Manipulate Package
The manipulate from RStudio allows you to create simple Tcl/Tk operators for interactive visualization. I will use it for a simple slider to view different slices of an image.
library(manipulate)
fslr package
I'm calling the fslr package because I know that if you have it installed, you will likely have FSL and have a 1mm T1 template from MNI in a specific location. fslr also loads the oro.nifti package so that readNIfTI is accessible after loading fslr. You can download a test NIfTI image here if you don't have access to any and don't have FSL downlaoded.
Here I will read in the template image:
library(fslr)
options(fsl.path='/usr/local/fsl')
template = file.path(fsldir(), "data/standard",
"MNI152_T1_1mm_brain.nii.gz")
img = readNIfTI(template)
The iplot function
The iplot function defined below takes in a nifti object, the specific plane to be plotted and additional options to be passed to oro.nifti::image. The function is located on my GitHub here.
iplot = function(img, plane = c("axial",
"coronal", "sagittal"), ...){
## pick the plane
plane = match.arg(plane, c("axial",
"coronal", "sagittal"))
# Get the max number of slices in that plane for the slider
ns= switch(plane,
"axial"=dim(img)[3],
"coronal"=dim(img)[2],
"sagittal"=dim(img)[1])
## run the manipulate command
manipulate({
image(img, z = z, plot.type= "single", plane = plane, ...)
# this will return mouse clicks (future experimental work)
pos <- manipulatorMouseClick()
if (!is.null(pos)) {
print(pos)
}
},
## make the slider
z = slider(1, ns, step=1, initial = ceiling(ns/2))
)
}
Example plots
Here are some examples of how this iplot function would be used:
iplot(img) iplot(img, plane = "coronal") iplot(img, plane = "sagittal")
The result will be a plotted image of the slice with a slider. This is most useful if you run it within RStudio.
Below are 2 example outputs of what you see in RStudio:
Slice 91:
Slice 145:
Conclusions
The iplot function allows users to interactively explore neuroimages. The plotting is not as fast as I'd like, I may try to speed up the oro.nifti::image command or implement some subsampling. It does however show a proof of concept how interactive neuroimaging visualization can be done in R.
Note
manipulate must be run in RStudio for manipulation. The fslr function fslview will call FSLView from FSL for interactive visualization. This is an option of interactive neuroimaging “in R”, but not a real or satisfactory implementation for me (even though I use it frequently). If anyone has implemented such a solution in R, I'd love to hear about it.
John,
Great post! I think we definitely need some tools for fMRI visualization in R, and I like your idea of use of manipulate function!
The current iPlot function though seems to be a little bit too slow. Interestingly if you run manipulate by it self without iplot wrapper it is thee times as fast (my guess there are issues with memory allocation):
plane = “axial”
plane = match.arg(plane, c(“axial”, “coronal”, “sagittal”))
# Get the max number of slices in that plane for the slider
ns = switch(plane,
“axial”=dim(img)[3],
“coronal”=dim(img)[2],
“sagittal”=dim(img)[1])
manipulate({
image(img, z = z, plot.type= “single”)
# this will return mouse clicks (future experimental work)
pos <- manipulatorMouseClick()
if (!is.null(pos)) {
print(pos)
}
},
## make the slider
z = slider(1, ns, step=1, initial = ceiling(ns/2))
)
###############################################################
I was also able to speed it up even more by using "rasterPlot" function
###############################################################
plane = "axial"
plane = match.arg(plane, c("axial", "coronal", "sagittal"))
# Get the max number of slices in that plane for the slider
ns = switch(plane,
"axial"=dim(img)[3],
"coronal"=dim(img)[2],
"sagittal"=dim(img)[1])
manipulate({
# Draw a slice
#==========================================================
simg = img[,,z];
simg = simg/max(simg) #Normalize simg to be between 0 and 1
simg2 = t(simg[,ncol(simg):1]) # Rotate Matrix
w = dim(simg2)[1];
h = dim(simg2)[2];
plot(c(0, h), c(0, w), type = "n", xlab = "", ylab = "", axes = F)
rasterImage(as.raster(simg2), 0, w, h, 0)
#==========================================================
# this will return mouse clicks (future experimental work)
pos <- manipulatorMouseClick()
if (!is.null(pos)) {
print(pos)
}
},
## make the slider
z = slider(1, ns, step=1, initial = ceiling(ns/2))
)
Let me know what you think,
Best,
~Max
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