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Ironsmith is a comprehensive, fully automated pipeline for creating and processing Quantitative Susceptibility Maps (QSM), extracting QSM based iron concentrations from subcortical and cortical brain regions and evaluating the quality of QSM data using SNR measures and assessment of outlier regions on phase images.

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mri neuroimaging qsm neuroscience aging quantitative-susceptibility-mapping brain-iron pipeline software

ironsmithqsm's Introduction

Ironsmith QSM

Copyright (C) 2022 Valentinos Zachariou, University of Kentucky (see LICENSE file for more details).
Third party software provided with Ironsmith are subject to their own licenses and copyrights (see section 7 for details).

This software has been developed for research purposes only and is not a clinical tool.

Description:

Ironsmith is a comprehensive, fully automated pipeline for creating and processing Quantitative Susceptibility Maps (QSM), extracting QSM values from subcortical and cortical brain regions and evaluating the quality of QSM data using SNR measures and assessment of outlier regions on phase images. Ironsmith can also filter out per-ROI outlier QSM values (such as values associated with large veins) and offers a precise, CSF-only reference region for QSM reconstruction to minimize partial volume effects.

Ironsmith can perform the following tasks:

a) Automate the process of creating QSM maps from GRE DICOM images using MEDI Toolbox (see section 7 for details) .
b) Register MPR or multi-echo MPR (MEMPR) T1 images to QSM maps and then segment these into 89 ROIs (ROI list in section 8) using FreeSurfer.
c) Filter outlier voxels from these ROIs (default: QSM values larger than 97th percentile of values), extract QSM based iron concentration and output the results into CSV formatted tables.
d) Calculate SNR (GRE magnitude image based) for each ROI and output SNR values into CSV tables.
e) Identify outlier regions on phase images using median absolute deviation (MAD); calculate percent overlap between phase-image outlier regions and each ROI; Output results into CSV tables.
f) Non-linearly Warp QSM maps and aligned MPR/MEMPR to MNI152 1mm space. This step allows users to (1) extract QSM values from standard space ROIs not included with Ironsmith and (2) conduct voxelwise QSM analyses.
g) Process single or multiple participants in parallel (multiple instances and nohup supported).
h) Provide comprehensive user feedback and detailed error reporting. When an error or warning occurs, Ironsmith does not display cryptic messages but instead provides detailed reports of what might have gone wrong and how a user can fix the error/warning.

If Ironsmith QSM was useful for your research/project, please cite the following publication:

Zachariou, V., Bauer, C. E., Powell, D. K., & Gold, B. T. (2022). Ironsmith: An automated pipeline for QSM-based data analyses. NeuroImage, 249, 118835.

Release Notes (latest release):

Ironsmith QSM v1.4 Beta (12/12/2022)

Can be installed via git clone https://github.com/vzachari/IronSmithQSM.git
or via direct download as described in section #2, Installation.

Updates:

Fixed bug related to issue #5. Ironsmith would crash if MyInputFile.csv was provided with complete path to file AND BOM/line endings needed adjustment.
Fixed bug related to numbers appearing in scientific notation instead of in decimal.
Support for oblique MRI datasets added (MRI scans collected at an angle). Previously these would look misaligned in certain NIFTI viewers.
Options added in MEDI.sh so one can choose the phase unwrapping techinque desired (region-growing or laplacian).
Options added in MEDI.sh so one can specify if even or uneven echo spacing was used for the QSM scan.

NOTE for Siemens XA30 software users:
MEDI Toolbox (version 01/15/2020) does not support XA30 enhanced DICOMs. To fix this issue:

DICOMs must be exported as "Interoperability" from the MRI scanner console.
A single line of code needs to be changed in one of the MEDI Toolbox (version 01/15/2020) functions:

In file Read_DICOM.m under MEDI_toolbox/functions
Change line 50 from case {'siemens','siemens healthcare gmbh'} to case {'siemens','siemens healthcare gmbh','siemens healthineers'}

NOTE:
Due to the FreeSurfer update, the per-ROI QSM values are not identical between Ironsmith v1.0 and later versions.
Correlation of QSM values extracted from the 89 ROIs offered by Ironsmith, between v1.0 and v1.2, yielded an r^2 value of 0.99.
For this reason, we do not recommend updating Ironsmith mid-analyses. All data should be analyzed with either v1.0 OR v1.2 (FreeSurfer 6.0.0 or FreeSurfer 7.1.1).

1) Software requirements:

a) Operating system

Unix
Any Linux distribution that supports Singularity (see point c).

