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241 rows Below is a list of all files for MRIcron. Before downloading, you may want to read the Release Notes and ChangeLog (accessible by clicking on the release name). Package: mricron 31 Subscribers. Download mricron mac for free. Education downloads - mricron by NITRC and many more programs are available for instant and free download.
Update: Please refer to the comments below to read about updated versions of the software (https://www.nitrc.org/frs/?group_id=889). Neuroimaging software is frequently advancing so check with man pages or other documentation for options and changes.
Note: While I will try to be verbose in my how-to guides of imaging, I assume any readers have at least basic knowledge of the command line (Terminal) and bash. If you feel lost with some of the bash references and need some additional info, here is a good beginner’s guide to bash.
Usually the first step to process structural MRI data is to convert the DICOM files from the MR scanner into a more useable format. The current “best” format for files is NIfTI. There are other formats but this is a file format that is widely used and very useable. There are problems with it (specifically with how some software deal handle it but that is a topic for a later time).
One of the best tools to convert DICOM files to NIfTI is Chris Rorden’s dcm2nii (see also his profile on the NITRC website for contact info). This is a command line tool (there is also a graphical user interface version) that wil convert just about any DICOM image to an appropriate NIfTI. dcm2nii is part of the MRIcron package – the most recent version of the software can be downloaded from this link from the NITRC website. There are Linux, Macintosh, and Windows versions of the software (as well as source code if you care to build your own version). This software is available to download for free.
After the software is downloaded and extracted, it can be run from wherever its location is. In other words, this is not “installed” software, it is run from whatever location it is saved to (for example, it could be saved in /Applications/mricronmac/ or in /Users/bbunny/neuroimaging/).
See the screenshot for an example of dcm2nii location.
In order to run dcm2nii from Terminal, move into the directory where dcm2nii is (e.g., using the command line type: cd /Applications/mricronmac/ – again, this should be where the actual dcm2nii program is on your computer).
Next, in Terminal type: ./dcm2nii and press return (enter).
This will bring up some text like this:
It gives an example of how to run the program. Also notice my default preferences. I edited the dcm2nii.ini file to have those defaults because I’ve found that they work the best for the work I do. You can also run dcm2nii from anywhere in Terminal by typing: /[path to mricronmac]/dcm2nii where [path to mricronmac is replaced with the full path to where dcm2nii is – e.g., /Applications/mricronmac/ (check out this post to see how to be able to run a program without typing in the whole path to it). I’ve found it easiest to run dcm2nii from within the folder of DICOM files I want to convert to NIfTI images. To do this first you need to move to the directory where your raw DICOMs are (I typically make a temporary copy of the files so I am not doing anything with the originals, just to prevent any potential issues): for example, /Users/Shared/DWI/blindnum001/DATA/64dir_DWI. Now from within this directory you can run dcm2nii and convert the images with the following command (or whatever is the equivalent on your computer):
/Applications/mricronmac/dcm2nii –a y –d n –e n –f y –g y –i n –r n –x n /Users/Shared/DWI/blindnum001/DATA/64dir_DWI/image001.dcm
Note: you do not need to include all those options (–a y –d n –e n –f y –g y –i n –r n –x n), I included them just to show what are the defaults that I run with dcm2nii. If you have edited the dcm2nii.ini file or if you want to use other defaults or options, go ahead – those are just the options I prefer. After the conversion completes you should have one or more NIfTI files in the same directory where your dicoms are. For diffusion weighted data you should have three files: the .nii.gz (if you selected the gzip option), a .bval, and a .bvec. The only times I’ve not had dcm2nii create bval or bvec files (and I have verified that the dicom files are not corrupted) is when I did not change the default options for dcm2nii, which resulted in overly long NIfTI file names; that, for some reason, prevented the bval and bvec files from being created. That is why I make sure output filename is only the input filename. I’ve never had any conversion issues since setting that option.
There is your conversion. You are all done with this step of neuroimaging! Testdisk mac install.
Alternatively, you can use the graphical user interface for dcm2nii:
First you want to make sure your preferences look something like this (if these are the preferences you want; I prefer just to keep the input filename because it tends to be descriptive but fairly short; I typically rename my files later):
In the GUI under File select DICOM to NifTi and locate the dicoms you wish to convert in the dialogue box that appears. After you select the files and push Open the conversion will run. When it is finished verify that you have a .nii.gz file and .bvec and .bval files. The bval file is a simple text file that includes all the b-values of the diffusion scan (e.g., for a 64 direction scan with one b0 image there will be 65 numbers in there – one 0 and 64 1000s or whatever your b weighting was for your scan. 1000 is pretty typical). I prefer using the command line because it can speed up the process but either way will result in NIfTI images.
Now we are ready to move on to another step in image processing.
The cerebellum is a functionally highly diverse structure: Different regions have their unique pattern of connectivity with the neocortex, and therefore likely a specialized functional role. To promote accurate anatomical reference for human functional and anatomical imaging studies, we present here a probabilistic atlas of the cerebellar lobules in the space defined by the MNI152 template. The anatomical definitions are based on the fMRI atlas of an individual cerebellum by Schmahmann et al. (2000). To obtain a representative anatomical atlas, we annotated the lobules on T1-weighted MRI scans (1mm isotropic resolution) of 20 individual healthy young participants (10 male, 10 female, average age 23.7 yrs). Using a different set of 23 participants, we also identified the location of the deep cerebellar nuclei. The individual cerebella were then aligned using different commonly used normalization algorithms.
