Combining volumes - what am I missing?

Operating system: Mac - High Sierra
Slicer version: 4.8.1
Expected behavior: One perfect, high resolution volume
Actual behavior: Three volumes, each one being high resolution in only one plane (sagittal, coronal, transverse)

I have been using 3D Slicer for many months and I’m slowly learning the ropes. I am using it to produce anatomical boney models, and have probably created between 10-15 models.

After loading CT scans from their DICOM folder I always get several volumes, often with the precursor ‘ax’, ‘sag’, ‘cor’. Individually these volumes have great resolution in the plane they are named after, but poor voxel resolution in the other two planes. Obviously I can load different volumes into each plane to view them all together, but when I start a label map I seem to be committed at that point to one volume, and so the label map resolution is affected in two of the planes.

Is it possible to recreate one high resolution volume from the three ‘ax’, ‘sag’, ‘cor’ volumes or is this something the Radiology department would have to do for me when they create the DICOM folder?

Help would be much appreciated.

Kind regards

Matt Carter

In general, these multiple low-resolution acquisitions are not well suited for any 3D processing, so the best would be to acquire proper 3D volume, with as isotropic spacing (cubic voxel shape) as possible.

You may try to find toolkits that can create an isotropic super-resolution image from multiple low-resolution sweeps, but this is a difficult image reconstruction problem, so there are no standard solutions. Have a look at this post Import volume by projections for info on a toolkit that might be usable.

You may also try to use your volumes for segmentation one by one (since you can swap the master volume any time): Create a high-resolution isotropic volume from any of the input volumes, using Crop volume module. Then create a segmentation node using this isotropic volume as master volume. After this you can switch between master volumes - the segmentation node’s internal binary labelmap representation will remain high-resolution and isotropic. For example, you can segment the structure of interest based on one image, then switch master volume and make adjustments in the segmentation as needed. Unfortunately, this is a manual, iterative process.