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Filtering by: Creator Fessler, Jeffrey A. Remove constraint Creator: Fessler, Jeffrey A. Keyword machine learning Remove constraint Keyword: machine learning Discipline Engineering Remove constraint Discipline: Engineering

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  • Data for "Myelin Water Fraction Estimation Using Small-Tip Fast Recovery MRI"

    work
    Creator:
    Whitaker, Steven T., Nataraj, Gopal, Nielsen, Jon-Fredrik, and Fessler, Jeffrey A.
    Description:
    File: P,jf06Sep2019,mese.7 The multi-echo spin echo (MESE) data was acquired using a 3D acquisition with an initial 90 degree excitation pulse followed by 32 refocusing (180 degree) pulses, resulting in 32 echoes with echo spacing of 10 ms. The repetition time of the sequence was 1200 ms. Each refocusing pulse was flanked by crusher gradients to impart 14 cycles of phase across the imaging volume. The initial excitation pulse had time-bandwidth product of 6, duration of 3 ms, and slab thickness of 0.9 cm, and each refocusing pulse had time-bandwidth product of 2, duration of 2 ms, and slab thickness of 2.1 cm. The scan took 36 min 11 s and covered a field of view (FOV) of 22 x 22 x 0.99 cm^3 with matrix size 200 x 200 x 9., File: P,jf06Sep2019,b1.7 The Bloch-Siegert (BS) scans were acquired using a 3D acquisition. The excitation pulse of these scans had time-bandwidth product of 8 and duration of 1 ms. The pair of scans used +/-4 kHz off-resonant Fermi pulses between excitation and readout. The BS scans took 2 min 40 s in total and covered a FOV of 22 x 22 x 0.99 cm^3 with matrix size 200 x 50 x 9., File: P,jf06Sep2019,mwf.7 The small-tip fast recovery (STFR) scans were acquired using a 3D acquisition. The first two and last two scans were pairs of spoiled gradient-recalled echo (SPGR) scans with echo time difference of 2.3 ms. (In the related paper, only the first set was used, i.e., only 11 of the 13 scans were used.) The remaining scans used scan parameters that were optimized to minimize the Cramer-Rao Lower Bound (CRLB) of estimates of myelin water fraction (MWF). The RF pulses had time-bandwidth product of 8 and duration of 1 ms. Each pair of SPGR scans took 58 s and the nine STFR scans took 3 min 36 s for a total scan time of 5 min 32 s (4 min 34 s if one pair of SPGR scans is ignored). The scans covered a field of view (FOV) of 22 x 22 x 0.99 cm^3 with matrix size 200 x 200 x 9., File: meseslice5.mat Contains the 32 echoes of the MESE image data for the middle slice of the imaging volume. Saved using Mathworks MATLAB R2019a., File: b1slice5.mat Contains the transmit field inhomogeneity map for the middle slice of the imaging volume., File: recon.jld Key "img" contains the 11 STFR images for the middle slice of the imaging volume. Key "b0map" contains a field map estimated from the two SPGR scans. Key "mask" contains a mask of the voxels for which to estimate MWF. Key "T1img" contains a T1-weighted image for anatomical reference., File: headmask.mat Contains a mask for visualizing just the brain (ignores the skull) for the middle slice of the imaging volume., File: rois.mat Contains masks for various regions of interest (ROIs), used for computing statistics. Keys "mtopleft", "mtopright", "mbottomleft", and "mbottomright" refer to the corresponding locations on the anatomical reference image (see related paper). Key "mic" refers to the internal capsules, and key "mgm" refers to a gray matter ROI., The raw data files (P-files) can be read into the Julia programming language using the Julia version of the Michigan Image Reconstruction Toolbox ( https://github.com/JeffFessler/MIRT.jl) or into MATLAB using TOPPE ( https://github.com/toppeMRI/toppe). The reconstructed slices used in the related paper are provided for convenience, and are stored in .mat files that can be loaded into Julia (using the package MAT.jl) or MATLAB, and a .jld file that can be loaded into Julia (using the package JLD.jl). The Julia code for processing the data to create MWF maps is hosted publicly on GitHub at  https://github.com/StevenWhitaker/STFR-MWF., and Files: toppe-master.zip and MIRT.jl-master.zip are archived versions of the TOPPE and Michigan Image Reconstruction Toolbox code sets from GitHub as of 2/28/2020.
    Keyword:
    myelin, machine learning, kernel learning, magnetic resonance imaging, and scan design
    Citation to related publication:
    Whitaker, S. T., Nataraj, G., Nielsen, J.-F., & Fessler, J. A. (2020). Myelin water fraction estimation using small-tip fast recovery MRI. Magnetic Resonance in Medicine, 84(4), 1977–1990.  https://doi.org/10.1002/mrm.28259
    Discipline:
    Health Sciences and Engineering