Ironsmith tested on:
Red Hat Enterprise Linux Workstation release 7.8 (Maipo)

Windows 10 WSL2
Ironsmith tested on:
Ubuntu 18.04 running on Windows 10 Subsystem for Linux V2 (WSL2)
Ubuntu 16.04 running on Windows 10 Subsystem for Linux V2 (WSL2)

b) MATLAB

Ironsmith requires Matlab to run MEDI Toolbox and supports versions R2017b to R2019b.
MEDI Toolbox requires the Matlab Image Processing Toolbox to be installed.
Matlab is not needed if MEDI is not required.

c) Singularity

Ironsmith tested on Singularity versions 3.5.2 and 3.5.3
Installation guide:
https://sylabs.io/guides/3.5/admin-guide/installation.html

d) Bash UNIX shell version 4.2.46(2) or later with GNU coreutils

e) MEDI Toolbox version 01/15/2020

Ironsmith requires MEDI Toolbox if QSM maps need to be generated
Download from:
http://pre.weill.cornell.edu/mri/pages/qsm.html
MEDI Toolbox is not required if QSM maps are already available
Currently only MEDI Toolbox version 01/15/2020 is supported

NOTE: 10.8 GB of free space is required for each instance of IronSmith running (see section #5, parallel processing)

2) Installation:

a) Download Ironsmith QSM

Option 1: download from github

Visit https://github.com/vzachari/IronSmithQSM
Click on tags
Click on desired IronsmithQSM version (release notes are displayed by clicking the three dots ... )
Click on source code link (zip or tar.gz) to download

NOTE: Via this download option the IronSmithQSM folder will be IronSmithQSM-version# (Ex. IronSmithQSM-1.0).

Option 2: using git

git clone https://github.com/vzachari/IronSmithQSM.git && cd IronSmithQSM && git checkout v1.0

NOTE: git checkout v1.0 can be replaced with a different version number. Type git tag -l from within the IronSmithQSM folder for a list of available versions.

b) Download QSM_Container.simg

For Ironsmith v1.2 (09/29/2021): FreeSurfer7.1.1 (10.8GB)

From: https://drive.google.com/file/d/1NFV2z0yIEPKGblQVrcMe8bza8ZS21AO7/view?usp=sharing

For Ironsmith v1.0 (06/04/2021): FreeSurfer6.0.0

Ironsmith V1.0 is no longer supported due to University of Kentucky cloud space limitations. Please use a newer version of Ironsmith.

c) Place QSM_Container.simg in IronSmithQSM/Functions

d) Download MEDI Toolbox version 01/15/2020 (~7MB)

From: http://pre.weill.cornell.edu/mri/pages/qsm.html

e) Unzip MEDI Toolbox (typically MEDI_toolbox.zip)

f) Place MEDI_toolbox folder (folder with README.m, UPDATES.m etc) into IronSmithQSM/Functions

NOTE: Make sure the MEDI_toolbox folder in IronSmithQSM/Functions does not have another MEDI_toolbox folder in it (e.g MEDI_toolbox/MEDI_toolbox).

g) Edit IronSmithQSM/Matlab_Config.txt with the path to the matlab executable on your system

(e.g. /usr/local/MATLAB/R2019b/bin/matlab)
Supported versions R2017b to R2019b.
NOTE: the Image Processing Toolbox needs to be installed for the matlab version specified in IronSmithQSM/Matlab_Config.txt.

h) Add the IronSmithQSM directory to $PATH

Guide: https://opensource.com/article/17/6/set-path-linux

3) Syntax:

Ironsmith [MyInputFile] [absolute path to output folder]

Example: "Ironsmith File.csv /home/data/MyAmazingExp/QSM_Analysis"

a) The output folder does not need to exist.

b) The output folder does not need to be empty but Ironsmith will skip any participant specified in MyInputFile that has a corresponding folder inside the output folder.

Ex. if S0001 is specified in MyInputFile and folder S0001 exists in output folder, S0001 will be skipped.

c) Absolute path to MyInputFile needs to be provided
(Ex. /home/data/MyAmazingExp/CSVFileVault/File.csv) if MyInputFile is not in current folder.

d) Freesurfer_Skip is a reserved folder name under output folder and may be used by Ironsmith. See section #5 on optional features below.