The resultant probabilistic maps allow for the valid assignment of functional activations to specific cerebellar lobules and the nuclei, while providing a quantitative measure of the certainty of such assignments. Furthermore, maximum probability maps derived from these atlases can be used to define regions of interest (ROIs, more on this here). The atlas is included in the newer releases of FSL and the Anatomy toolbox. More versions of the atlases for use with MRIcron or AFNI are also available here.
Please cite the atlas as:
- Diedrichsen J., Balster J.H., Flavell J., Cussans E., Ramnani N. (2009). A probabilistic MR atlas of the human cerebellum. Neuroimage.
- Diedrichsen J., Maderwald S., K�per M., Th�rling M., Rabe K., Gizewski ER, Ladd M, Timmann D (2011). Imaging the deep cerebellar nuclei:A probabilistic atlas and normalization procedure. Neuroimage.
The generation of the atlas was supported by a Grant by the National Science foundation (NSF) - BSC 0726685.
Why a probabilistic anatomical atlas?
Individual brains are often normalized to MNI-space, and activation foci from group analysis assigned to cerebellar lobules using the a standard atlas (Schmahmann et al., 2000). Our analysis shows that such assignments can be incorrect (often by as much as a full lobule) for up to 2/3 of cerebellar voxels for some normalisation methods. There are two reasons for this:
These problems stress the importance of a representative atlas and of good correspondence between the normalization method used for the atlas and for the group analysis. For this reason we provide the atlas generated with a number of different normalization methods.
The probabilistic atlas also provides a measure of the certainty with with anatomical assignments can be made. The image shows the proportion of the 20 individuals that overlapped with the same lobule in atlas space. We generated the atlas for:
The overlap in SUIT (A) often reaches 100% whereas the overlap using affine methods (FLIRT, B) is somewhat poorer.
The left figure shows the percent overlap between different lobules after normalization.Newer non-linear methods (Segmentation in SPM, FNIRT) lead to a good correspondence and to relatively high accuracies. Cerebellum-only normalization with SUIT leads to the best overlap.
Thus, for new imaging studies of the cerebellum we would strong recommend one of the newer non-linear methods, or ideally the use of SUIT. For the interpretation of older results (for example for meta-analyses of cerebellar imaging results), we also make the atlas for the SPM nonlinear normalization also available.
In collaboration with Prof. Timmann from the University of Duisburg-Essen we also imaged the deep cerebellar nuclei in a set of 23 separate subjects at 7T, using susceptibility-weighted imaging. The dentate, emboliform, globose and fastigial nuclei play an important part of the cerebellar circuit, as the relay all output of the cerebellum. In the latest versions of the probabilistic atlas we provide probabilistic ROIs for the dentate, the interposed (emboliform and globose) and fastigial nuclei. Note that given the small size of the cerebellar nuclei, the overlap is relatively poor even after SUIT normalization. For studies of these structures, we therefore recommend spatial normalization using a dentate ROI.
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The Atlas for FSL View
The atlas can be used with the Atlas Widget in FSLView, part of the FSL package. The atlas for FSLView is available for
The FLIRT and FNIRT versions are already included in newer distributions of FSL. The lobular assignment is approximately spatially unbiased in these atlases, however the maximal probabilities after SUIT alignment is in general higher.
The Atlas for MRICroN
The atlas can be used MRIcron, which is an image viewer written by Chris Rorden and which is freely available for Linux, Windows, and Mac OS X.The atlas for MRICroN is available for:
The necessary files (Maximum-probabiliy-map, size of maximum probability, full probability maps, color map (lut) and text files are all included. To use MRICroN to look up probabilities
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The Atlas for Anatomy Toolbox in SPM
The atlas is now included in the Anatomy toolbox, which was developed S. Eickhoff and colleagues. For the generation of the maps, we used the Segementation and Normalisation algorithm in SPM5/8. To make the maps fit with the rest of the toolbox, they were then warped into the space defined by the Colin-brain.
The Atlas for AFNI
Thanks to Daniel Glen, the atlas is now also available for the AFNI whereAmI atlas GUI. Note that any atlas that has a reachable space is queried, so atlases that are in Talairach or MNI_ANAT space are shown there also. The whereAmI output is continuously updated as the user moves the cross-hair in the viewer, so the information is quite interactive. The command line version of whereAmI shows similar information. The command 'whereami -show_atlases' gives this relevant output.
To use, please download SUIT for AFNI to a new directory and untar with
If you find the atlases and templates useful, please cite using the references includedin
Registration and Download
Note that the SUIT-MRICro version of the probabilistic atlas comes with the new release (v. 2.4) of the SUIT toolbox.
The SUIT toolbox distributed under the Creative Commons Attribution-NonCommercial 3.0 Unported License, meaning that it can be freely used for non-commercial purposes, as long as proper attribution in form of acknowledgments and links (for online use) or citations (in publications) are given. The relevant references are:
Probabilistic atlas for cerebellar lobules
- Diedrichsen, J., Balsters, J. H., Flavell, J., Cussans, E., & Ramnani, N. (2009).A probabilistic atlas of the human cerebellum. Neuroimage.46(1), 39-46.
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Probabilistic atlas and normalisation for deep cerebellar nuclei
- Diedrichsen, J., Maderwald, S., Kuper, M., Thurling, M., Rabe, K., Gizewski, E. R., et al. (2011).Imaging the deep cerebellar nuclei: A probabilistic atlas and normalization procedure.Neuroimage. 54(3), 1786-1794.