajit Devaraj, Barrow Neurological Institute) D. POP_BHAR_09AUG06.zip: a Stack-of-Spirals trajectory coordinates and data. (trajectory designed Brian Hargreaves, Stanford University) E. POP_HUWU_09SEP12.zip: a VIPR trajectory coordinates and data. (trajectory designed Huimin Wu, University of Wisconsin) Originally posted on: http://www.ismrm.org/mri_unbound/simulated.htm

Related Organizations
Keywords

k-Space, Raw Data, Challenge, Reconstruction, Unbound, MRI

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