4) MyInputFile format:

a) MyInputFile has to be CSV formatted (entries separated by commas ',').

b) MyInputFile can be created in Excel (MS Windows or MacOS) and saved as a CSV (Comma delimited) file or in a Unix text editor (e.g. Gedit, Atom, Emacs).

c) Each row in MyInputFile corresponds to a different participant.

see Example_File.csv in IronSmithQSM folder:

If MEDI Toolbox is required to create QSM images/maps:

Column1 = Subj (nominal subject variable e.g. S0001 or 01 or Xanthar_The_Destroyer)
Column2 = MEDI_Yes <-- this is case sensitive
Column3 = Either absolute path to directory with MPR/MEMPR files OR absolute path to a single NIFTI (.nii or .nii.gz) MPR/MEMPR file
(e.g. /home/subjects/S01/MPR OR /home/subjects/S01/MPR/S01_MPR.nii.gz)

If a single NIFTI (.nii or .nii.gz) MPR/MEMPR file is provided:
File can have any name.
File can have multiple volumes, each corresponding to a different echo (RMS will be calculated).
File can have a single echo/volume.

If path to directory with MPR/MEMPR files is provided:

MPR/MEMPR files in provided folder can be:

a) DICOMS

Only MPR/MEMPR DICOMS should be present in the MPR folder if you want Ironsmith to process DICOMS. If NIFTI files exist together with DICOMS, they will be selected instead (see "b" below).

b) Multiple .nii/.nii.gz files each corresponding to a different echo (will be catenated and RMS will be calculated).

To make sure the correct files are selected, each NIFTI file needs to have _e# in the file name, where # is the echo number.
(e.g. S0001_MEMPR_e1.nii.gz, S0001_MEMPR_e2.nii.gz...)
This is the default dcm2niix output format for multiple echos.

c) Single .nii/.nii.gz file with multiple volumes, each corresponding to a different echo (RMS will be calculated).
This single NIFTI file can have any name.

d) Single .nii/.nii.gz file with a single echo/volume.
This can be rms/averaged across echos or just a single echo T1 MPRAGE.
This single NIFTI file can have any name and will be used as is.

Column4 = Absolute path to folder with QSM DICOM files
(e.g. /home/subjects/S01/QSM_Dicom)

The QSM DICOM folder must include DICOMS for both GRE magnitude and phase. T2* DICOMS that are sometimes saved as part of a GRE sequence can be present in the QSM DICOM folder and will be ignored.

Preferably only DICOMS should be present in the QSM DICOM folder. However, Ironsmith can filter out the following filetypes .nii .json .txt .nii.gz .HEAD .BRIK .hdr .img

All 4 columns need to be provided, otherwise Ironsmith will exit with errors.

If QSM maps and GRE magnitude images are already available:

Column1 = Subj (nominal subject variable e.g. S0001 or 01 or Xanthar_The_Destroyer)
Column2 = MEDI_No <-- This is case sensitive
Column3 = Either absolute path to directory with MPR/MEMPR files OR absolute path to a single NIFTI (.nii or .nii.gz) MPR/MEMPR file
(e.g. /home/subjects/S01/MPR OR /home/subjects/S01/MPR/S01_MPR.nii.gz)

If path to directory with MPR/MEMPR files is provided:

MPR/MEMPR files in provided folder can be:

a) DICOMS

Only MPR/MEMPR DICOMS should be present in the MPR folder if you want Ironsmith to process DICOMS. If NIFTI files exist together with DICOMS, they will be selected instead (see "b" below).

b) Multiple .nii/.nii.gz files each corresponding to a different echo (will be catenated and RMS will be calculated).

c) Single .nii/.nii.gz file with multiple volumes, each corresponding to a different echo (RMS will be calculated).
This single NIFTI file can have any name.

d) Single .nii/.nii.gz file with a single echo/volume.
This can be rms/averaged across echos or just a single echo T1 MPRAGE.
This single NIFTI file can have any name and will be used as is.

Column4 = Absolute path including filename to QSM magnitude image (.nii or .nii.gz)
(e.g. /home/subjects/S01/QSM/QSM_Magnitude.nii.gz)

Column5 = Absolute path including filename to QSM map (.nii or .nii.gz)
(e.g. /home/subjects/S01/QSM/QSM_Map.nii.gz)

All 5 columns need to be provided, otherwise Ironsmith will exit with errors.

5) Optional features:

Skipping FreeSurfer segmentation

If FreeSurfer has already run and a participant has a completed FreeSurfer recon-all -all segmentation folder, Ironsmith can skip the FreeSurfer segmentation step by doing the following:

a) Copy the FreeSurfer recon-all folder (the one containing the label, mri, scripts, stats, surf... folders) into /OutputFolder/Freesurfer_Skip, where OutputFolder is the one specified/to be specified in the Ironsmith command.

