I would use MRIcroGL for this. Use Fill/Open to open your background channel, and File/AddOverlay to add your additional layers. In the Layers panel, you will see all your layers listed. You can click on the layer name to set properties (e.g. in the screenshot I have selected the `green` image and set its color scheme to `2green`). Note that each layer has a checkbox that toggles if it is visible (or drag the opacity slider for translucent). The Draw/DrawColor allows you to set your drawing color. Note you can toggle layers on or off to see the desired layer.

Hello,

  I think you will find that the MRIcron value has simply not yet updated, move the crosshair a little and you will see that it is merely reporting the previous location.

You could use FSL:

fslstats input.nii.gz -V

I would derive these details using Python, which is very scriptable.  Note that you may want to consider partial volume effects when computing volumes for non-binary images, e.g. if you have a gray matter probability mask, you may only want to consider voxels that are over 50% GM. To be even more accurate, you could modulate volume by the probability, such that a voxel which is 50% GM only contributes half a voxels volume to the sum.

import nibabel as nb
import numpy as np
from nibabel.imagestats import mask_volume
fnm = 'input.nii.gz'
img = nb.load(fnm)
computed_volume = mask_volume(img)
print(f"{computed_volume}mm3")

Do remember that the MNI template is larger than the average brain, explaining the difference between SPM and MNI templates when normalizing data, e.g. Figure 1 here:

  https://www.ncbi.nlm.nih.gov/pmc/article...

While I am glad that MRIcron remains popular, it is a mature tool thatis not actively developed. Can I suggest you try out MRIcroGL - you can set the Axial view as your default in the Preferences - see the attached screenshot.

Hello,

  MRIcron is a visualization tool, not an image manipulation tool. The View/FlipLR is designed to change between neurological and radiological orientation.

You could try MRIcroGL, which has the Import/Tools/Rotation menu item.

Alternately, you could write a Python script. Here is what ChatGPT suggests:

--------------

import nibabel as nib
import numpy as np

def flip_first_dimension(input_path, output_path):
    # Load the NIfTI file
    img = nib.load(input_path)
    data = img.get_fdata()

    # Flip the order of voxels in the first dimension
    flipped_data = np.flip(data, axis=0)

    # Create a new NIfTI image with the flipped data
    flipped_img = nib.Nifti1Image(flipped_data, img.affine, img.header)

    # Save the flipped NIfTI file
    nib.save(flipped_img, output_path)
    print(f"Flipped NIfTI file saved to {output_path}")

if __name__ == "__main__":
    # Replace 'input_file.nii.gz' and 'output_file.nii.gz' with your file paths
    input_file_path = 'input_file.nii.gz'
    output_file_path = 'output_file.nii.gz'

    flip_first_dimension(input_file_path, output_file_path)

Drawings are special objects for MRIcroGL.

 1. The color reflects the voxel intensity (e.g. 1=red, 2=blue, 3 = green, etc). 

 2. The drawings are not shown in the renderings. The renderings require gradient information for volumes, which is computationally expensive and therefore incompatible with interactively modifying drawings. Once the dynamic drawing stage is completed, you can always load it as a static overlay (File/AddOverlay) to view it on the rendering.

Our nascent NiiVue does allow you to customize drawing colors, and does show them in the rendering (albeit the rendering view is blank while the drawing pen is being dragged).

You can see this by changing the palette in the draw menu here:

 https://niivue.github.io/niivue/features/draw.ui.html

and you can make your own custom palette by adjusting the script at the bottom of this web page:

  https://niivue.github.io/niivue/features...

Thank you, Chris, for yoursuper quick response!

While I can use both of my ROIs as overlays in MRICroGL, my challenge lies in adjusting the transparency of one of them while keeping the other opaque in the render view. Specifically, I'm using these overlays to visualize connectivity results, where one ROI serves as the seed and the other as the target (activation area).

I aim to make the seed ROI appear slightly transparent (like in "image1" in light red and on top the opaqe green) while keeping the target ROI fully opaque. However, when I load them as overlays, I find it challenging to adjust the transparency of only one of them without affecting the transparency of the other or the underlying template ("image2".

I tried also "draw->advance->create overlap image" option to combine the rendered brain with the seed ROI thinking then to overlay the other ROI (target) on top of them. Unfortunately, this approach distorts the template template, and the colors are not preserved as desired ("image3").

I would appreciate any further advice or alternative methods you might suggest for achieving this visualization goal, either within MRICro or using any other tools you have in mind.

Thank you very much for your assistance!

Tammar

HI，joyce have you solved your problem？I have a samilar error about data type. If you don't mind,could we talk about it together.

For Siemens V* the CSA header is tag 0029,1020. For Siemens XA the CSA header is tag 0021,1019.

Thanks very much Chris !

You can use the "Output Directory" button in the graphical interface to select an output folder.

Alternatively, you can run the core dcm2niix conversion tool from the command line and specify the output directory:

 dcm2niix -o /output/path /path/to/DICOMs

It is often useful to specify your output filename:

  https://github.com/rordenlab/dcm2niix/bl...

so to specify the series number (%s) and protocol name (%p) you could call

 dcm2niix -f %s_%p -o /output/path /path/to/DICOMs

We describe the JHU 189 atlas here

  https://www.ncbi.nlm.nih.gov/pmc/article...

by citing this paper

  https://pubmed.ncbi.nlm.nih.gov/22498656...

The ch2bet is based on a single individual and is described here:

  https://www.mcgill.ca/bic/software/tools...

While I am happy that my classic MRIcro and MRIcron tools have proved mature and popular, please understand that my development efforts focus on modern graphics hardware  resulting in MRIcroGL and NiiVue. Personally, I prefer the mni152 and spm152 templates provided with MRIcroGL. These are based on the 2009 atlases with MNI size (larger than average) and SPM size (average) to match the most popular normalization atlases:

https://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009

For details on these two sizes, see:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6050588/

You will want to save your contrast image in NIfTI format, and then you can load the image just like a statistical map. For example here

http://brainmap.org/software.html#Sleuth

Once you create the NIfTI image "MyMap.nii" you could visualize it, for example the MRIcroGL script:

import gl
gl.resetdefaults()
gl.loadimage('spm152')
gl.overlayload('myMap')

• You can find more ImageJ/MRIcron color lookup tables here.
• Here is a simple LUT maker for Windows.
• See here for more Matlab code. 
• Here are tools that help make color maps with sensible luminance gradients.
• I would suggest Viridis and Plasma which help those with the most common form of color blindness.