MriResearchTools

Prerequisites

A Julia installation v1.3 or higher is required. To get the newest version of this package, Julia v1.7 or newer is recommended.

Magnitude and Phase images in NIfTI fileformat (4D images with echoes in the 4th dimension, 5D images with channels in the 5th dimension)

Installing

Open the Julia REPL and type

julia> ] # enter julia package manager
(v1.11) pkg> add MriResearchTools
(v1.11) pkg> # type backspace to get back to the julia REPL
julia>

Quick Start

Open multi-echo 4D NIfTI phase and magnitude files and perform ROMEO phase unwrapping.

using MriResearchTools
# input images
TEs = [4,8,12]
nifti_folder = joinpath("test", "data", "small")
magfile = joinpath(nifti_folder, "Mag.nii") # Path to the magnitude image in nifti format, must be .nii or .hdr
phasefile = joinpath(nifti_folder, "Phase.nii") # Path to the phase image
# load images
mag = readmag(magfile)
phase = readphase(phasefile; fix_ge=true) # fix_ge=true only for GE data with corrupted phase
# unwrap
unwrapped = romeo(phase; mag=mag, TEs=TEs)
# save unwrapped image
outputfolder = "outputFolder"
mkpath(outputfolder)
savenii(unwrapped, "unwrapped", outputfolder, header(phase))

Included Functionality

Function Reference: https://korbinian90.github.io/MriResearchTools.jl/dev

ROMEO 3D/4D Phase Unwrapping
romeo unwrap unwrap_individual romeovoxelquality mask_from_voxelquality calculateB0_unwrapped get_B0_snr

Laplacian unwrapping
laplacianunwrap

MCPC-3D-S multi-echo coil combination
mcpc3ds

MCPC-3D-S phase offset removal of multi-echo, multi-timepoint data
mcpc3ds_meepi

Reading, writing and other functions for NIfTI files (adapted from JuliaIO/NIfTI)
readphase readmag niread savenii header write_emptynii

Magnitude homogeneity correction (example)
makehomogeneous

Masking
robustmask phase_based_mask

Combine multiple coils or echoes (magnitude only)
RSS

Unwarping of B0 dependent shifts
getVSM thresholdforward unwarp

Fast gaussian smoothing for real, complex data and phase (via complex smoothing)
gaussiansmooth3d gaussiansmooth3d_phase

• standard
• weighted
• with missing values
• optional padding to avoid border effects

Fast numeric estimation of T2 and R2
NumART2star r2s_from_t2s

QSM integration with single-echo / multi-echo data (experimental stage)
qsm_average qsm_B0 qsm_laplacian_combine qsm_romeo_B0 qsm_mask_filled
Needs the command using QuantitativeSusceptibilityMappingTGV for the TGV QSM backend or using QSM to load the QSM.jl (rts default) backend.

Other functions
robustrescale getHIP getsensitivity getscaledimage estimatequantile estimatenoise

Methods are implemented from these Publications

ROMEO

Dymerska, B., Eckstein, K., Bachrata, B., Siow, B., Trattnig, S., Shmueli, K., Robinson, S.D., 2020. Phase Unwrapping with a Rapid Opensource Minimum Spanning TreE AlgOrithm (ROMEO). Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.28563

MCPC-3D-S

Eckstein, K., Dymerska, B., Bachrata, B., Bogner, W., Poljanc, K., Trattnig, S., Robinson, S.D., 2018. Computationally Efficient Combination of Multi-channel Phase Data From Multi-echo Acquisitions (ASPIRE). Magnetic Resonance in Medicine 79, 2996–3006. https://doi.org/10.1002/mrm.26963

Homogeneity Correction

Eckstein, K., Trattnig, S., Robinson, S.D., 2019. A Simple Homogeneity Correction for Neuroimaging at 7T, in: Proceedings of the 27th Annual Meeting ISMRM. Presented at the ISMRM, Montréal, Québec, Canada. https://index.mirasmart.com/ISMRM2019/PDFfiles/2716.html Eckstein, K., Bachrata, B., Hangel, G., Widhalm, G., Enzinger, C., Barth, M., Trattnig, S., Robinson, S.D., 2021. Improved susceptibility weighted imaging at ultra-high field using bipolar multi-echo acquisition and optimized image processing: CLEAR-SWI. NeuroImage 237, 118175. https://doi.org/10.1016/j.neuroimage.2021.118175

NumART2 - fast T2 and R2* fitting

Hagberg, G.E., Indovina, I., Sanes, J.N., Posse, S., 2002. Real-time quantification of T2* changes using multiecho planar imaging and numerical methods. Magnetic Resonance in Medicine 48(5), 877-882. https://doi.org/10.1002/mrm.10283

Phase-based-masking

Hagberg, G.E., Eckstein, K., Tuzzi, E., Zhou, J., Robinson, S.D., Scheffler, K., 2022. Phase-based masking for quantitative susceptibility mapping of the human brain at 9.4T. Magnetic Resonance in Medicine. https://doi.org/10.1002/mrm.29368

License

This project is licensed under the MIT License - see the LICENSE for details