You can create the /OutputFolder/Freesurfer_Skip folder or Ironsmith will create it for you if you have run it at least once previously for OutputFolder.

b) Rename the recon-all folder to Subj_FreeSurfSeg_Skull. Subj should match the one provided in MyInputFile and should correspond to the participant you want the segmentation step skipped.

Note: if Ironsmith runs FreeSurfer, it will create Subj_FreeSurfSeg_Skull and place it under /OutputFolder/Subj/MPR. This helps reduce processing time if for any reason one would like to repeat the analysis on a given participant (e.g. due to a crash or errors). Just copy/move this folder over to /OutputFolder/Freesurfer_Skip, delete the problematic participant folder (e.g. /OutputFolder/Subj) and re-run Ironsmith.

Processing participants in parallel

Parallel processing can significantly increase the speed of analyses. Running the Ironsmith command with the same MyInputFile and output folder in multiple terminal windows allows for parallel processing of participants specified in MyInputFile.

For example, running three instances of Ironsmith:

Terminal 1:
Ironsmith File.csv /home/data/MyAmazingExp/QSM_Analysis

Terminal 2:
Ironsmith File.csv /home/data/MyAmazingExp/QSM_Analysis

Terminal 3:
Ironsmith File.csv /home/data/MyAmazingExp/QSM_Analysis

Terminals 1-3 will each be running a different instance of Ironsmith (each working on a different set of participants) but all instances will be working on the same group/list of participants (from File.csv) and in the same output folder (/home/data/MyAmazingExp/QSM_Analysis) and will only create a single set of group output files (see section #6 below).

NOTE: nohup can also be used with parallel processing:

For example, running three nohup instances of Ironsmith:

Terminal 1:
bash (press enter to switch to bash)
nohup Ironsmith File.csv /home/data/MyAmazingExp/QSM_Analysis &> Ironsmith_Inst_1.txt &
nohup Ironsmith File.csv /home/data/MyAmazingExp/QSM_Analysis &> Ironsmith_Inst_2.txt &
nohup Ironsmith File.csv /home/data/MyAmazingExp/QSM_Analysis &> Ironsmith_Inst_3.txt &

To monitor the nohup progress of an Ironsmith instance, locate the Ironsmith_Inst_#.txt file (in directory where the nohup command was executed) and use the following command:
tail -f Ironsmith_Inst_#.txt
ctrl+c exits the tail -f process.

Adding participants to an existing analysis/output folder

To add participants to an existing Ironsmith analysis, simply edit MyInputFile.csv with the info of the additional participants and rerun the Ironsmith command. Ironsmith will skip all participants that have been previously processed (without errors) and will proceed to process the newly added participants. Lastly, the QSM/SNR/MAD values of the new participants will be added to the group output files in OutputFolder.

What if Ironsmith fails while writing data to output files (computer resets, power outage etc)?

Ironsmith can identify corrupted output files and will attempt to correct the damage by reverting these back to the last participant that completed successfully. This error is clearly communicated to the user both on screen and in log files so a corrupted/unfinished participant can be re-processed.

Viewing output NIFTI files

If you do not have a NIFTI viewer, AFNI or fsleyes can be launched from within the QSM_Container.simg by using the Ironsmith_AFNI or Ironsmith_fsleyes commands. Just type Ironsmith_AFNI or Ironsmith_fsleyes from within the folder you would like to view NIFTI files from (provided the IronSmithQSM directory (the installation folder) was added to $PATH; see section 2: Installation ).

AFNI viewer documentation:
https://afni.nimh.nih.gov/pub/dist/edu/latest/afni_handouts/afni03_interactive.pdf

Fsleyes documentation:
https://open.win.ox.ac.uk/pages/fsl/fsleyes/fsleyes/userdoc/

6) Outputs:

Each participant processed by Ironsmith will have a corresponding folder in OutputFolder. For example, if "S0001" is one of the participants processed, then OutputFolder/S0001 will be created and populated with data.

a) All FreeSurfer based masks/ROIs are placed under:

S0001/QSM/FreeSurf_QSM_Masks/Cort_Mask_AL_QSM_RS_Erx1
S0001/QSM/FreeSurf_QSM_Masks/SubC_Mask_AL_QSM_RS_Erx1

b) All QSM maps/images created are placed under S0001/QSM/FreeSurf_QSM_Masks and are labelled as:

Subj_QSM_Map_FSL.nii.gz <-- Whole brain CSF, segmented by MEDI Toolbox, as the QSM reference structure (default MEDI)
Subj_QSM_Map_New_CSF_FSL.nii.gz <-- Lateral ventricles as the QSM reference structure
Subj_QSM_Map_New_WM_FSL.nii.gz <-- White matter as the QSM reference structure

c) All QSM maps warped to MNI space are placed under

S0001/QSM/FreeSurf_QSM_Masks/MNI152_QSM

d) QSM per ROI means (89 ROIs) are under OutputFolder/Group as follows:

Group_QSM_Mean.csv
Group_QSM_Mean_CSF.csv
Group_QSM_Mean_WM.csv
Group_QSM_ADJ_Mean.csv
Group_QSM_ADJ_Mean_CSF.csv
Group_QSM_ADJ_Mean_WM.csv
Group_QSM_SNR.csv
Group_QSM_MAD.csv

_Mean = Using only positive QSM voxels
_ADJ_Mean = Using only positive QSM voxels and adjusting for ROI size (sum of all positive QSM voxels / Number of all voxels within an ROI)
_CSF = Lateral ventricles as the QSM reference structure
_WM = White matter as the QSM reference structure
_SNR = Per ROI SNR values (see SNR description below)
_MAD = Percent overlap between phase image outlier regions and each of the 89 supported ROIs (see Phase QC description below).

NOTE: For each ROI, only QSM voxels with values less than the 97th percentile of all positive QSM values are included in averages. This percentile cutoff point for outliers can be modified by manually editing the header of the 05_Extract_QSM.sh script file (line 38) under the Ironsmith installation folder:

#Percentile cutoff for outlier removal. Edit Percnt variable to change outlier cutoff
Percnt="97"

SNR:
SNR is calculated as follows:
Mean signal intensity of magnitude image (root mean square of all echos) within an ROI / standard deviation of magnitude signal outside the head (away from the frequency and phase axes). Lastly, SNR is multiplied by the Rayleigh distribution correction factor √(2−π/2).

The outside of the head mask used for SNR can be found in a participants folder within the OutputFolder. For example:
/OutputFolder/S0001/QSM/FreeSurf_QSM_Masks/S0001_QSM_Mag_FSL_rms_OH_Mask.nii.gz

Phase QC:

Note: Phase QC is only available if "MEDI_Yes" is indicated for a participant in MyInputFile.csv and QSM maps are created through Ironsmith using MEDI Toolbox.

Median Absolute deviation (MAD) based outlier regions on phase images are identified as follows: first, using MEDI Toolbox, a relative difference field (RDF) image is created by unwrapping (region growing method) the input phase image and removing the background field (using projections onto dipole fields). Then, the median of an RDF image is calculated, constrained within an aligned, Freesurfer-derived whole-brain (WB) mask, eroded by one voxel. The median is calculated using the FSL function fslstats. Then, using the same FSL function and WB mask, the median is subtracted from every voxel of the RDF image and the absolute value of the outcome is saved into a new intermediate map/image. The median of this intermediate map/image is then calculated to get the MAD of the RDF image. Positive and negative thresholds for outlier voxels are subsequently calculated and correspond to the median of the RDF image +/- (5 * MAD). These outlier thresholds have been determined by testing the outlier detection procedure on 35 participants. Next, a MAD-based outlier mask is created using the AFNI function 3dcalc by identifying all voxels within an RDF image (WB-mask constrained) lower or higher than the positive and negative outlier thresholds calculated in the previous step. Lastly, the percent overlap between a MAD-based outlier mask and each of the 89 anatomical ROIs supported by Ironsmith is calculated and saved in an output file labelled Group_QSM_MAD.csv within the output directory (output directory/Group/ Group_QSM_MAD.csv).

7) Ironsmith uses the following software, provided in the form of a Singularity image:

AFNI

RW Cox. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29:162-173, 1996.

RW Cox and JS Hyde. Software tools for analysis and visualization of FMRI Data. NMR in Biomedicine, 10:171-178, 1997.

S Gold, B Christian, S Arndt, G Zeien, T Cizadlo, DL Johnson, M Flaum, and NC Andreasen. Functional MRI statistical software packages: a comparative analysis. Human Brain Mapping, 6:73-84, 1998.

dcm2niix

Li, Xiangrui, et al. "The first step for neuroimaging data analysis: DICOM to NIFTI conversion." Journal of neuroscience methods 264 (2016): 47-56.

FreeSurfer

Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179-194.

Dale, A.M., Sereno, M.I., 1993. Improved localization of cortical activity by combining EEG and MEG with MRI cortical surface reconstruction: a linear approach. J Cogn Neurosci 5, 162-176.

Desikan, R.S., Segonne, F., Fischl, B., Quinn, B.T., Dickerson, B.C., Blacker, D., Buckner, R.L., Dale, A.M., Maguire, R.P., Hyman, B.T., Albert, M.S., Killiany, R.J., 2006. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968-980.

FSL

M.W. Woolrich, S. Jbabdi, B. Patenaude, M. Chappell, S. Makni, T. Behrens, C. Beckmann, M. Jenkinson, S.M. Smith. Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45:S173-86, 2009

S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-19, 2004

M. Jenkinson, C.F. Beckmann, T.E. Behrens, M.W. Woolrich, S.M. Smith. FSL. NeuroImage, 62:782-90, 2012

Neurodocker

The singularity containers provided with Ironsmith have been created using Neurodocker:
https://github.com/ReproNim/neurodocker

8) ROI List:

L_ = Left hemisphere
R_ = Right hemisphere
LR_ = Bilateral
_GM = Gray matter

LR_Frontal_Lobe_GM
LR_Parietal_Lobe_GM
LR_Occipital_Lobe_GM
LR_Temporal_Lobe_GM
L_CaudalAnteriorCingulate_GM
L_CaudalMiddleFrontal_GM
L_Cuneus_GM
L_DLPFC_GM
L_Entorhinal_GM
L_Frontal_GM
L_Fusiform_GM
L_InferiorParietal_GM
L_AngularGyrus_GM
L_InferiorTemporal_GM
L_Insula_GM
L_IsthmusCingulate_GM
L_LateralOccipital_GM
L_LateralOrbitofrontal_GM
L_Lingual_GM
L_MedialOrbitofrontal_GM
L_MiddleTemporal_GM
L_Occipital_GM_Mask
L_Parietal_GM_Mask
L_Temporal_GM_Mask
L_Parahippocampal_GM
L_Pericalcarine_GM
L_Postcentral_GM
L_PosteriorCingulate_GM
L_Precentral_GM
L_Precuneus_GM
L_RostralMiddleFrontal_GM
L_RostralAnteriorCingulate_GM
L_SuperiorFrontal_GM
L_SuperiorParietal_GM
L_SuperiorTemporal_GM
L_TransverseTemporal_GM
R_CaudalAnteriorCingulate_GM
R_CaudalMiddleFrontal_GM
R_Cuneus_GM
R_DLPFC_GM
R_Entorhinal_GM
R_Frontal_GM_Mask
R_Fusiform_GM
R_InferiorParietal_GM
R_AngularGyrus_GM
R_InferiorTemporal_GM
R_Insula_GM
R_IsthmusCingulate_GM
R_LateralOccipital_GM
R_LateralOrbitofrontal_GM
R_Lingual_GM
R_MedialOrbitofrontal_GM
R_MiddleTemporal_GM
R_Occipital_GM_Mask
R_Parietal_GM_Mask
R_Temporal_GM_Mask
R_Parahippocampal_GM
R_Pericalcarine_GM
R_Postcentral_GM
R_PosteriorCingulate_GM
R_Precentral_GM
R_Precuneus_GM
R_RostralMiddleFrontal_GM
R_RostralAnteriorCingulate_GM
R_SuperiorFrontal_GM
R_SuperiorParietal_GM
R_SuperiorTemporal_GM
R_TransverseTemporal_GM
LR_Accumbens_area
LR_Amygdala
LR_Caudate
LR_Hipp
LR_Pallidum
LR_Putamen
LR_Thalamus_Proper
L_Accumbens_area
L_Amygdala
L_Caudate
L_Hipp
L_Pallidum
L_Putamen
L_Thalamus_Proper
R_Accumbens_area
R_Amygdala
R_Caudate
R_Hipp
R_Pallidum
R_Putamen
R_Thalamus_Proper

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ironsmithqsm's Issues

QSM_container.simg download - file not working

Dear Prof Zachariou,

There seems to be an issue with the QSM_container.simg file I downloaded via the link you provided:
https://drive.google.com/file/d/1NFV2z0yIEPKGblQVrcMe8bza8ZS21AO7/view?usp=sharing

The file is found in the Functions folder, but after re-downloading, I still get the following error-message:
ERROR : Unknown image format/type: /home/668866/IronSmith/IronSmithQSM-1.2/Functions/QSM_Container.simg
ERROR: QSM_Container.simg test FAILED!

I already tried with Ironsmith v1.2 and v1.3

Currently using:

Ubuntu 16.04.7

Thank you for your answer in advance!
Best regards

MEDI new ref fails

Hello,

I am trying to get IronSmith to run on a QSM dataset, but every subject fails at the same point. This is the error message

Creating new CSF/WM reference MEDI analysis file for 21827_11853 --> Subj_21827_11853_MEDI_QSM_New_Ref.m
in: /cbica/projects/pennlinc_qsm/output/Ironsmith/21827_11853/QSM

Running MEDI analysis for 21827_11853...

Error: Input folder invalid: MEDI_Output_New_CSF
Chris Rorden's dcm2niiX version v1.0.20210317 GCC6.3.0 x86-64 (64-bit Linux)

ESC[31m----------------------------------------------
ERROR: MEDI_QSM_New_Ref.sh FAILED! Skipping 21827_11853...
----------------------------------------------ESC[0m

Nothing gets written in the MEDI_New_Ref_Matlab_Log.txt

If I run the Subj_21827_11853_MEDI_QSM_New_Ref.m code in MATLAB, the error seems to occur at this step:
Write_DICOM(Mask_CSF_New_Rot_F3, files, New_CSF_Mask_Dir);

The error it produces is this:

Error using Write_DICOM (line 38)
96 filenames given for 120 slices.

Just to note, the original run of MEDI (e.g. Subj_21827_11853_MEDI_QSM.m) complete successfully.

Any advice is appreciated!

Error copying DICOM file from my folders to the folders created by Ironsmith

Hi,

I have been trying to run Ironsmith, but I am running into an issue when the program tries to copy DICOM files from my input into the output folder it creates. I figured out that Ironsmith is adding a hidden character to the subject id (which is the name of the folder it creates). Is this supposed to happen, or is something else going weird on my end?

This is what the code looks like:
Copying DICOM files from /inputfiles/subjectID/SWI_Images/ to /outputfiles/trial 1/subjectID/QSM/QSM_DICOM

cp: cannot create regular file 'QSM_DICOM/': Not a directory

The above line repeats many times, and then the following:

Converting QSM DICOMS in /DataDisk/ironsmith_data/ironsmith_output/1301L2_permissionstrial/1301L2/QSM/QSM_DICOM/ to NIFTI (.nii.gz)

Error: Unable to find any DICOM images in QSM_DICOM (or subfolders 5 deep)
Chris Rorden's dcm2niiX version v1.0.20210317 GCC6.3.0 x86-64 (64-bit Linux)

Any ideas would be helpful. Thanks!

error when running MEDI_New_Ref.m

I ran into the following issue when trying to run the IronSmith pipeline (i.e., MEDI_QSM_New_Ref.{sh,m}) If you have any ideas on why this might be happening or how to debug the following, it would be greatly appreciated. We're very interested in using IronSmith for our data!

matlab command:

QSM_New_CSF = MEDI_L1('lambda',1000,'lambda_CSF',100,'merit','smv',5);

matlab output:

CSF regularization used
Using gaussnewton
Warning: *ERROR*ERROR*ERROR* 
Array dimensions must match for binary array op.

Error in MEDI_L1>@(x)Mask_CSF.*(x-mean(x(Mask_CSF))) (line 91)
            LT_reg = @(x) Mask_CSF.*(x - mean(x(Mask_CSF)));

Error in MEDI_L1>@(dx)lam_CSF.*LT_reg(LT_reg(real(dx))) (line 113)
                reg_CSF = @(dx) lam_CSF.*LT_reg(LT_reg(real(dx)));

Error in MEDI_L1>@(dx)reg(dx)+reg_CSF(dx) (line 114)
                reg = @(dx) reg(dx) + reg_CSF(dx);

Error in MEDI_L1/gaussnewton (line 119)
            b = reg(x) + 2*lambda*Dconv( real(conj(w).*conj(1i).*(w-b0)) );

Error in MEDI_L1 (line 85)
        [x, cost_reg_history, cost_data_history] = gaussnewton();

I gathered so far that the prior commands are swapping the dimensions using the permute function, and our actual dimensions of 512x512x72 and are now 512x72x512 after the Mask_CSF_New_Rot_* set of commands. I believe this MEDI_L1 command does not like these new dimensions, but am not certain and am not sure what the next step should be.

Thanks in advance!

Error where QSM Dicoms are not found

Hello,

I am having an issue where Ironsmith is only able to find the QSM dicom directory for one of my participants. I have been able to successfully run 1 participant. Now I am trying to run ironsmith for 10 participants. The data for each of the participants is organized the same, and Ironsmith is able to successfully find all participant's MPR files. However, ironsmith is unable to find 9 out of the ten participant's mag/phase files.

The first 9 participants in the csv input file get an error something like this when it comes to finding the QSM dicoms:
ERROR: QSM DICOM directory: /raw_data/iron_data/ironsmith_batc NOT FOUND in specified path! Skipping 9998...

But the last participant's files are successfully found:
QSM DICOM directory: /raw_data/iron_data/ironsmith_batchtest2/9999/9999_magphasecombo FOUND! Checking folder contents

All of the participants are organized the same way. Within the directory "ironsmith_batchtest2", each participant has their own directory, and then within each participant directory are 2 separate directories for the MPR and QSM Dicom files. I'm not sure why only the last participant is processed correctly.

Any suggestions/advice would be helpful. Thanks!

Ironsmith stops at MEDI_QSM_New_Ref.sh step as of a MATLAB error

Hello, I'd like to begin with huge Thanks for all efforts up to date, and for any coming help.

Starting from the Ironsmith version featuring the new way of aligning the freesurfer ventricles mask to the QSM data (Mask_xx_New_Perm_Tran instead of the old Mask_xx_New_Rot..), current script always halts at step: "Creating new CSF/WM reference MEDI analysis file for [...] "

I see that RDF.mat is correctly renamed to RDF.mat.QSM.Script just a moment before the error, thus bringing to the conclusion that the issue is related to MATLAB execution of Subj_$Subj_MEDI_QSM_New_Ref.m .

System used: Ubuntu 20.04 with MATLAB r2018b, Ironsmith 1.4 beta (error occurred also with 1.3beta).

executing Subj_$Subj_MEDI_QSM_New_Ref.m on windowed Matlab for debugging purposes, gave these errors (I'm using reference lines of the corresponding MEDI_QSN_New_Ref.sh )

227 - echo "MatOrder(2)=find(abs(Affine3DR2)>0.98 & abs(Affine3DR2)<=1);" >> Subj_${Subj}MEDI_QSM_New_Ref.m
228 - echo "MatOrder(3)=find(abs(Affine3DR3)>0.98 & abs(Affine3DR3)<=1);" >> Subj${Subj}_MEDI_QSM_New_Ref.m

obviously resulted in these lines on Subj_$Subj_MEDI_QSM_New_Ref.m:

27 - MatOrder(2)=find(abs(Affine3DR2)>0.98 & abs(Affine3DR2)<=1);
28 - MatOrder(3)=find(abs(Affine3DR3)>0.98 & abs(Affine3DR3)<=1);

*** On both commands the output was: ***
Unable to perform assignment because the left and right sides have a different number of elements.

Please note that first assignment MatOrder(1)=find(abs(Affine3DR1)>0.98 & abs(Affine3DR1)<=1); gave no errors.

Again with many thanks, I ask you to let us know if logfiles, scripts or original dicoms are needed for you to help somehow.
Best Regards
Ivano Chiarotti

Will there be a docker version?

Hi,
After reading about your nice paper in NI, I finally got here, with hope. As I already installed docker, you know, like fmriprep etc. I am looking for a docker version of this package if possible. Can you please provide a docker container version or a simple instruction on how to build IronSmithQSM as a docker image or just provide a DockerFile?
Thank you!

MEDI Toolbox requires the Matlab Image Processing Toolbox to be installed.

There is an omission in the Ironsmith installation instructions for v1.0:

MEDI Toolbox requires the Matlab Image Processing Toolbox to be installed. Without the Image Processing Toolbox installed, Ironsmith v1.0 will exit with "MEDI.sh failed message"

The upcoming Ironsmith v1.1 release addresses this issue by checking whether the Image Processing Toolbox is installed for the Matlab version specified in IronsmithQSM/Matlab_Config.txt and prompts users to install it if it is not found.

Documentation for Ironsmith V1.1 will also be updated to inform users of this requirement.

MEDI Toolbox and supports versions

Hello,
I tried to run Ironsmith on Ubuntu 22.04. The version of MATLAB is “2021b” and the version number is “9.11”, but it prompts that the version is too old (read as “9.1”), how can I solve them?
Looking forward to your reply. Thank you very much!

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